Weighted fusion of depth and inertial data to improve view invariance for real-time human action recognition

NASA Astrophysics Data System (ADS)

Chen, Chen; Hao, Huiyan; Jafari, Roozbeh; Kehtarnavaz, Nasser

2017-05-01

This paper presents an extension to our previously developed fusion framework [10] involving a depth camera and an inertial sensor in order to improve its view invariance aspect for real-time human action recognition applications. A computationally efficient view estimation based on skeleton joints is considered in order to select the most relevant depth training data when recognizing test samples. Two collaborative representation classifiers, one for depth features and one for inertial features, are appropriately weighted to generate a decision making probability. The experimental results applied to a multi-view human action dataset show that this weighted extension improves the recognition performance by about 5% over equally weighted fusion deployed in our previous fusion framework.

Self-organizing neural integration of pose-motion features for human action recognition

PubMed Central

Parisi, German I.; Weber, Cornelius; Wermter, Stefan

2015-01-01

The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented toward human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR) networks that obtain progressively generalized representations of sensory inputs and learn inherent spatio-temporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best results for a public benchmark of domestic daily actions. PMID:26106323

Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks

NASA Astrophysics Data System (ADS)

Liu, Jun; Wang, Gang; Duan, Ling-Yu; Abdiyeva, Kamila; Kot, Alex C.

2018-04-01

Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, Long Short-Term Memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action recognition. This network is capable of selectively focusing on the informative joints in each frame of each skeleton sequence by using a global context memory cell. To further improve the attention capability of our network, we also introduce a recurrent attention mechanism, with which the attention performance of the network can be enhanced progressively. Moreover, we propose a stepwise training scheme in order to train our network effectively. Our approach achieves state-of-the-art performance on five challenging benchmark datasets for skeleton based action recognition.

Action Recognition Using Nonnegative Action Component Representation and Sparse Basis Selection.

PubMed

Wang, Haoran; Yuan, Chunfeng; Hu, Weiming; Ling, Haibin; Yang, Wankou; Sun, Changyin

2014-02-01

In this paper, we propose using high-level action units to represent human actions in videos and, based on such units, a novel sparse model is developed for human action recognition. There are three interconnected components in our approach. First, we propose a new context-aware spatial-temporal descriptor, named locally weighted word context, to improve the discriminability of the traditionally used local spatial-temporal descriptors. Second, from the statistics of the context-aware descriptors, we learn action units using the graph regularized nonnegative matrix factorization, which leads to a part-based representation and encodes the geometrical information. These units effectively bridge the semantic gap in action recognition. Third, we propose a sparse model based on a joint l2,1-norm to preserve the representative items and suppress noise in the action units. Intuitively, when learning the dictionary for action representation, the sparse model captures the fact that actions from the same class share similar units. The proposed approach is evaluated on several publicly available data sets. The experimental results and analysis clearly demonstrate the effectiveness of the proposed approach.

Weighted score-level feature fusion based on Dempster-Shafer evidence theory for action recognition

NASA Astrophysics Data System (ADS)

Zhang, Guoliang; Jia, Songmin; Li, Xiuzhi; Zhang, Xiangyin

2018-01-01

The majority of human action recognition methods use multifeature fusion strategy to improve the classification performance, where the contribution of different features for specific action has not been paid enough attention. We present an extendible and universal weighted score-level feature fusion method using the Dempster-Shafer (DS) evidence theory based on the pipeline of bag-of-visual-words. First, the partially distinctive samples in the training set are selected to construct the validation set. Then, local spatiotemporal features and pose features are extracted from these samples to obtain evidence information. The DS evidence theory and the proposed rule of survival of the fittest are employed to achieve evidence combination and calculate optimal weight vectors of every feature type belonging to each action class. Finally, the recognition results are deduced via the weighted summation strategy. The performance of the established recognition framework is evaluated on Penn Action dataset and a subset of the joint-annotated human metabolome database (sub-JHMDB). The experiment results demonstrate that the proposed feature fusion method can adequately exploit the complementarity among multiple features and improve upon most of the state-of-the-art algorithms on Penn Action and sub-JHMDB datasets.

Exploring perceptual processing of ASL and human actions: Effects of inversion and repetition priming

PubMed Central

Corina, David P.; Grosvald, Michael

2011-01-01

In this paper, we compare responses of deaf signers and hearing non-signers engaged in a categorization task of signs and non-linguistic human actions. We examine the time it takes to make such categorizations under conditions of 180-degree stimulus inversion and as a function of repetition priming, in an effort to understand whether the processing of sign language forms draws upon special processing mechanisms or makes use of mechanisms used in recognition of non-linguistic human actions. Our data show that deaf signers were much faster in the categorization of both linguistic and non-linguistic actions, and relative to hearing non-signers, show evidence that they were more sensitive to the configural properties of signs. Our study suggests that sign expertise may lead to modifications of a general-purpose human action recognition system rather than evoking a qualitatively different mode of processing, and supports the contention that signed languages make use of perceptual systems through which humans understand or parse human actions and gestures more generally. PMID:22153323

Joint object and action recognition via fusion of partially observable surveillance imagery data

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir; Chan, Alex L.

2017-05-01

Partially observable group activities (POGA) occurring in confined spaces are epitomized by their limited observability of the objects and actions involved. In many POGA scenarios, different objects are being used by human operators for the conduct of various operations. In this paper, we describe the ontology of such as POGA in the context of In-Vehicle Group Activity (IVGA) recognition. Initially, we describe the virtue of ontology modeling in the context of IVGA and show how such an ontology and a priori knowledge about the classes of in-vehicle activities can be fused for inference of human actions that consequentially leads to understanding of human activity inside the confined space of a vehicle. In this paper, we treat the problem of "action-object" as a duality problem. We postulate a correlation between observed human actions and the object that is being utilized within those actions, and conversely, if an object being handled is recognized, we may be able to expect a number of actions that are likely to be performed on that object. In this study, we use partially observable human postural sequences to recognition actions. Inspired by convolutional neural networks (CNNs) learning capability, we present an architecture design using a new CNN model to learn "action-object" perception from surveillance videos. In this study, we apply a sequential Deep Hidden Markov Model (DHMM) as a post-processor to CNN to decode realized observations into recognized actions and activities. To generate the needed imagery data set for the training and testing of these new methods, we use the IRIS virtual simulation software to generate high-fidelity and dynamic animated scenarios that depict in-vehicle group activities under different operational contexts. The results of our comparative investigation are discussed and presented in detail.

A method of depth image based human action recognition

NASA Astrophysics Data System (ADS)

Li, Pei; Cheng, Wanli

2017-05-01

In this paper, we propose an action recognition algorithm framework based on human skeleton joint information. In order to extract the feature of human motion, we use the information of body posture, speed and acceleration of movement to construct spatial motion feature that can describe and reflect the joint. On the other hand, we use the classical temporal pyramid matching algorithm to construct temporal feature and describe the motion sequence variation from different time scales. Then, we use bag of words to represent these actions, which is to present every action in the histogram by clustering these extracted feature. Finally, we employ Hidden Markov Model to train and test the extracted motion features. In the experimental part, the correctness and effectiveness of the proposed model are comprehensively verified on two well-known datasets.

MATHEMATICS OF SENSING, EXPLOITATION, AND EXECUTION (MSEE) Sensing, Exploitation, and Execution (SEE) on a Foundation for Representation, Inference, and Learning

DTIC Science & Technology

2016-07-01

reconstruction, video synchronization, multi - view tracking, action recognition, reasoning with uncertainty 16. SECURITY CLASSIFICATION OF: 17...3.4.2. Human action recognition across multi - views ......................................................................................... 44 3.4.3...68 4.2.1. Multi - view Multi -object Tracking with 3D cues

Action recognition in depth video from RGB perspective: A knowledge transfer manner

NASA Astrophysics Data System (ADS)

Chen, Jun; Xiao, Yang; Cao, Zhiguo; Fang, Zhiwen

2018-03-01

Different video modal for human action recognition has becoming a highly promising trend in the video analysis. In this paper, we propose a method for human action recognition from RGB video to Depth video using domain adaptation, where we use learned feature from RGB videos to do action recognition for depth videos. More specifically, we make three steps for solving this problem in this paper. First, different from image, video is more complex as it has both spatial and temporal information, in order to better encode this information, dynamic image method is used to represent each RGB or Depth video to one image, based on this, most methods for extracting feature in image can be used in video. Secondly, as video can be represented as image, so standard CNN model can be used for training and testing for videos, beside, CNN model can be also used for feature extracting as its powerful feature expressing ability. Thirdly, as RGB videos and Depth videos are belong to two different domains, in order to make two different feature domains has more similarity, domain adaptation is firstly used for solving this problem between RGB and Depth video, based on this, the learned feature from RGB video model can be directly used for Depth video classification. We evaluate the proposed method on one complex RGB-D action dataset (NTU RGB-D), and our method can have more than 2% accuracy improvement using domain adaptation from RGB to Depth action recognition.

A Low-Dimensional Radial Silhouette-Based Feature for Fast Human Action Recognition Fusing Multiple Views.

PubMed

Chaaraoui, Alexandros Andre; Flórez-Revuelta, Francisco

2014-01-01

This paper presents a novel silhouette-based feature for vision-based human action recognition, which relies on the contour of the silhouette and a radial scheme. Its low-dimensionality and ease of extraction result in an outstanding proficiency for real-time scenarios. This feature is used in a learning algorithm that by means of model fusion of multiple camera streams builds a bag of key poses, which serves as a dictionary of known poses and allows converting the training sequences into sequences of key poses. These are used in order to perform action recognition by means of a sequence matching algorithm. Experimentation on three different datasets returns high and stable recognition rates. To the best of our knowledge, this paper presents the highest results so far on the MuHAVi-MAS dataset. Real-time suitability is given, since the method easily performs above video frequency. Therefore, the related requirements that applications as ambient-assisted living services impose are successfully fulfilled.

Action Spotting and Recognition Based on a Spatiotemporal Orientation Analysis.

PubMed

Derpanis, Konstantinos G; Sizintsev, Mikhail; Cannons, Kevin J; Wildes, Richard P

2013-03-01

This paper provides a unified framework for the interrelated topics of action spotting, the spatiotemporal detection and localization of human actions in video, and action recognition, the classification of a given video into one of several predefined categories. A novel compact local descriptor of video dynamics in the context of action spotting and recognition is introduced based on visual spacetime oriented energy measurements. This descriptor is efficiently computed directly from raw image intensity data and thereby forgoes the problems typically associated with flow-based features. Importantly, the descriptor allows for the comparison of the underlying dynamics of two spacetime video segments irrespective of spatial appearance, such as differences induced by clothing, and with robustness to clutter. An associated similarity measure is introduced that admits efficient exhaustive search for an action template, derived from a single exemplar video, across candidate video sequences. The general approach presented for action spotting and recognition is amenable to efficient implementation, which is deemed critical for many important applications. For action spotting, details of a real-time GPU-based instantiation of the proposed approach are provided. Empirical evaluation of both action spotting and action recognition on challenging datasets suggests the efficacy of the proposed approach, with state-of-the-art performance documented on standard datasets.

Neural Correlates of Human Action Observation in Hearing and Deaf Subjects

PubMed Central

Corina, David; Chiu, Yi-Shiuan; Knapp, Heather; Greenwald, Ralf; Jose-Robertson, Lucia San; Braun, Allen

2007-01-01

Accumulating evidence has suggested the existence of a human action recognition system involving inferior frontal, parietal, and superior temporal regions that may participate in both the perception and execution of actions. However, little is known about the specificity of this system in response to different forms of human action. Here we present data from PET neuroimaging studies from passive viewing of three distinct action types, intransitive self-oriented actions (e.g., stretching, rubbing one’s eyes, etc.), transitive object-oriented actions (e.g., opening a door, lifting a cup to the lips to drink), and the abstract, symbolic actions–signs used in American Sign Language. Our results show that these different classes of human actions engage a frontal/parietal/STS human action recognition system in a highly similar fashion. However, the results indicate that this neural consistency across motion classes is true primarily for hearing subjects. Data from deaf signers shows a non-uniform response to different classes of human actions. As expected, deaf signers engaged left-hemisphere perisylvian language areas during the perception of signed language signs. Surprisingly, these subjects did not engage the expected frontal/parietal/STS circuitry during passive viewing of non-linguistic actions, but rather reliably activated middle-occipital temporal-ventral regions which are known to participate in the detection of human bodies, faces, and movements. Comparisons with data from hearing subjects establish statistically significant contributions of middle-occipital temporal-ventral during the processing of non-linguistic actions in deaf signers. These results suggest that during human motion processing, deaf individuals may engage specialized neural systems that allow for rapid, online differentiation of meaningful linguistic actions from non-linguistic human movements. PMID:17459349

Hybrid generative-discriminative human action recognition by combining spatiotemporal words with supervised topic models

NASA Astrophysics Data System (ADS)

Sun, Hao; Wang, Cheng; Wang, Boliang

2011-02-01

We present a hybrid generative-discriminative learning method for human action recognition from video sequences. Our model combines a bag-of-words component with supervised latent topic models. A video sequence is represented as a collection of spatiotemporal words by extracting space-time interest points and describing these points using both shape and motion cues. The supervised latent Dirichlet allocation (sLDA) topic model, which employs discriminative learning using labeled data under a generative framework, is introduced to discover the latent topic structure that is most relevant to action categorization. The proposed algorithm retains most of the desirable properties of generative learning while increasing the classification performance though a discriminative setting. It has also been extended to exploit both labeled data and unlabeled data to learn human actions under a unified framework. We test our algorithm on three challenging data sets: the KTH human motion data set, the Weizmann human action data set, and a ballet data set. Our results are either comparable to or significantly better than previously published results on these data sets and reflect the promise of hybrid generative-discriminative learning approaches.

Human Action Recognition Using Wireless Wearable In-Ear Microphone

NASA Astrophysics Data System (ADS)

Nishimura, Jun; Kuroda, Tadahiro

To realize the ubiquitous eating habits monitoring, we proposed the use of sounds sensed by an in-ear placed wireless wearable microphone. A prototype of wireless wearable in-ear microphone was developed by utilizing a common Bluetooth headset. We proposed a robust chewing action recognition algorithm which consists of two recognition stages: “chew-like” signal detection and chewing sound verification stages. We also provide empirical results on other action recognition using in-ear sound including swallowing, cough, belch, and etc. The average chewing number counting error rate of 1.93% is achieved. Lastly, chewing sound mapping is proposed as a new prototypical approach to provide an additional intuitive feedback on food groups to be able to infer the eating habits in their daily life context.

Towards discrete wavelet transform-based human activity recognition

NASA Astrophysics Data System (ADS)

Khare, Manish; Jeon, Moongu

2017-06-01

Providing accurate recognition of human activities is a challenging problem for visual surveillance applications. In this paper, we present a simple and efficient algorithm for human activity recognition based on a wavelet transform. We adopt discrete wavelet transform (DWT) coefficients as a feature of human objects to obtain advantages of its multiresolution approach. The proposed method is tested on multiple levels of DWT. Experiments are carried out on different standard action datasets including KTH and i3D Post. The proposed method is compared with other state-of-the-art methods in terms of different quantitative performance measures. The proposed method is found to have better recognition accuracy in comparison to the state-of-the-art methods.

Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach.

PubMed

Liu, Li; Shao, Ling; Li, Xuelong; Lu, Ke

2016-01-01

Extracting discriminative and robust features from video sequences is the first and most critical step in human action recognition. In this paper, instead of using handcrafted features, we automatically learn spatio-temporal motion features for action recognition. This is achieved via an evolutionary method, i.e., genetic programming (GP), which evolves the motion feature descriptor on a population of primitive 3D operators (e.g., 3D-Gabor and wavelet). In this way, the scale and shift invariant features can be effectively extracted from both color and optical flow sequences. We intend to learn data adaptive descriptors for different datasets with multiple layers, which makes fully use of the knowledge to mimic the physical structure of the human visual cortex for action recognition and simultaneously reduce the GP searching space to effectively accelerate the convergence of optimal solutions. In our evolutionary architecture, the average cross-validation classification error, which is calculated by an support-vector-machine classifier on the training set, is adopted as the evaluation criterion for the GP fitness function. After the entire evolution procedure finishes, the best-so-far solution selected by GP is regarded as the (near-)optimal action descriptor obtained. The GP-evolving feature extraction method is evaluated on four popular action datasets, namely KTH, HMDB51, UCF YouTube, and Hollywood2. Experimental results show that our method significantly outperforms other types of features, either hand-designed or machine-learned.

Comparative study of methods for recognition of an unknown person's action from a video sequence

NASA Astrophysics Data System (ADS)

Hori, Takayuki; Ohya, Jun; Kurumisawa, Jun

2009-02-01

This paper proposes a Tensor Decomposition Based method that can recognize an unknown person's action from a video sequence, where the unknown person is not included in the database (tensor) used for the recognition. The tensor consists of persons, actions and time-series image features. For the observed unknown person's action, one of the actions stored in the tensor is assumed. Using the motion signature obtained from the assumption, the unknown person's actions are synthesized. The actions of one of the persons in the tensor are replaced by the synthesized actions. Then, the core tensor for the replaced tensor is computed. This process is repeated for the actions and persons. For each iteration, the difference between the replaced and original core tensors is computed. The assumption that gives the minimal difference is the action recognition result. For the time-series image features to be stored in the tensor and to be extracted from the observed video sequence, the human body silhouette's contour shape based feature is used. To show the validity of our proposed method, our proposed method is experimentally compared with Nearest Neighbor rule and Principal Component analysis based method. Experiments using 33 persons' seven kinds of action show that our proposed method achieves better recognition accuracies for the seven actions than the other methods.

A Human Mirror Neuron System for Language: Perspectives from Signed Languages of the Deaf

ERIC Educational Resources Information Center

Knapp, Heather Patterson; Corina, David P.

2010-01-01

Language is proposed to have developed atop the human analog of the macaque mirror neuron system for action perception and production [Arbib M.A. 2005. From monkey-like action recognition to human language: An evolutionary framework for neurolinguistics (with commentaries and author's response). "Behavioral and Brain Sciences, 28", 105-167; Arbib…

An improved silhouette for human pose estimation

NASA Astrophysics Data System (ADS)

Hawes, Anthony H.; Iftekharuddin, Khan M.

2017-08-01

We propose a novel method for analyzing images that exploits the natural lines of a human poses to find areas where self-occlusion could be present. Errors caused by self-occlusion cause several modern human pose estimation methods to mis-identify body parts, which reduces the performance of most action recognition algorithms. Our method is motivated by the observation that, in several cases, occlusion can be reasoned using only boundary lines of limbs. An intelligent edge detection algorithm based on the above principle could be used to augment the silhouette with information useful for pose estimation algorithms and push forward progress on occlusion handling for human action recognition. The algorithm described is applicable to computer vision scenarios involving 2D images and (appropriated flattened) 3D images.

Action recognition via cumulative histogram of multiple features

NASA Astrophysics Data System (ADS)

Yan, Xunshi; Luo, Yupin

2011-01-01

Spatial-temporal interest points (STIPs) are popular in human action recognition. However, they suffer from difficulties in determining size of codebook and losing much information during forming histograms. In this paper, spatial-temporal interest regions (STIRs) are proposed, which are based on STIPs and are capable of marking the locations of the most ``shining'' human body parts. In order to represent human actions, the proposed approach takes great advantages of multiple features, including STIRs, pyramid histogram of oriented gradients and pyramid histogram of oriented optical flows. To achieve this, cumulative histogram is used to integrate dynamic information in sequences and to form feature vectors. Furthermore, the widely used nearest neighbor and AdaBoost methods are employed as classification algorithms. Experiments on public datasets KTH, Weizmann and UCF sports show that the proposed approach achieves effective and robust results.

MSEE: Stochastic Cognitive Linguistic Behavior Models for Semantic Sensing

DTIC Science & Technology

2013-09-01

recognition, a Gaussian Process Dynamic Model with Social Network Analysis (GPDM-SNA) for a small human group action recognition, an extended GPDM-SNA...44  3.2. Small Human Group Activity Modeling Based on Gaussian Process Dynamic Model and Social Network Analysis (SN-GPDM...51  Approved for public release; distribution unlimited. 3 3.2.3. Gaussian Process Dynamical Model and

Novel dynamic Bayesian networks for facial action element recognition and understanding

NASA Astrophysics Data System (ADS)

Zhao, Wei; Park, Jeong-Seon; Choi, Dong-You; Lee, Sang-Woong

2011-12-01

In daily life, language is an important tool of communication between people. Besides language, facial action can also provide a great amount of information. Therefore, facial action recognition has become a popular research topic in the field of human-computer interaction (HCI). However, facial action recognition is quite a challenging task due to its complexity. In a literal sense, there are thousands of facial muscular movements, many of which have very subtle differences. Moreover, muscular movements always occur simultaneously when the pose is changed. To address this problem, we first build a fully automatic facial points detection system based on a local Gabor filter bank and principal component analysis. Then, novel dynamic Bayesian networks are proposed to perform facial action recognition using the junction tree algorithm over a limited number of feature points. In order to evaluate the proposed method, we have used the Korean face database for model training. For testing, we used the CUbiC FacePix, facial expressions and emotion database, Japanese female facial expression database, and our own database. Our experimental results clearly demonstrate the feasibility of the proposed approach.

Towards automated assistance for operating home medical devices.

PubMed

Gao, Zan; Detyniecki, Marcin; Chen, Ming-Yu; Wu, Wen; Hauptmann, Alexander G; Wactlar, Howard D

2010-01-01

To detect errors when subjects operate a home medical device, we observe them with multiple cameras. We then perform action recognition with a robust approach to recognize action information based on explicitly encoding motion information. This algorithm detects interest points and encodes not only their local appearance but also explicitly models local motion. Our goal is to recognize individual human actions in the operations of a home medical device to see if the patient has correctly performed the required actions in the prescribed sequence. Using a specific infusion pump as a test case, requiring 22 operation steps from 6 action classes, our best classifier selects high likelihood action estimates from 4 available cameras, to obtain an average class recognition rate of 69%.

Can Humans Fly Action Understanding with Multiple Classes of Actors

DTIC Science & Technology

2015-06-08

recognition using structure from motion point clouds. In European Conference on Computer Vision, 2008. [5] R. Caruana. Multitask learning. Machine Learning...tonomous driving ? the kitti vision benchmark suite. In IEEE Conference on Computer Vision and Pattern Recognition, 2012. [12] L. Gorelick, M. Blank

A Neural Basis of Facial Action Recognition in Humans

PubMed Central

Srinivasan, Ramprakash; Golomb, Julie D.

2016-01-01

By combining different facial muscle actions, called action units, humans can produce an extraordinarily large number of facial expressions. Computational models and studies in cognitive science and social psychology have long hypothesized that the brain needs to visually interpret these action units to understand other people's actions and intentions. Surprisingly, no studies have identified the neural basis of the visual recognition of these action units. Here, using functional magnetic resonance imaging and an innovative machine learning analysis approach, we identify a consistent and differential coding of action units in the brain. Crucially, in a brain region thought to be responsible for the processing of changeable aspects of the face, multivoxel pattern analysis could decode the presence of specific action units in an image. This coding was found to be consistent across people, facilitating the estimation of the perceived action units on participants not used to train the multivoxel decoder. Furthermore, this coding of action units was identified when participants attended to the emotion category of the facial expression, suggesting an interaction between the visual analysis of action units and emotion categorization as predicted by the computational models mentioned above. These results provide the first evidence for a representation of action units in the brain and suggest a mechanism for the analysis of large numbers of facial actions and a loss of this capacity in psychopathologies. SIGNIFICANCE STATEMENT Computational models and studies in cognitive and social psychology propound that visual recognition of facial expressions requires an intermediate step to identify visible facial changes caused by the movement of specific facial muscles. Because facial expressions are indeed created by moving one's facial muscles, it is logical to assume that our visual system solves this inverse problem. Here, using an innovative machine learning method and neuroimaging data, we identify for the first time a brain region responsible for the recognition of actions associated with specific facial muscles. Furthermore, this representation is preserved across subjects. Our machine learning analysis does not require mapping the data to a standard brain and may serve as an alternative to hyperalignment. PMID:27098688

Proposal of Self-Learning and Recognition System of Facial Expression

NASA Astrophysics Data System (ADS)

Ogawa, Yukihiro; Kato, Kunihito; Yamamoto, Kazuhiko

We describe realization of more complicated function by using the information acquired from some equipped unripe functions. The self-learning and recognition system of the human facial expression, which achieved under the natural relation between human and robot, are proposed. The robot with this system can understand human facial expressions and behave according to their facial expressions after the completion of learning process. The system modelled after the process that a baby learns his/her parents’ facial expressions. Equipping the robot with a camera the system can get face images and equipping the CdS sensors on the robot’s head the robot can get the information of human action. Using the information of these sensors, the robot can get feature of each facial expression. After self-learning is completed, when a person changed his facial expression in front of the robot, the robot operates actions under the relevant facial expression.

Action Categorization in Rhesus Monkeys: discrimination of grasping from non-grasping manual motor acts.

PubMed

Nelissen, Koen; Vanduffel, Wim

2017-11-08

The ability to recognize others' actions is an important aspect of social behavior. While neurophysiological and behavioral research in monkeys has offered a better understanding of how the primate brain processes this type of information, further insight with respect to the neural correlates of action recognition requires tasks that allow recording of brain activity or perturbing brain regions while monkeys simultaneously make behavioral judgements about certain aspects of observed actions. Here we investigated whether rhesus monkeys could actively discriminate videos showing grasping or non-grasping manual motor acts in a two-alternative categorization task. After monkeys became proficient in this task, we tested their ability to generalize to a number of untrained, novel videos depicting grasps or other manual motor acts. Monkeys generalized to a wide range of novel human or conspecific grasping and non-grasping motor acts. They failed, however, for videos showing unfamiliar actions such as a non-biological effector performing a grasp, or a human hand touching an object with the back of the hand. This study shows the feasibility of training monkeys to perform active judgements about certain aspects of observed actions, instrumental for causal investigations into the neural correlates of action recognition.

Human Activity Recognition in AAL Environments Using Random Projections.

PubMed

Damaševičius, Robertas; Vasiljevas, Mindaugas; Šalkevičius, Justas; Woźniak, Marcin

2016-01-01

Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject's body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented.

Human Activity Recognition in AAL Environments Using Random Projections

PubMed Central

Damaševičius, Robertas; Vasiljevas, Mindaugas; Šalkevičius, Justas; Woźniak, Marcin

2016-01-01

Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject's body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented. PMID:27413392

Neural Correlates of Intentional Communication

PubMed Central

Noordzij, Matthijs L.; Newman-Norlund, Sarah E.; de Ruiter, Jan Peter; Hagoort, Peter; Levinson, Stephen C.; Toni, Ivan

2010-01-01

We know a great deal about the neurophysiological mechanisms supporting instrumental actions, i.e., actions designed to alter the physical state of the environment. In contrast, little is known about our ability to select communicative actions, i.e., actions directly designed to modify the mental state of another agent. We have recently provided novel empirical evidence for a mechanism in which a communicator selects his actions on the basis of a prediction of the communicative intentions that an addressee is most likely to attribute to those actions. The main novelty of those findings was that this prediction of intention recognition is cerebrally implemented within the intention recognition system of the communicator, is modulated by the ambiguity in meaning of the communicative acts, and not by their sensorimotor complexity. The characteristics of this predictive mechanism support the notion that human communicative abilities are distinct from both sensorimotor and linguistic processes. PMID:21151781

Computational Model of Primary Visual Cortex Combining Visual Attention for Action Recognition

PubMed Central

Shu, Na; Gao, Zhiyong; Chen, Xiangan; Liu, Haihua

2015-01-01

Humans can easily understand other people’s actions through visual systems, while computers cannot. Therefore, a new bio-inspired computational model is proposed in this paper aiming for automatic action recognition. The model focuses on dynamic properties of neurons and neural networks in the primary visual cortex (V1), and simulates the procedure of information processing in V1, which consists of visual perception, visual attention and representation of human action. In our model, a family of the three-dimensional spatial-temporal correlative Gabor filters is used to model the dynamic properties of the classical receptive field of V1 simple cell tuned to different speeds and orientations in time for detection of spatiotemporal information from video sequences. Based on the inhibitory effect of stimuli outside the classical receptive field caused by lateral connections of spiking neuron networks in V1, we propose surround suppressive operator to further process spatiotemporal information. Visual attention model based on perceptual grouping is integrated into our model to filter and group different regions. Moreover, in order to represent the human action, we consider the characteristic of the neural code: mean motion map based on analysis of spike trains generated by spiking neurons. The experimental evaluation on some publicly available action datasets and comparison with the state-of-the-art approaches demonstrate the superior performance of the proposed model. PMID:26132270

A neuroanatomical predictor of mirror self-recognition in chimpanzees.

PubMed

Hecht, E E; Mahovetz, L M; Preuss, T M; Hopkins, W D

2017-01-01

The ability to recognize one's own reflection is shared by humans and only a few other species, including chimpanzees. However, this ability is highly variable across individual chimpanzees. In humans, self-recognition involves a distributed, right-lateralized network including frontal and parietal regions involved in the production and perception of action. The superior longitudinal fasciculus (SLF) is a system of white matter tracts linking these frontal and parietal regions. The current study measured mirror self-recognition (MSR) and SLF anatomy in 60 chimpanzees using diffusion tensor imaging. Successful self-recognition was associated with greater rightward asymmetry in the white matter of SLFII and SLFIII, and in SLFIII's gray matter terminations in Broca's area. We observed a visible progression of SLFIII's prefrontal extension in apes that show negative, ambiguous, and compelling evidence of MSR. Notably, SLFIII's terminations in Broca's area are not right-lateralized or particularly pronounced at the population level in chimpanzees, as they are in humans. Thus, chimpanzees with more human-like behavior show more human-like SLFIII connectivity. These results suggest that self-recognition may have co-emerged with adaptations to frontoparietal circuitry. © The Author (2016). Published by Oxford University Press.

A neuroanatomical predictor of mirror self-recognition in chimpanzees

PubMed Central

Mahovetz, L. M.; Preuss, T. M.; Hopkins, W. D.

2017-01-01

Abstract The ability to recognize one’s own reflection is shared by humans and only a few other species, including chimpanzees. However, this ability is highly variable across individual chimpanzees. In humans, self-recognition involves a distributed, right-lateralized network including frontal and parietal regions involved in the production and perception of action. The superior longitudinal fasciculus (SLF) is a system of white matter tracts linking these frontal and parietal regions. The current study measured mirror self-recognition (MSR) and SLF anatomy in 60 chimpanzees using diffusion tensor imaging. Successful self-recognition was associated with greater rightward asymmetry in the white matter of SLFII and SLFIII, and in SLFIII’s gray matter terminations in Broca’s area. We observed a visible progression of SLFIII’s prefrontal extension in apes that show negative, ambiguous, and compelling evidence of MSR. Notably, SLFIII’s terminations in Broca’s area are not right-lateralized or particularly pronounced at the population level in chimpanzees, as they are in humans. Thus, chimpanzees with more human-like behavior show more human-like SLFIII connectivity. These results suggest that self-recognition may have co-emerged with adaptations to frontoparietal circuitry. PMID:27803287

Neural theory for the perception of causal actions.

PubMed

Fleischer, Falk; Christensen, Andrea; Caggiano, Vittorio; Thier, Peter; Giese, Martin A

2012-07-01

The efficient prediction of the behavior of others requires the recognition of their actions and an understanding of their action goals. In humans, this process is fast and extremely robust, as demonstrated by classical experiments showing that human observers reliably judge causal relationships and attribute interactive social behavior to strongly simplified stimuli consisting of simple moving geometrical shapes. While psychophysical experiments have identified critical visual features that determine the perception of causality and agency from such stimuli, the underlying detailed neural mechanisms remain largely unclear, and it is an open question why humans developed this advanced visual capability at all. We created pairs of naturalistic and abstract stimuli of hand actions that were exactly matched in terms of their motion parameters. We show that varying critical stimulus parameters for both stimulus types leads to very similar modulations of the perception of causality. However, the additional form information about the hand shape and its relationship with the object supports more fine-grained distinctions for the naturalistic stimuli. Moreover, we show that a physiologically plausible model for the recognition of goal-directed hand actions reproduces the observed dependencies of causality perception on critical stimulus parameters. These results support the hypothesis that selectivity for abstract action stimuli might emerge from the same neural mechanisms that underlie the visual processing of natural goal-directed action stimuli. Furthermore, the model proposes specific detailed neural circuits underlying this visual function, which can be evaluated in future experiments.

Speech-Associated Gestures, Broca's Area, and the Human Mirror System

ERIC Educational Resources Information Center

Skipper, Jeremy I.; Goldin-Meadow, Susan; Nusbaum, Howard C.; Small, Steven L.

2007-01-01

Speech-associated gestures are hand and arm movements that not only convey semantic information to listeners but are themselves actions. Broca's area has been assumed to play an important role both in semantic retrieval or selection (as part of a language comprehension system) and in action recognition (as part of a "mirror" or…

Computational validation of the motor contribution to speech perception.

PubMed

Badino, Leonardo; D'Ausilio, Alessandro; Fadiga, Luciano; Metta, Giorgio

2014-07-01

Action perception and recognition are core abilities fundamental for human social interaction. A parieto-frontal network (the mirror neuron system) matches visually presented biological motion information onto observers' motor representations. This process of matching the actions of others onto our own sensorimotor repertoire is thought to be important for action recognition, providing a non-mediated "motor perception" based on a bidirectional flow of information along the mirror parieto-frontal circuits. State-of-the-art machine learning strategies for hand action identification have shown better performances when sensorimotor data, as opposed to visual information only, are available during learning. As speech is a particular type of action (with acoustic targets), it is expected to activate a mirror neuron mechanism. Indeed, in speech perception, motor centers have been shown to be causally involved in the discrimination of speech sounds. In this paper, we review recent neurophysiological and machine learning-based studies showing (a) the specific contribution of the motor system to speech perception and (b) that automatic phone recognition is significantly improved when motor data are used during training of classifiers (as opposed to learning from purely auditory data). Copyright © 2014 Cognitive Science Society, Inc.

Human action recognition based on point context tensor shape descriptor

NASA Astrophysics Data System (ADS)

Li, Jianjun; Mao, Xia; Chen, Lijiang; Wang, Lan

2017-07-01

Motion trajectory recognition is one of the most important means to determine the identity of a moving object. A compact and discriminative feature representation method can improve the trajectory recognition accuracy. This paper presents an efficient framework for action recognition using a three-dimensional skeleton kinematic joint model. First, we put forward a rotation-scale-translation-invariant shape descriptor based on point context (PC) and the normal vector of hypersurface to jointly characterize local motion and shape information. Meanwhile, an algorithm for extracting the key trajectory based on the confidence coefficient is proposed to reduce the randomness and computational complexity. Second, to decrease the eigenvalue decomposition time complexity, a tensor shape descriptor (TSD) based on PC that can globally capture the spatial layout and temporal order to preserve the spatial information of each frame is proposed. Then, a multilinear projection process is achieved by tensor dynamic time warping to map the TSD to a low-dimensional tensor subspace of the same size. Experimental results show that the proposed shape descriptor is effective and feasible, and the proposed approach obtains considerable performance improvement over the state-of-the-art approaches with respect to accuracy on a public action dataset.

Action perception in individuals with congenital blindness or deafness: how does the loss of a sensory modality from birth affect perception-induced motor facilitation?

PubMed

Alaerts, Kaat; Swinnen, Stephan P; Wenderoth, Nicole

2011-05-01

Seeing or hearing manual actions activates the mirror neuron system, that is, specialized neurons within motor areas which fire when an action is performed but also when it is passively perceived. Using TMS, it was shown that motor cortex of typically developed subjects becomes facilitated not only from seeing others' actions, but also from merely hearing action-related sounds. In the present study, TMS was used for the first time to explore the "auditory" and "visual" responsiveness of motor cortex in individuals with congenital blindness or deafness. TMS was applied over left primary motor cortex (M1) to measure cortico-motor facilitation while subjects passively perceived manual actions (either visually or aurally). Although largely unexpected, congenitally blind or deaf subjects displayed substantially lower resonant motor facilitation upon action perception compared to seeing/hearing control subjects. Moreover, muscle-specific changes in cortico-motor excitability within M1 appeared to be absent in individuals with profound blindness or deafness. Overall, these findings strongly argue against the hypothesis that an increased reliance on the remaining sensory modality in blind or deaf subjects is accompanied by an increased responsiveness of the "auditory" or "visual" perceptual-motor "mirror" system, respectively. Moreover, the apparent lack of resonant motor facilitation for the blind and deaf subjects may challenge the hypothesis of a unitary mirror system underlying human action recognition and may suggest that action perception in blind and deaf subjects engages a mode of action processing that is different from the human action recognition system recruited in typically developed subjects.

A Human Activity Recognition System Using Skeleton Data from RGBD Sensors.

PubMed

Cippitelli, Enea; Gasparrini, Samuele; Gambi, Ennio; Spinsante, Susanna

2016-01-01

The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.

Multi-modal low cost mobile indoor surveillance system on the Robust Artificial Intelligence-based Defense Electro Robot (RAIDER)

NASA Astrophysics Data System (ADS)

Nair, Binu M.; Diskin, Yakov; Asari, Vijayan K.

2012-10-01

We present an autonomous system capable of performing security check routines. The surveillance machine, the Clearpath Husky robotic platform, is equipped with three IP cameras with different orientations for the surveillance tasks of face recognition, human activity recognition, autonomous navigation and 3D reconstruction of its environment. Combining the computer vision algorithms onto a robotic machine has given birth to the Robust Artificial Intelligencebased Defense Electro-Robot (RAIDER). The end purpose of the RAIDER is to conduct a patrolling routine on a single floor of a building several times a day. As the RAIDER travels down the corridors off-line algorithms use two of the RAIDER's side mounted cameras to perform a 3D reconstruction from monocular vision technique that updates a 3D model to the most current state of the indoor environment. Using frames from the front mounted camera, positioned at the human eye level, the system performs face recognition with real time training of unknown subjects. Human activity recognition algorithm will also be implemented in which each detected person is assigned to a set of action classes picked to classify ordinary and harmful student activities in a hallway setting.The system is designed to detect changes and irregularities within an environment as well as familiarize with regular faces and actions to distinguish potentially dangerous behavior. In this paper, we present the various algorithms and their modifications which when implemented on the RAIDER serves the purpose of indoor surveillance.

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning.

PubMed

Chung, Michael Jae-Yoon; Friesen, Abram L; Fox, Dieter; Meltzoff, Andrew N; Rao, Rajesh P N

2015-01-01

A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i) learn probabilistic models of actions through self-discovery and experience, (ii) utilize these learned models for inferring the goals of human actions, and (iii) perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i) a simulated robot that learns human-like gaze following behavior, and (ii) a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration.

A Bayesian Developmental Approach to Robotic Goal-Based Imitation Learning

PubMed Central

Chung, Michael Jae-Yoon; Friesen, Abram L.; Fox, Dieter; Meltzoff, Andrew N.; Rao, Rajesh P. N.

2015-01-01

A fundamental challenge in robotics today is building robots that can learn new skills by observing humans and imitating human actions. We propose a new Bayesian approach to robotic learning by imitation inspired by the developmental hypothesis that children use self-experience to bootstrap the process of intention recognition and goal-based imitation. Our approach allows an autonomous agent to: (i) learn probabilistic models of actions through self-discovery and experience, (ii) utilize these learned models for inferring the goals of human actions, and (iii) perform goal-based imitation for robotic learning and human-robot collaboration. Such an approach allows a robot to leverage its increasing repertoire of learned behaviors to interpret increasingly complex human actions and use the inferred goals for imitation, even when the robot has very different actuators from humans. We demonstrate our approach using two different scenarios: (i) a simulated robot that learns human-like gaze following behavior, and (ii) a robot that learns to imitate human actions in a tabletop organization task. In both cases, the agent learns a probabilistic model of its own actions, and uses this model for goal inference and goal-based imitation. We also show that the robotic agent can use its probabilistic model to seek human assistance when it recognizes that its inferred actions are too uncertain, risky, or impossible to perform, thereby opening the door to human-robot collaboration. PMID:26536366

Discovering motion primitives for unsupervised grouping and one-shot learning of human actions, gestures, and expressions.

PubMed

Yang, Yang; Saleemi, Imran; Shah, Mubarak

2013-07-01

This paper proposes a novel representation of articulated human actions and gestures and facial expressions. The main goals of the proposed approach are: 1) to enable recognition using very few examples, i.e., one or k-shot learning, and 2) meaningful organization of unlabeled datasets by unsupervised clustering. Our proposed representation is obtained by automatically discovering high-level subactions or motion primitives, by hierarchical clustering of observed optical flow in four-dimensional, spatial, and motion flow space. The completely unsupervised proposed method, in contrast to state-of-the-art representations like bag of video words, provides a meaningful representation conducive to visual interpretation and textual labeling. Each primitive action depicts an atomic subaction, like directional motion of limb or torso, and is represented by a mixture of four-dimensional Gaussian distributions. For one--shot and k-shot learning, the sequence of primitive labels discovered in a test video are labeled using KL divergence, and can then be represented as a string and matched against similar strings of training videos. The same sequence can also be collapsed into a histogram of primitives or be used to learn a Hidden Markov model to represent classes. We have performed extensive experiments on recognition by one and k-shot learning as well as unsupervised action clustering on six human actions and gesture datasets, a composite dataset, and a database of facial expressions. These experiments confirm the validity and discriminative nature of the proposed representation.

What's she doing in the kitchen? Context helps when actions are hard to recognize.

PubMed

Wurm, Moritz F; Schubotz, Ricarda I

2017-04-01

Specific spatial environments are often indicative of where certain actions may take place: In kitchens we prepare food, and in bathrooms we engage in personal hygiene, but not vice versa. In action recognition, contextual cues may constrain an observer's expectations toward actions that are more strongly associated with a particular context than others. Such cues should become particularly helpful when the action itself is difficult to recognize. However, to date only easily identifiable actions were investigated, and the effects of context on recognition were rather interfering than facilitatory. To test whether context also facilitates action recognition, we measured recognition performance of hardly identifiable actions that took place in compatible, incompatible, and neutral contextual settings. Action information was degraded by pixelizing the area of the object manipulation while the room in which the action took place remained fully visible. We found significantly higher accuracy for actions that took place in compatible compared to incompatible and neutral settings, indicating facilitation. Additionally, action recognition was slower in incompatible settings than in compatible and neutral settings, indicating interference. Together, our findings demonstrate that contextual information is effectively exploited during action observation, in particular when visual information about the action itself is sparse. Differential effects on speed and accuracy suggest that contexts modulate action recognition at different levels of processing. Our findings emphasize the importance of contextual information in comprehensive, ecologically valid models of action recognition.

Multi-dimension feature fusion for action recognition

NASA Astrophysics Data System (ADS)

Dong, Pei; Li, Jie; Dong, Junyu; Qi, Lin

2018-04-01

Typical human actions last several seconds and exhibit characteristic spatio-temporal structure. The challenge for action recognition is to capture and fuse the multi-dimension information in video data. In order to take into account these characteristics simultaneously, we present a novel method that fuses multiple dimensional features, such as chromatic images, depth and optical flow fields. We built our model based on the multi-stream deep convolutional networks with the help of temporal segment networks and extract discriminative spatial and temporal features by fusing ConvNets towers multi-dimension, in which different feature weights are assigned in order to take full advantage of this multi-dimension information. Our architecture is trained and evaluated on the currently largest and most challenging benchmark NTU RGB-D dataset. The experiments demonstrate that the performance of our method outperforms the state-of-the-art methods.

Transfer learning for visual categorization: a survey.

PubMed

Shao, Ling; Zhu, Fan; Li, Xuelong

2015-05-01

Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In recent years, with transfer learning being applied to visual categorization, some typical problems, e.g., view divergence in action recognition tasks and concept drifting in image classification tasks, can be efficiently solved. In this paper, we survey state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition.

Human behavior recognition using a context-free grammar

NASA Astrophysics Data System (ADS)

Rosani, Andrea; Conci, Nicola; De Natale, Francesco G. B.

2014-05-01

Automatic recognition of human activities and behaviors is still a challenging problem for many reasons, including limited accuracy of the data acquired by sensing devices, high variability of human behaviors, and gap between visual appearance and scene semantics. Symbolic approaches can significantly simplify the analysis and turn raw data into chains of meaningful patterns. This allows getting rid of most of the clutter produced by low-level processing operations, embedding significant contextual information into the data, as well as using simple syntactic approaches to perform the matching between incoming sequences and models. We propose a symbolic approach to learn and detect complex activities through the sequences of atomic actions. Compared to previous methods based on context-free grammars, we introduce several important novelties, such as the capability to learn actions based on both positive and negative samples, the possibility of efficiently retraining the system in the presence of misclassified or unrecognized events, and the use of a parsing procedure that allows correct detection of the activities also when they are concatenated and/or nested one with each other. An experimental validation on three datasets with different characteristics demonstrates the robustness of the approach in classifying complex human behaviors.

Posture-based processing in visual short-term memory for actions.

PubMed

Vicary, Staci A; Stevens, Catherine J

2014-01-01

Visual perception of human action involves both form and motion processing, which may rely on partially dissociable neural networks. If form and motion are dissociable during visual perception, then they may also be dissociable during their retention in visual short-term memory (VSTM). To elicit form-plus-motion and form-only processing of dance-like actions, individual action frames can be presented in the correct or incorrect order. The former appears coherent and should elicit action perception, engaging both form and motion pathways, whereas the latter appears incoherent and should elicit posture perception, engaging form pathways alone. It was hypothesized that, if form and motion are dissociable in VSTM, then recognition of static body posture should be better after viewing incoherent than after viewing coherent actions. However, as VSTM is capacity limited, posture-based encoding of actions may be ineffective with increased number of items or frames. Using a behavioural change detection task, recognition of a single test posture was significantly more likely after studying incoherent than after studying coherent stimuli. However, this effect only occurred for spans of two (but not three) items and for stimuli with five (but not nine) frames. As in perception, posture and motion are dissociable in VSTM.

A Review on Human Activity Recognition Using Vision-Based Method.

PubMed

Zhang, Shugang; Wei, Zhiqiang; Nie, Jie; Huang, Lei; Wang, Shuang; Li, Zhen

2017-01-01

Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research.

A Review on Human Activity Recognition Using Vision-Based Method

PubMed Central

Nie, Jie

2017-01-01

Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research. PMID:29065585

Action Recognition Using 3D Histograms of Texture and A Multi-Class Boosting Classifier.

PubMed

Zhang, Baochang; Yang, Yun; Chen, Chen; Yang, Linlin; Han, Jungong; Shao, Ling

2017-10-01

Human action recognition is an important yet challenging task. This paper presents a low-cost descriptor called 3D histograms of texture (3DHoTs) to extract discriminant features from a sequence of depth maps. 3DHoTs are derived from projecting depth frames onto three orthogonal Cartesian planes, i.e., the frontal, side, and top planes, and thus compactly characterize the salient information of a specific action, on which texture features are calculated to represent the action. Besides this fast feature descriptor, a new multi-class boosting classifier (MBC) is also proposed to efficiently exploit different kinds of features in a unified framework for action classification. Compared with the existing boosting frameworks, we add a new multi-class constraint into the objective function, which helps to maintain a better margin distribution by maximizing the mean of margin, whereas still minimizing the variance of margin. Experiments on the MSRAction3D, MSRGesture3D, MSRActivity3D, and UTD-MHAD data sets demonstrate that the proposed system combining 3DHoTs and MBC is superior to the state of the art.

Human and animal sounds influence recognition of body language.

PubMed

Van den Stock, Jan; Grèzes, Julie; de Gelder, Beatrice

2008-11-25

In naturalistic settings emotional events have multiple correlates and are simultaneously perceived by several sensory systems. Recent studies have shown that recognition of facial expressions is biased towards the emotion expressed by a simultaneously presented emotional expression in the voice even if attention is directed to the face only. So far, no study examined whether this phenomenon also applies to whole body expressions, although there is no obvious reason why this crossmodal influence would be specific for faces. Here we investigated whether perception of emotions expressed in whole body movements is influenced by affective information provided by human and by animal vocalizations. Participants were instructed to attend to the action displayed by the body and to categorize the expressed emotion. The results indicate that recognition of body language is biased towards the emotion expressed by the simultaneously presented auditory information, whether it consist of human or of animal sounds. Our results show that a crossmodal influence from auditory to visual emotional information obtains for whole body video images with the facial expression blanked and includes human as well as animal sounds.

Recognizing human actions by learning and matching shape-motion prototype trees.

PubMed

Jiang, Zhuolin; Lin, Zhe; Davis, Larry S

2012-03-01

A shape-motion prototype-based approach is introduced for action recognition. The approach represents an action as a sequence of prototypes for efficient and flexible action matching in long video sequences. During training, an action prototype tree is learned in a joint shape and motion space via hierarchical K-means clustering and each training sequence is represented as a labeled prototype sequence; then a look-up table of prototype-to-prototype distances is generated. During testing, based on a joint probability model of the actor location and action prototype, the actor is tracked while a frame-to-prototype correspondence is established by maximizing the joint probability, which is efficiently performed by searching the learned prototype tree; then actions are recognized using dynamic prototype sequence matching. Distance measures used for sequence matching are rapidly obtained by look-up table indexing, which is an order of magnitude faster than brute-force computation of frame-to-frame distances. Our approach enables robust action matching in challenging situations (such as moving cameras, dynamic backgrounds) and allows automatic alignment of action sequences. Experimental results demonstrate that our approach achieves recognition rates of 92.86 percent on a large gesture data set (with dynamic backgrounds), 100 percent on the Weizmann action data set, 95.77 percent on the KTH action data set, 88 percent on the UCF sports data set, and 87.27 percent on the CMU action data set.

Robots Learn to Recognize Individuals from Imitative Encounters with People and Avatars

NASA Astrophysics Data System (ADS)

Boucenna, Sofiane; Cohen, David; Meltzoff, Andrew N.; Gaussier, Philippe; Chetouani, Mohamed

2016-02-01

Prior to language, human infants are prolific imitators. Developmental science grounds infant imitation in the neural coding of actions, and highlights the use of imitation for learning from and about people. Here, we used computational modeling and a robot implementation to explore the functional value of action imitation. We report 3 experiments using a mutual imitation task between robots, adults, typically developing children, and children with Autism Spectrum Disorder. We show that a particular learning architecture - specifically one combining artificial neural nets for (i) extraction of visual features, (ii) the robot’s motor internal state, (iii) posture recognition, and (iv) novelty detection - is able to learn from an interactive experience involving mutual imitation. This mutual imitation experience allowed the robot to recognize the interactive agent in a subsequent encounter. These experiments using robots as tools for modeling human cognitive development, based on developmental theory, confirm the promise of developmental robotics. Additionally, findings illustrate how person recognition may emerge through imitative experience, intercorporeal mapping, and statistical learning.

Robots Learn to Recognize Individuals from Imitative Encounters with People and Avatars

PubMed Central

Boucenna, Sofiane; Cohen, David; Meltzoff, Andrew N.; Gaussier, Philippe; Chetouani, Mohamed

2016-01-01

Prior to language, human infants are prolific imitators. Developmental science grounds infant imitation in the neural coding of actions, and highlights the use of imitation for learning from and about people. Here, we used computational modeling and a robot implementation to explore the functional value of action imitation. We report 3 experiments using a mutual imitation task between robots, adults, typically developing children, and children with Autism Spectrum Disorder. We show that a particular learning architecture - specifically one combining artificial neural nets for (i) extraction of visual features, (ii) the robot’s motor internal state, (iii) posture recognition, and (iv) novelty detection - is able to learn from an interactive experience involving mutual imitation. This mutual imitation experience allowed the robot to recognize the interactive agent in a subsequent encounter. These experiments using robots as tools for modeling human cognitive development, based on developmental theory, confirm the promise of developmental robotics. Additionally, findings illustrate how person recognition may emerge through imitative experience, intercorporeal mapping, and statistical learning. PMID:26844862

Learning Human Actions by Combining Global Dynamics and Local Appearance.

PubMed

Luo, Guan; Yang, Shuang; Tian, Guodong; Yuan, Chunfeng; Hu, Weiming; Maybank, Stephen J

2014-12-01

In this paper, we address the problem of human action recognition through combining global temporal dynamics and local visual spatio-temporal appearance features. For this purpose, in the global temporal dimension, we propose to model the motion dynamics with robust linear dynamical systems (LDSs) and use the model parameters as motion descriptors. Since LDSs live in a non-Euclidean space and the descriptors are in non-vector form, we propose a shift invariant subspace angles based distance to measure the similarity between LDSs. In the local visual dimension, we construct curved spatio-temporal cuboids along the trajectories of densely sampled feature points and describe them using histograms of oriented gradients (HOG). The distance between motion sequences is computed with the Chi-Squared histogram distance in the bag-of-words framework. Finally we perform classification using the maximum margin distance learning method by combining the global dynamic distances and the local visual distances. We evaluate our approach for action recognition on five short clips data sets, namely Weizmann, KTH, UCF sports, Hollywood2 and UCF50, as well as three long continuous data sets, namely VIRAT, ADL and CRIM13. We show competitive results as compared with current state-of-the-art methods.

A human mirror neuron system for language: Perspectives from signed languages of the deaf.

PubMed

Knapp, Heather Patterson; Corina, David P

2010-01-01

Language is proposed to have developed atop the human analog of the macaque mirror neuron system for action perception and production [Arbib M.A. 2005. From monkey-like action recognition to human language: An evolutionary framework for neurolinguistics (with commentaries and author's response). Behavioral and Brain Sciences, 28, 105-167; Arbib M.A. (2008). From grasp to language: Embodied concepts and the challenge of abstraction. Journal de Physiologie Paris 102, 4-20]. Signed languages of the deaf are fully-expressive, natural human languages that are perceived visually and produced manually. We suggest that if a unitary mirror neuron system mediates the observation and production of both language and non-linguistic action, three prediction can be made: (1) damage to the human mirror neuron system should non-selectively disrupt both sign language and non-linguistic action processing; (2) within the domain of sign language, a given mirror neuron locus should mediate both perception and production; and (3) the action-based tuning curves of individual mirror neurons should support the highly circumscribed set of motions that form the "vocabulary of action" for signed languages. In this review we evaluate data from the sign language and mirror neuron literatures and find that these predictions are only partially upheld. 2009 Elsevier Inc. All rights reserved.

Appearance-based human gesture recognition using multimodal features for human computer interaction

NASA Astrophysics Data System (ADS)

Luo, Dan; Gao, Hua; Ekenel, Hazim Kemal; Ohya, Jun

2011-03-01

The use of gesture as a natural interface plays an utmost important role for achieving intelligent Human Computer Interaction (HCI). Human gestures include different components of visual actions such as motion of hands, facial expression, and torso, to convey meaning. So far, in the field of gesture recognition, most previous works have focused on the manual component of gestures. In this paper, we present an appearance-based multimodal gesture recognition framework, which combines the different groups of features such as facial expression features and hand motion features which are extracted from image frames captured by a single web camera. We refer 12 classes of human gestures with facial expression including neutral, negative and positive meanings from American Sign Languages (ASL). We combine the features in two levels by employing two fusion strategies. At the feature level, an early feature combination can be performed by concatenating and weighting different feature groups, and LDA is used to choose the most discriminative elements by projecting the feature on a discriminative expression space. The second strategy is applied on decision level. Weighted decisions from single modalities are fused in a later stage. A condensation-based algorithm is adopted for classification. We collected a data set with three to seven recording sessions and conducted experiments with the combination techniques. Experimental results showed that facial analysis improve hand gesture recognition, decision level fusion performs better than feature level fusion.

Multi-modal gesture recognition using integrated model of motion, audio and video

NASA Astrophysics Data System (ADS)

Goutsu, Yusuke; Kobayashi, Takaki; Obara, Junya; Kusajima, Ikuo; Takeichi, Kazunari; Takano, Wataru; Nakamura, Yoshihiko

2015-07-01

Gesture recognition is used in many practical applications such as human-robot interaction, medical rehabilitation and sign language. With increasing motion sensor development, multiple data sources have become available, which leads to the rise of multi-modal gesture recognition. Since our previous approach to gesture recognition depends on a unimodal system, it is difficult to classify similar motion patterns. In order to solve this problem, a novel approach which integrates motion, audio and video models is proposed by using dataset captured by Kinect. The proposed system can recognize observed gestures by using three models. Recognition results of three models are integrated by using the proposed framework and the output becomes the final result. The motion and audio models are learned by using Hidden Markov Model. Random Forest which is the video classifier is used to learn the video model. In the experiments to test the performances of the proposed system, the motion and audio models most suitable for gesture recognition are chosen by varying feature vectors and learning methods. Additionally, the unimodal and multi-modal models are compared with respect to recognition accuracy. All the experiments are conducted on dataset provided by the competition organizer of MMGRC, which is a workshop for Multi-Modal Gesture Recognition Challenge. The comparison results show that the multi-modal model composed of three models scores the highest recognition rate. This improvement of recognition accuracy means that the complementary relationship among three models improves the accuracy of gesture recognition. The proposed system provides the application technology to understand human actions of daily life more precisely.

Reasoning about Users' Actions in a Graphical User Interface.

ERIC Educational Resources Information Center

Virvou, Maria; Kabassi, Katerina

2002-01-01

Describes a graphical user interface called IFM (Intelligent File Manipulator) that provides intelligent help to users. Explains two underlying reasoning mechanisms, one an adaptation of human plausible reasoning and one that performs goal recognition based on the effects of users' commands; and presents results of an empirical study that…

The simulating social mind: the role of the mirror neuron system and simulation in the social and communicative deficits of autism spectrum disorders.

PubMed

Oberman, Lindsay M; Ramachandran, Vilayanur S

2007-03-01

The mechanism by which humans perceive others differs greatly from how humans perceive inanimate objects. Unlike inanimate objects, humans have the distinct property of being "like me" in the eyes of the observer. This allows us to use the same systems that process knowledge about self-performed actions, self-conceived thoughts, and self-experienced emotions to understand actions, thoughts, and emotions in others. The authors propose that internal simulation mechanisms, such as the mirror neuron system, are necessary for normal development of recognition, imitation, theory of mind, empathy, and language. Additionally, the authors suggest that dysfunctional simulation mechanisms may underlie the social and communicative deficits seen in individuals with autism spectrum disorders.

Data-driven approach to human motion modeling with Lua and gesture description language

NASA Astrophysics Data System (ADS)

Hachaj, Tomasz; Koptyra, Katarzyna; Ogiela, Marek R.

2017-03-01

The aim of this paper is to present the novel proposition of the human motion modelling and recognition approach that enables real time MoCap signal evaluation. By motions (actions) recognition we mean classification. The role of this approach is to propose the syntactic description procedure that can be easily understood, learnt and used in various motion modelling and recognition tasks in all MoCap systems no matter if they are vision or wearable sensor based. To do so we have prepared extension of Gesture Description Language (GDL) methodology that enables movements description and real-time recognition so that it can use not only positional coordinates of body joints but virtually any type of discreetly measured output MoCap signals like accelerometer, magnetometer or gyroscope. We have also prepared and evaluated the cross-platform implementation of this approach using Lua scripting language and JAVA technology. This implementation is called Data Driven GDL (DD-GDL). In tested scenarios the average execution speed is above 100 frames per second which is an acquisition time of many popular MoCap solutions.

A unified probabilistic framework for spontaneous facial action modeling and understanding.

PubMed

Tong, Yan; Chen, Jixu; Ji, Qiang

2010-02-01

Facial expression is a natural and powerful means of human communication. Recognizing spontaneous facial actions, however, is very challenging due to subtle facial deformation, frequent head movements, and ambiguous and uncertain facial motion measurements. Because of these challenges, current research in facial expression recognition is limited to posed expressions and often in frontal view. A spontaneous facial expression is characterized by rigid head movements and nonrigid facial muscular movements. More importantly, it is the coherent and consistent spatiotemporal interactions among rigid and nonrigid facial motions that produce a meaningful facial expression. Recognizing this fact, we introduce a unified probabilistic facial action model based on the Dynamic Bayesian network (DBN) to simultaneously and coherently represent rigid and nonrigid facial motions, their spatiotemporal dependencies, and their image measurements. Advanced machine learning methods are introduced to learn the model based on both training data and subjective prior knowledge. Given the model and the measurements of facial motions, facial action recognition is accomplished through probabilistic inference by systematically integrating visual measurements with the facial action model. Experiments show that compared to the state-of-the-art techniques, the proposed system yields significant improvements in recognizing both rigid and nonrigid facial motions, especially for spontaneous facial expressions.

Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening †

PubMed Central

Yoon, Sang Min

2018-01-01

Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches. PMID:29614767

Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening.

PubMed

Cho, Heeryon; Yoon, Sang Min

2018-04-01

Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches.

Action Recognition and Movement Direction Discrimination Tasks Are Associated with Different Adaptation Patterns

PubMed Central

de la Rosa, Stephan; Ekramnia, Mina; Bülthoff, Heinrich H.

2016-01-01

The ability to discriminate between different actions is essential for action recognition and social interactions. Surprisingly previous research has often probed action recognition mechanisms with tasks that did not require participants to discriminate between actions, e.g., left-right direction discrimination tasks. It is not known to what degree visual processes in direction discrimination tasks are also involved in the discrimination of actions, e.g., when telling apart a handshake from a high-five. Here, we examined whether action discrimination is influenced by movement direction and whether direction discrimination depends on the type of action. We used an action adaptation paradigm to target action and direction discrimination specific visual processes. In separate conditions participants visually adapted to forward and backward moving handshake and high-five actions. Participants subsequently categorized either the action or the movement direction of an ambiguous action. The results showed that direction discrimination adaptation effects were modulated by the type of action but action discrimination adaptation effects were unaffected by movement direction. These results suggest that action discrimination and direction categorization rely on partly different visual information. We propose that action discrimination tasks should be considered for the exploration of visual action recognition mechanisms. PMID:26941633

A Meta-Analysis of Factors Influencing the Development of Trust in Automation: Implications for Human-Robot Interaction

DTIC Science & Technology

2014-07-01

Submoderating factors were examined and reported for human-related (i.e., age, cognitive factors, emotive factors) and automation- related (i.e., features and...capabilities) effects. Analyses were also conducted for type of automated aid: cognitive, control, and perceptual automation aids. Automated cognitive...operator, user) action. Perceptual aids are used to assist the operator or user by providing warnings or to assist with pattern recognition. All

Early prediction of student goals and affect in narrative-centered learning environments

NASA Astrophysics Data System (ADS)

Lee, Sunyoung

Recent years have seen a growing recognition of the role of goal and affect recognition in intelligent tutoring systems. Goal recognition is the task of inferring users' goals from a sequence of observations of their actions. Because of the uncertainty inherent in every facet of human computer interaction, goal recognition is challenging, particularly in contexts in which users can perform many actions in any order, as is the case with intelligent tutoring systems. Affect recognition is the task of identifying the emotional state of a user from a variety of physical cues, which are produced in response to affective changes in the individual. Accurately recognizing student goals and affect states could contribute to more effective and motivating interactions in intelligent tutoring systems. By exploiting knowledge of student goals and affect states, intelligent tutoring systems can dynamically modify their behavior to better support individual students. To create effective interactions in intelligent tutoring systems, goal and affect recognition models should satisfy two key requirements. First, because incorrectly predicted goals and affect states could significantly diminish the effectiveness of interactive systems, goal and affect recognition models should provide accurate predictions of user goals and affect states. When observations of users' activities become available, recognizers should make accurate early" predictions. Second, goal and affect recognition models should be highly efficient so they can operate in real time. To address key issues, we present an inductive approach to recognizing student goals and affect states in intelligent tutoring systems by learning goals and affect recognition models. Our work focuses on goal and affect recognition in an important new class of intelligent tutoring systems, narrative-centered learning environments. We report the results of empirical studies of induced recognition models from observations of students' interactions in narrative-centered learning environments. Experimental results suggest that induced models can make accurate early predictions of student goals and affect states, and they are sufficiently efficient to meet the real-time performance requirements of interactive learning environments.

A Generalized Process Model of Human Action Selection and Error and its Application to Error Prediction

DTIC Science & Technology

2014-07-01

Macmillan & Creelman , 2005). This is a quite high degree of discriminability and it means that when the decision model predicts a probability of...ROC analysis. Pattern Recognition Letters, 27(8), 861-874. Retrieved from Google Scholar. Macmillan, N. A., & Creelman , C. D. (2005). Detection

Les Droits de l'Homme et l'Education

NASA Astrophysics Data System (ADS)

Best, Francine

2002-07-01

The 21st century will, we hope, be the century of education or, as Jacques Delors put it in his report for UNESCO, the century of "lifelong learning". But this hope will only be realised if education is the subject and aim of a universal right. This right is enshrined in the 1948 Universal Declaration of Human Rights, which ought to be recognised in all countries of the world as the set of principles that should guide human action. The recognition of these rights should lead to a functioning democracy within educational establishments, where the rules of life should be the same for all: pupils, teachers and administrators. It is no less essential that human rights should constitute guiding principles for educational practice. The United Nations Decade for Human Rights (1995-2004) is an outstanding opportunity for each state to establish a plan of action for a true programme of human rights education.

It's all connected: Pathways in visual object recognition and early noun learning.

PubMed

Smith, Linda B

2013-11-01

A developmental pathway may be defined as the route, or chain of events, through which a new structure or function forms. For many human behaviors, including object name learning and visual object recognition, these pathways are often complex and multicausal and include unexpected dependencies. This article presents three principles of development that suggest the value of a developmental psychology that explicitly seeks to trace these pathways and uses empirical evidence on developmental dependencies among motor development, action on objects, visual object recognition, and object name learning in 12- to 24-month-old infants to make the case. The article concludes with a consideration of the theoretical implications of this approach. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Human Actions Analysis: Templates Generation, Matching and Visualization Applied to Motion Capture of Highly-Skilled Karate Athletes

PubMed Central

Piekarczyk, Marcin; Ogiela, Marek R.

2017-01-01

The aim of this paper is to propose and evaluate the novel method of template generation, matching, comparing and visualization applied to motion capture (kinematic) analysis. To evaluate our approach, we have used motion capture recordings (MoCap) of two highly-skilled black belt karate athletes consisting of 560 recordings of various karate techniques acquired with wearable sensors. We have evaluated the quality of generated templates; we have validated the matching algorithm that calculates similarities and differences between various MoCap data; and we have examined visualizations of important differences and similarities between MoCap data. We have concluded that our algorithms works the best when we are dealing with relatively short (2–4 s) actions that might be averaged and aligned with the dynamic time warping framework. In practice, the methodology is designed to optimize the performance of some full body techniques performed in various sport disciplines, for example combat sports and martial arts. We can also use this approach to generate templates or to compare the correct performance of techniques between various top sportsmen in order to generate a knowledge base of reference MoCap videos. The motion template generated by our method can be used for action recognition purposes. We have used the DTW classifier with angle-based features to classify various karate kicks. We have performed leave-one-out action recognition for the Shorin-ryu and Oyama karate master separately. In this case, 100% actions were correctly classified. In another experiment, we used templates generated from Oyama master recordings to classify Shorin-ryu master recordings and vice versa. In this experiment, the overall recognition rate was 94.2%, which is a very good result for this type of complex action. PMID:29125560

Action Identity in Style Simulation Systems: Do Players Consider Machine-Generated Music As of Their Own Style?

PubMed

Khatchatourov, Armen; Pachet, François; Rowe, Victoria

2016-01-01

The generation of musical material in a given style has been the subject of many studies with the increased sophistication of artificial intelligence models of musical style. In this paper we address a question of primary importance for artificial intelligence and music psychology: can such systems generate music that users indeed consider as corresponding to their own style? We address this question through an experiment involving both performance and recognition tasks with musically naïve school-age children. We asked 56 children to perform a free-form improvisation from which two kinds of music excerpt were created. One was a mere recording of original performances. The other was created by a software program designed to simulate the participants' style, based on their original performances. Two hours after the performance task, the children completed the recognition task in two conditions, one with the original excerpts and one with machine-generated music. Results indicate that the success rate is practically equivalent in two conditions: children tended to make correct attribution of the excerpts to themselves or to others, whether the music was human-produced or machine-generated (mean accuracy = 0.75 and = 0.71, respectively). We discuss this equivalence in accuracy for machine-generated and human produced music in the light of the literature on memory effects and action identity which addresses the recognition of one's own production.

Action Identity in Style Simulation Systems: Do Players Consider Machine-Generated Music As of Their Own Style?

PubMed Central

Khatchatourov, Armen; Pachet, François; Rowe, Victoria

2016-01-01

The generation of musical material in a given style has been the subject of many studies with the increased sophistication of artificial intelligence models of musical style. In this paper we address a question of primary importance for artificial intelligence and music psychology: can such systems generate music that users indeed consider as corresponding to their own style? We address this question through an experiment involving both performance and recognition tasks with musically naïve school-age children. We asked 56 children to perform a free-form improvisation from which two kinds of music excerpt were created. One was a mere recording of original performances. The other was created by a software program designed to simulate the participants' style, based on their original performances. Two hours after the performance task, the children completed the recognition task in two conditions, one with the original excerpts and one with machine-generated music. Results indicate that the success rate is practically equivalent in two conditions: children tended to make correct attribution of the excerpts to themselves or to others, whether the music was human-produced or machine-generated (mean accuracy = 0.75 and = 0.71, respectively). We discuss this equivalence in accuracy for machine-generated and human produced music in the light of the literature on memory effects and action identity which addresses the recognition of one's own production. PMID:27199788

Emotion through locomotion: gender impact.

PubMed

Krüger, Samuel; Sokolov, Alexander N; Enck, Paul; Krägeloh-Mann, Ingeborg; Pavlova, Marina A

2013-01-01

Body language reading is of significance for daily life social cognition and successful social interaction, and constitutes a core component of social competence. Yet it is unclear whether our ability for body language reading is gender specific. In the present work, female and male observers had to visually recognize emotions through point-light human locomotion performed by female and male actors with different emotional expressions. For subtle emotional expressions only, males surpass females in recognition accuracy and readiness to respond to happy walking portrayed by female actors, whereas females exhibit a tendency to be better in recognition of hostile angry locomotion expressed by male actors. In contrast to widespread beliefs about female superiority in social cognition, the findings suggest that gender effects in recognition of emotions from human locomotion are modulated by emotional content of actions and opposite actor gender. In a nutshell, the study makes a further step in elucidation of gender impact on body language reading and on neurodevelopmental and psychiatric deficits in visual social cognition.

Speech-associated gestures, Broca’s area, and the human mirror system

PubMed Central

Skipper, Jeremy I.; Goldin-Meadow, Susan; Nusbaum, Howard C.; Small, Steven L

2009-01-01

Speech-associated gestures are hand and arm movements that not only convey semantic information to listeners but are themselves actions. Broca’s area has been assumed to play an important role both in semantic retrieval or selection (as part of a language comprehension system) and in action recognition (as part of a “mirror” or “observation–execution matching” system). We asked whether the role that Broca’s area plays in processing speech-associated gestures is consistent with the semantic retrieval/selection account (predicting relatively weak interactions between Broca’s area and other cortical areas because the meaningful information that speech-associated gestures convey reduces semantic ambiguity and thus reduces the need for semantic retrieval/selection) or the action recognition account (predicting strong interactions between Broca’s area and other cortical areas because speech-associated gestures are goal-direct actions that are “mirrored”). We compared the functional connectivity of Broca’s area with other cortical areas when participants listened to stories while watching meaningful speech-associated gestures, speech-irrelevant self-grooming hand movements, or no hand movements. A network analysis of neuroimaging data showed that interactions involving Broca’s area and other cortical areas were weakest when spoken language was accompanied by meaningful speech-associated gestures, and strongest when spoken language was accompanied by self-grooming hand movements or by no hand movements at all. Results are discussed with respect to the role that the human mirror system plays in processing speech-associated movements. PMID:17533001

Mapping the emotional face. How individual face parts contribute to successful emotion recognition.

PubMed

Wegrzyn, Martin; Vogt, Maria; Kireclioglu, Berna; Schneider, Julia; Kissler, Johanna

2017-01-01

Which facial features allow human observers to successfully recognize expressions of emotion? While the eyes and mouth have been frequently shown to be of high importance, research on facial action units has made more precise predictions about the areas involved in displaying each emotion. The present research investigated on a fine-grained level, which physical features are most relied on when decoding facial expressions. In the experiment, individual faces expressing the basic emotions according to Ekman were hidden behind a mask of 48 tiles, which was sequentially uncovered. Participants were instructed to stop the sequence as soon as they recognized the facial expression and assign it the correct label. For each part of the face, its contribution to successful recognition was computed, allowing to visualize the importance of different face areas for each expression. Overall, observers were mostly relying on the eye and mouth regions when successfully recognizing an emotion. Furthermore, the difference in the importance of eyes and mouth allowed to group the expressions in a continuous space, ranging from sadness and fear (reliance on the eyes) to disgust and happiness (mouth). The face parts with highest diagnostic value for expression identification were typically located in areas corresponding to action units from the facial action coding system. A similarity analysis of the usefulness of different face parts for expression recognition demonstrated that faces cluster according to the emotion they express, rather than by low-level physical features. Also, expressions relying more on the eyes or mouth region were in close proximity in the constructed similarity space. These analyses help to better understand how human observers process expressions of emotion, by delineating the mapping from facial features to psychological representation.

Mapping the emotional face. How individual face parts contribute to successful emotion recognition

PubMed Central

Wegrzyn, Martin; Vogt, Maria; Kireclioglu, Berna; Schneider, Julia; Kissler, Johanna

2017-01-01

Which facial features allow human observers to successfully recognize expressions of emotion? While the eyes and mouth have been frequently shown to be of high importance, research on facial action units has made more precise predictions about the areas involved in displaying each emotion. The present research investigated on a fine-grained level, which physical features are most relied on when decoding facial expressions. In the experiment, individual faces expressing the basic emotions according to Ekman were hidden behind a mask of 48 tiles, which was sequentially uncovered. Participants were instructed to stop the sequence as soon as they recognized the facial expression and assign it the correct label. For each part of the face, its contribution to successful recognition was computed, allowing to visualize the importance of different face areas for each expression. Overall, observers were mostly relying on the eye and mouth regions when successfully recognizing an emotion. Furthermore, the difference in the importance of eyes and mouth allowed to group the expressions in a continuous space, ranging from sadness and fear (reliance on the eyes) to disgust and happiness (mouth). The face parts with highest diagnostic value for expression identification were typically located in areas corresponding to action units from the facial action coding system. A similarity analysis of the usefulness of different face parts for expression recognition demonstrated that faces cluster according to the emotion they express, rather than by low-level physical features. Also, expressions relying more on the eyes or mouth region were in close proximity in the constructed similarity space. These analyses help to better understand how human observers process expressions of emotion, by delineating the mapping from facial features to psychological representation. PMID:28493921

The hows and whys of face memory: level of construal influences the recognition of human faces

PubMed Central

Wyer, Natalie A.; Hollins, Timothy J.; Pahl, Sabine; Roper, Jean

2015-01-01

Three experiments investigated the influence of level of construal (i.e., the interpretation of actions in terms of their meaning or their details) on different stages of face memory. We employed a standard multiple-face recognition paradigm, with half of the faces inverted at test. Construal level was manipulated prior to recognition (Experiment 1), during study (Experiment 2) or both (Experiment 3). The results support a general advantage for high-level construal over low-level construal at both study and at test, and suggest that matching processing style between study and recognition has no advantage. These experiments provide additional evidence in support of a link between semantic processing (i.e., construal) and visual (i.e., face) processing. We conclude with a discussion of implications for current theories relating to both construal and face processing. PMID:26500586

The human mirror neuron system: A link between action observation and social skills

PubMed Central

Pineda, Jaime A.; Ramachandran, Vilayanur S.

2007-01-01

The discovery of the mirror neuron system (MNS) has led researchers to speculate that this system evolved from an embodied visual recognition apparatus in monkey to a system critical for social skills in humans. It is accepted that the MNS is specialized for processing animate stimuli, although the degree to which social interaction modulates the firing of mirror neurons has not been investigated. In the current study, EEG mu wave suppression was used as an index of MNS activity. Data were collected while subjects viewed four videos: (1) Visual White Noise: baseline, (2) Non-interacting: three individuals tossed a ball up in the air to themselves, (3) Social Action, Spectator: three individuals tossed a ball to each other and (4) Social Action, Interactive: similar to video 3 except occasionally the ball would be thrown off the screen toward the viewer. The mu wave was modulated by the degree of social interaction, with the Non-interacting condition showing the least suppression, followed by the Social Action, Spectator condition and the Social Action, Interactive condition showing the most suppression. These data suggest that the human MNS is specialized not only for processing animate stimuli, but specifically stimuli with social relevance. PMID:18985120

Brain mechanisms underlying human communication.

PubMed

Noordzij, Matthijs L; Newman-Norlund, Sarah E; de Ruiter, Jan Peter; Hagoort, Peter; Levinson, Stephen C; Toni, Ivan

2009-01-01

Human communication has been described as involving the coding-decoding of a conventional symbol system, which could be supported by parts of the human motor system (i.e. the "mirror neurons system"). However, this view does not explain how these conventions could develop in the first place. Here we target the neglected but crucial issue of how people organize their non-verbal behavior to communicate a given intention without pre-established conventions. We have measured behavioral and brain responses in pairs of subjects during communicative exchanges occurring in a real, interactive, on-line social context. In two fMRI studies, we found robust evidence that planning new communicative actions (by a sender) and recognizing the communicative intention of the same actions (by a receiver) relied on spatially overlapping portions of their brains (the right posterior superior temporal sulcus). The response of this region was lateralized to the right hemisphere, modulated by the ambiguity in meaning of the communicative acts, but not by their sensorimotor complexity. These results indicate that the sender of a communicative signal uses his own intention recognition system to make a prediction of the intention recognition performed by the receiver. This finding supports the notion that our communicative abilities are distinct from both sensorimotor processes and language abilities.

Brain Mechanisms Underlying Human Communication

PubMed Central

Noordzij, Matthijs L.; Newman-Norlund, Sarah E.; de Ruiter, Jan Peter; Hagoort, Peter; Levinson, Stephen C.; Toni, Ivan

2009-01-01

Human communication has been described as involving the coding-decoding of a conventional symbol system, which could be supported by parts of the human motor system (i.e. the “mirror neurons system”). However, this view does not explain how these conventions could develop in the first place. Here we target the neglected but crucial issue of how people organize their non-verbal behavior to communicate a given intention without pre-established conventions. We have measured behavioral and brain responses in pairs of subjects during communicative exchanges occurring in a real, interactive, on-line social context. In two fMRI studies, we found robust evidence that planning new communicative actions (by a sender) and recognizing the communicative intention of the same actions (by a receiver) relied on spatially overlapping portions of their brains (the right posterior superior temporal sulcus). The response of this region was lateralized to the right hemisphere, modulated by the ambiguity in meaning of the communicative acts, but not by their sensorimotor complexity. These results indicate that the sender of a communicative signal uses his own intention recognition system to make a prediction of the intention recognition performed by the receiver. This finding supports the notion that our communicative abilities are distinct from both sensorimotor processes and language abilities. PMID:19668699

Survey on Classifying Human Actions through Visual Sensors

DTIC Science & Technology

2011-04-08

International Conference on Automatic Face and Gesture Recognition, 2008, pp. 1-6, doi:10.1109/AFGR.2008.4813416. [47] Herrera, A., Beck , A., Bell, D...Announcement, DARPA- BAA -10-53, 2010 www.darpa.mil/tcto/docs/DARPA_ME_BAA-10-53_Mod1.pdf [84] Del Rose, M., Stein, J., “Survivability on the ART Robotic

Towards human behavior recognition based on spatio temporal features and support vector machines

NASA Astrophysics Data System (ADS)

Ghabri, Sawsen; Ouarda, Wael; Alimi, Adel M.

2017-03-01

Security and surveillance are vital issues in today's world. The recent acts of terrorism have highlighted the urgent need for efficient surveillance. There is indeed a need for an automated system for video surveillance which can detect identity and activity of person. In this article, we propose a new paradigm to recognize an aggressive human behavior such as boxing action. Our proposed system for human activity detection includes the use of a fusion between Spatio Temporal Interest Point (STIP) and Histogram of Oriented Gradient (HoG) features. The novel feature called Spatio Temporal Histogram Oriented Gradient (STHOG). To evaluate the robustness of our proposed paradigm with a local application of HoG technique on STIP points, we made experiments on KTH human action dataset based on Multi Class Support Vector Machines classification. The proposed scheme outperforms basic descriptors like HoG and STIP to achieve 82.26% us an accuracy value of classification rate.

The relationship between action anticipation and emotion recognition in athletes of open skill sports.

PubMed

Shih, Yu-Ling; Lin, Chia-Yen

2016-08-01

Action anticipation plays an important role in the successful performance of open skill sports, such as ball and combat sports. Evidence has shown that elite athletes of open sports excel in action anticipation. Most studies have targeted ball sports and agreed that information on body mechanics is one of the key determinants for successful action anticipation in open sports. However, less is known about combat sports, and whether facial emotions have an influence on athletes' action anticipation skill. It has been suggested that the understanding of intention in combat sports relies heavily on emotional context. Based on this suggestion, the present study compared the action anticipation performances of taekwondo athletes, weightlifting athletes, and non-athletes and then correlated these with their performances of emotion recognition. This study primarily found that accurate action anticipation does not necessarily rely on the dynamic information of movement, and that action anticipation performance is correlated with that of emotion recognition in taekwondo athletes, but not in weightlifting athletes. Our results suggest that the recognition of facial emotions plays a role in the action prediction in such combat sports as taekwondo.

Exemplar-based human action pose correction.

PubMed

Shen, Wei; Deng, Ke; Bai, Xiang; Leyvand, Tommer; Guo, Baining; Tu, Zhuowen

2014-07-01

The launch of Xbox Kinect has built a very successful computer vision product and made a big impact on the gaming industry. This sheds lights onto a wide variety of potential applications related to action recognition. The accurate estimation of human poses from the depth image is universally a critical step. However, existing pose estimation systems exhibit failures when facing severe occlusion. In this paper, we propose an exemplar-based method to learn to correct the initially estimated poses. We learn an inhomogeneous systematic bias by leveraging the exemplar information within a specific human action domain. Furthermore, as an extension, we learn a conditional model by incorporation of pose tags to further increase the accuracy of pose correction. In the experiments, significant improvements on both joint-based skeleton correction and tag prediction are observed over the contemporary approaches, including what is delivered by the current Kinect system. Our experiments for the facial landmark correction also illustrate that our algorithm can improve the accuracy of other detection/estimation systems.

Plan recognition and generalization in command languages with application to telerobotics

NASA Technical Reports Server (NTRS)

Yared, Wael I.; Sheridan, Thomas B.

1991-01-01

A method for pragmatic inference as a necessary accompaniment to command languages is proposed. The approach taken focuses on the modeling and recognition of the human operator's intent, which relates sequences of domain actions ('plans') to changes in some model of the task environment. The salient feature of this module is that it captures some of the physical and linguistic contextual aspects of an instruction. This provides a basis for generalization and reinterpretation of the instruction in different task environments. The theoretical development is founded on previous work in computational linguistics and some recent models in the theory of action and intention. To illustrate these ideas, an experimental command language to a telerobot is implemented. The program consists of three different components: a robot graphic simulation, the command language itself, and the domain-independent pragmatic inference module. Examples of task instruction processes are provided to demonstrate the benefits of this approach.

Mirror neurons: functions, mechanisms and models.

PubMed

Oztop, Erhan; Kawato, Mitsuo; Arbib, Michael A

2013-04-12

Mirror neurons for manipulation fire both when the animal manipulates an object in a specific way and when it sees another animal (or the experimenter) perform an action that is more or less similar. Such neurons were originally found in macaque monkeys, in the ventral premotor cortex, area F5 and later also in the inferior parietal lobule. Recent neuroimaging data indicate that the adult human brain is endowed with a "mirror neuron system," putatively containing mirror neurons and other neurons, for matching the observation and execution of actions. Mirror neurons may serve action recognition in monkeys as well as humans, whereas their putative role in imitation and language may be realized in human but not in monkey. This article shows the important role of computational models in providing sufficient and causal explanations for the observed phenomena involving mirror systems and the learning processes which form them, and underlines the need for additional circuitry to lift up the monkey mirror neuron circuit to sustain the posited cognitive functions attributed to the human mirror neuron system. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Inferior frontal oscillations reveal visuo-motor matching for actions and speech: evidence from human intracranial recordings.

PubMed

Halje, Pär; Seeck, Margitta; Blanke, Olaf; Ionta, Silvio

2015-12-01

The neural correspondence between the systems responsible for the execution and recognition of actions has been suggested both in humans and non-human primates. Apart from being a key region of this visuo-motor observation-execution matching (OEM) system, the human inferior frontal gyrus (IFG) is also important for speech production. The functional overlap of visuo-motor OEM and speech, together with the phylogenetic history of the IFG as a motor area, has led to the idea that speech function has evolved from pre-existing motor systems and to the hypothesis that an OEM system may exist also for speech. However, visuo-motor OEM and speech OEM have never been compared directly. We used electrocorticography to analyze oscillations recorded from intracranial electrodes in human fronto-parieto-temporal cortex during visuo-motor (executing or visually observing an action) and speech OEM tasks (verbally describing an action using the first or third person pronoun). The results show that neural activity related to visuo-motor OEM is widespread in the frontal, parietal, and temporal regions. Speech OEM also elicited widespread responses partly overlapping with visuo-motor OEM sites (bilaterally), including frontal, parietal, and temporal regions. Interestingly a more focal region, the inferior frontal gyrus (bilaterally), showed both visuo-motor OEM and speech OEM properties independent of orolingual speech-unrelated movements. Building on the methodological advantages in human invasive electrocorticography, the present findings provide highly precise spatial and temporal information to support the existence of a modality-independent action representation system in the human brain that is shared between systems for performing, interpreting and describing actions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Environmental Identity Development through Social Interactions, Action, and Recognition

ERIC Educational Resources Information Center

Stapleton, Sarah Riggs

2015-01-01

This article uses sociocultural identity theory to explore how practice, action, and recognition can facilitate environmental identity development. Recognition, a construct not previously explored in environmental identity literature, is particularly examined. The study is based on a group of diverse teens who traveled to South Asia to participate…

Understanding Decision Making in Critical Care

PubMed Central

Lighthall, Geoffrey K.; Vazquez-Guillamet, Cristina

2015-01-01

Background Human decision making involves the deliberate formulation of hypotheses and plans as well as the use of subconscious means of judging probability, likely outcome, and proper action. Rationale There is a growing recognition that intuitive strategies such as use of heuristics and pattern recognition described in other industries are applicable to high-acuity environments in medicine. Despite the applicability of theories of cognition to the intensive care unit, a discussion of decision-making strategies is currently absent in the critical care literature. Content This article provides an overview of known cognitive strategies, as well as a synthesis of their use in critical care. By understanding the ways by which humans formulate diagnoses and make critical decisions, we may be able to minimize errors in our own judgments as well as build training activities around known strengths and limitations of cognition. PMID:26387708

[The complexity of articulating rights: nutrition and care].

PubMed

Pautassi, Laura Cecilia

2016-01-01

This article analyzes the existing tensions between the recognition of human rights - especially the right to adequate food as it is defined in international agreements and treaties - and the insufficient connection made with care, understood as the set of activities necessary to satisfy the basic needs of existence and human and social reproduction. Applying a methodological approach based in rights and gender, the article analyzes, on one hand, the scope of the right to food and its impact at the level of public institutionality, and on the other, the recent recognition of care as a right at a regional level and its persistent invisibilization in public policies. The results obtained allow for a research and action agenda that identifies tensions and opportunities to achieve universalization in the exercise of rights based in comprehensive and interdependent public policies.

2004 Environment Industry

DTIC Science & Technology

2004-01-01

the country of greatest concern in the region, has put under government protection 10% of the Amazon region, created a national water resources policy...activity. The desire for basic human security often outweighs concern about environmental impact. Logging in the Amazon region, for example, is necessary...Conservation International and the Rainforest Action Network. Now that local groups are receiving more recognition from their own governments, the

Human action recognition with group lasso regularized-support vector machine

NASA Astrophysics Data System (ADS)

Luo, Huiwu; Lu, Huanzhang; Wu, Yabei; Zhao, Fei

2016-05-01

The bag-of-visual-words (BOVW) and Fisher kernel are two popular models in human action recognition, and support vector machine (SVM) is the most commonly used classifier for the two models. We show two kinds of group structures in the feature representation constructed by BOVW and Fisher kernel, respectively, since the structural information of feature representation can be seen as a prior for the classifier and can improve the performance of the classifier, which has been verified in several areas. However, the standard SVM employs L2-norm regularization in its learning procedure, which penalizes each variable individually and cannot express the structural information of feature representation. We replace the L2-norm regularization with group lasso regularization in standard SVM, and a group lasso regularized-support vector machine (GLRSVM) is proposed. Then, we embed the group structural information of feature representation into GLRSVM. Finally, we introduce an algorithm to solve the optimization problem of GLRSVM by alternating directions method of multipliers. The experiments evaluated on KTH, YouTube, and Hollywood2 datasets show that our method achieves promising results and improves the state-of-the-art methods on KTH and YouTube datasets.

Componential Network for the Recognition of Tool-Associated Actions: Evidence from Voxel-based Lesion-Symptom Mapping in Acute Stroke Patients.

PubMed

Martin, Markus; Dressing, Andrea; Bormann, Tobias; Schmidt, Charlotte S M; Kümmerer, Dorothee; Beume, Lena; Saur, Dorothee; Mader, Irina; Rijntjes, Michel; Kaller, Christoph P; Weiller, Cornelius

2017-08-01

The study aimed to elucidate areas involved in recognizing tool-associated actions, and to characterize the relationship between recognition and active performance of tool use.We performed voxel-based lesion-symptom mapping in a prospective cohort of 98 acute left-hemisphere ischemic stroke patients (68 male, age mean ± standard deviation, 65 ± 13 years; examination 4.4 ± 2 days post-stroke). In a video-based test, patients distinguished correct tool-related actions from actions with spatio-temporal (incorrect grip, kinematics, or tool orientation) or conceptual errors (incorrect tool-recipient matching, e.g., spreading jam on toast with a paintbrush). Moreover, spatio-temporal and conceptual errors were determined during actual tool use.Deficient spatio-temporal error discrimination followed lesions within a dorsal network in which the inferior parietal lobule (IPL) and the lateral temporal cortex (sLTC) were specifically relevant for assessing functional hand postures and kinematics, respectively. Conversely, impaired recognition of conceptual errors resulted from damage to ventral stream regions including anterior temporal lobe. Furthermore, LTC and IPL lesions impacted differently on action recognition and active tool use, respectively.In summary, recognition of tool-associated actions relies on a componential network. Our study particularly highlights the dissociable roles of LTC and IPL for the recognition of action kinematics and functional hand postures, respectively. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Neural and Computational Mechanisms of Action Processing: Interaction between Visual and Motor Representations.

PubMed

Giese, Martin A; Rizzolatti, Giacomo

2015-10-07

Action recognition has received enormous interest in the field of neuroscience over the last two decades. In spite of this interest, the knowledge in terms of fundamental neural mechanisms that provide constraints for underlying computations remains rather limited. This fact stands in contrast with a wide variety of speculative theories about how action recognition might work. This review focuses on new fundamental electrophysiological results in monkeys, which provide constraints for the detailed underlying computations. In addition, we review models for action recognition and processing that have concrete mathematical implementations, as opposed to conceptual models. We think that only such implemented models can be meaningfully linked quantitatively to physiological data and have a potential to narrow down the many possible computational explanations for action recognition. In addition, only concrete implementations allow judging whether postulated computational concepts have a feasible implementation in terms of realistic neural circuits. Copyright © 2015 Elsevier Inc. All rights reserved.

Neural associative memories for the integration of language, vision and action in an autonomous agent.

PubMed

Markert, H; Kaufmann, U; Kara Kayikci, Z; Palm, G

2009-03-01

Language understanding is a long-standing problem in computer science. However, the human brain is capable of processing complex languages with seemingly no difficulties. This paper shows a model for language understanding using biologically plausible neural networks composed of associative memories. The model is able to deal with ambiguities on the single word and grammatical level. The language system is embedded into a robot in order to demonstrate the correct semantical understanding of the input sentences by letting the robot perform corresponding actions. For that purpose, a simple neural action planning system has been combined with neural networks for visual object recognition and visual attention control mechanisms.

Behavioral and neurophysiological correlates of regret in rat decision-making on a neuroeconomic task

PubMed Central

Steiner, Adam P.; Redish, A. David

2014-01-01

Summary Disappointment entails the recognition that one did not get the value one expected. In contrast, regret entails the recognition that an alternate (counterfactual) action would have produced a more valued outcome. Thus, the key to identifying regret is the representation of that counterfactual option in situations in which a mistake has been made. In humans, the orbitofrontal cortex is active during expressions of regret, and humans with damage to the orbitofrontal cortex do not express regret. In rats and non-human primates, both the orbitofrontal cortex and the ventral striatum have been implicated in decision-making, particularly in representations of expectations of reward. In order to examine representations of regretful situations, we recorded neural ensembles from orbitofrontal cortex and ventral striatum in rats encountering a spatial sequence of wait/skip choices for delayed delivery of different food flavors. We were able to measure preferences using an economic framework. Rats occasionally skipped low-cost choices and then encountered a high-cost choice. This sequence economically defines a potential regret-inducing instance. In these situations, rats looked backwards towards the lost option, the cells within the orbitofrontal cortex and ventral striatum represented that missed action, rats were more likely to wait for the long delay, and rats rushed through eating the food after that delay. That these situations drove rats to modify their behavior suggests that regret-like processes modify decision-making in non-human mammals. PMID:24908102

Human brain regions involved in recognizing environmental sounds.

PubMed

Lewis, James W; Wightman, Frederic L; Brefczynski, Julie A; Phinney, Raymond E; Binder, Jeffrey R; DeYoe, Edgar A

2004-09-01

To identify the brain regions preferentially involved in environmental sound recognition (comprising portions of a putative auditory 'what' pathway), we collected functional imaging data while listeners attended to a wide range of sounds, including those produced by tools, animals, liquids and dropped objects. These recognizable sounds, in contrast to unrecognizable, temporally reversed control sounds, evoked activity in a distributed network of brain regions previously associated with semantic processing, located predominantly in the left hemisphere, but also included strong bilateral activity in posterior portions of the middle temporal gyri (pMTG). Comparisons with earlier studies suggest that these bilateral pMTG foci partially overlap cortex implicated in high-level visual processing of complex biological motion and recognition of tools and other artifacts. We propose that the pMTG foci process multimodal (or supramodal) information about objects and object-associated motion, and that this may represent 'action' knowledge that can be recruited for purposes of recognition of familiar environmental sound-sources. These data also provide a functional and anatomical explanation for the symptoms of pure auditory agnosia for environmental sounds reported in human lesion studies.

Temporal and Fine-Grained Pedestrian Action Recognition on Driving Recorder Database

PubMed Central

Satoh, Yutaka; Aoki, Yoshimitsu; Oikawa, Shoko; Matsui, Yasuhiro

2018-01-01

The paper presents an emerging issue of fine-grained pedestrian action recognition that induces an advanced pre-crush safety to estimate a pedestrian intention in advance. The fine-grained pedestrian actions include visually slight differences (e.g., walking straight and crossing), which are difficult to distinguish from each other. It is believed that the fine-grained action recognition induces a pedestrian intention estimation for a helpful advanced driver-assistance systems (ADAS). The following difficulties have been studied to achieve a fine-grained and accurate pedestrian action recognition: (i) In order to analyze the fine-grained motion of a pedestrian appearance in the vehicle-mounted drive recorder, a method to describe subtle change of motion characteristics occurring in a short time is necessary; (ii) even when the background moves greatly due to the driving of the vehicle, it is necessary to detect changes in subtle motion of the pedestrian; (iii) the collection of large-scale fine-grained actions is very difficult, and therefore a relatively small database should be focused. We find out how to learn an effective recognition model with only a small-scale database. Here, we have thoroughly evaluated several types of configurations to explore an effective approach in fine-grained pedestrian action recognition without a large-scale database. Moreover, two different datasets have been collected in order to raise the issue. Finally, our proposal attained 91.01% on National Traffic Science and Environment Laboratory database (NTSEL) and 53.23% on the near-miss driving recorder database (NDRDB). The paper has improved +8.28% and +6.53% from baseline two-stream fusion convnets. PMID:29461473

Study to determine potential flight applications and human factors design guidelines for voice recognition and synthesis systems

NASA Astrophysics Data System (ADS)

White, R. W.; Parks, D. L.

1985-07-01

A study was conducted to determine potential commercial aircraft flight deck applications and implementation guidelines for voice recognition and synthesis. At first, a survey of voice recognition and synthesis technology was undertaken to develop a working knowledge base. Then, numerous potential aircraft and simulator flight deck voice applications were identified and each proposed application was rated on a number of criteria in order to achieve an overall payoff rating. The potential voice recognition applications fell into five general categories: programming, interrogation, data entry, switch and mode selection, and continuous/time-critical action control. The ratings of the first three categories showed the most promise of being beneficial to flight deck operations. Possible applications of voice synthesis systems were categorized as automatic or pilot selectable and many were rated as being potentially beneficial. In addition, voice system implementation guidelines and pertinent performance criteria are proposed. Finally, the findings of this study are compared with those made in a recent NASA study of a 1995 transport concept.

Study to determine potential flight applications and human factors design guidelines for voice recognition and synthesis systems

NASA Technical Reports Server (NTRS)

White, R. W.; Parks, D. L.

1985-01-01

A study was conducted to determine potential commercial aircraft flight deck applications and implementation guidelines for voice recognition and synthesis. At first, a survey of voice recognition and synthesis technology was undertaken to develop a working knowledge base. Then, numerous potential aircraft and simulator flight deck voice applications were identified and each proposed application was rated on a number of criteria in order to achieve an overall payoff rating. The potential voice recognition applications fell into five general categories: programming, interrogation, data entry, switch and mode selection, and continuous/time-critical action control. The ratings of the first three categories showed the most promise of being beneficial to flight deck operations. Possible applications of voice synthesis systems were categorized as automatic or pilot selectable and many were rated as being potentially beneficial. In addition, voice system implementation guidelines and pertinent performance criteria are proposed. Finally, the findings of this study are compared with those made in a recent NASA study of a 1995 transport concept.

Human Action Recognition in Surveillance Videos using Abductive Reasoning on Linear Temporal Logic

DTIC Science & Technology

2012-08-29

help of the optical flows (Lucas 75 and Kanade, 1981). 76 3.2 Atomic Propositions 77 isAt (ti, Oj, Lk)  Object Oj is at location Lk at time...simultaneously at two locations in the same frame. This can 84 be represented mathematically as: 85 isAt (ti, Oj, Lk... isAt (ti, Oj, Lm)  Lk   Lm

The neural basis of body form and body action agnosia.

PubMed

Moro, Valentina; Urgesi, Cosimo; Pernigo, Simone; Lanteri, Paola; Pazzaglia, Mariella; Aglioti, Salvatore Maria

2008-10-23

Visual analysis of faces and nonfacial body stimuli brings about neural activity in different cortical areas. Moreover, processing body form and body action relies on distinct neural substrates. Although brain lesion studies show specific face processing deficits, neuropsychological evidence for defective recognition of nonfacial body parts is lacking. By combining psychophysics studies with lesion-mapping techniques, we found that lesions of ventromedial, occipitotemporal areas induce face and body recognition deficits while lesions involving extrastriate body area seem causatively associated with impaired recognition of body but not of face and object stimuli. We also found that body form and body action recognition deficits can be double dissociated and are causatively associated with lesions to extrastriate body area and ventral premotor cortex, respectively. Our study reports two category-specific visual deficits, called body form and body action agnosia, and highlights their neural underpinnings.

Attention, biological motion, and action recognition.

PubMed

Thompson, James; Parasuraman, Raja

2012-01-02

Interacting with others in the environment requires that we perceive and recognize their movements and actions. Neuroimaging and neuropsychological studies have indicated that a number of brain regions, particularly the superior temporal sulcus, are involved in a number of processes essential for action recognition, including the processing of biological motion and processing the intentions of actions. We review the behavioral and neuroimaging evidence suggesting that while some aspects of action recognition might be rapid and effective, they are not necessarily automatic. Attention is particularly important when visual information about actions is degraded or ambiguous, or if competing information is present. We present evidence indicating that neural responses associated with the processing of biological motion are strongly modulated by attention. In addition, behavioral and neuroimaging evidence shows that drawing inferences from the actions of others is attentionally demanding. The role of attention in action observation has implications for everyday social interactions and workplace applications that depend on observing, understanding and interpreting actions. Published by Elsevier Inc.

An intrinsic vasopressin system in the olfactory bulb is involved in social recognition

PubMed Central

Tobin, Vicky A.; Hashimoto, Hirofumi; Wacker, Douglas W.; Takayanagi, Yuki; Langnaese, Kristina; Caquineau, Celine; Noack, Julia; Landgraf, Rainer; Onaka, Tatsushi; Leng, Gareth; Meddle, Simone L.; Engelmann, Mario; Ludwig, Mike

2010-01-01

Many peptides, when released as chemical messengers within the brain, have powerful influences on complex behaviours. Most strikingly, vasopressin and oxytocin, once thought of as circulating hormones whose actions were confined to peripheral organs, are now known to be released in the brain where they play fundamentally important roles in social behaviours1. In humans, disruptions of these peptide systems have been linked to several neurobehavioural disorders, including Prader-Willi syndrome, affective disorders, and obsessive-compulsive disorder, and polymorphisms of the vasopressin V1a receptor have been linked to autism2,3. Here we report that the rat olfactory bulb contains a large population of interneurones which express vasopressin, that blocking the actions of vasopressin in the olfactory bulb impairs the social recognition abilities of rats, and that vasopressin agonists and antagonists can modulate the processing of information by olfactory bulb neurones. The findings indicate that social information is processed in part by a vasopressin system intrinsic to the olfactory system. PMID:20182426

Spontaneous cross-species imitation in interactions between chimpanzees and zoo visitors.

PubMed

Persson, Tomas; Sauciuc, Gabriela-Alina; Madsen, Elainie Alenkær

2018-01-01

Imitation is a cornerstone of human development, serving both a cognitive function (e.g. in the acquisition and transmission of skills and knowledge) and a social-communicative function, whereby the imitation of familiar actions serves to maintain social interaction and promote prosociality. In nonhuman primates, this latter function is poorly understood, or even claimed to be absent. In this observational study, we documented interactions between chimpanzees and zoo visitors and found that the two species imitated each other at a similar rate, corresponding to almost 10% of all produced actions. Imitation appeared to accomplish a social-communicative function, as cross-species interactions that contained imitative actions lasted significantly longer than interactions without imitation. In both species, physical proximity promoted cross-species imitation. Overall, imitative precision was higher among visitors than among chimpanzees, but this difference vanished in proximity contexts, i.e. in the indoor environment. Four of five chimpanzees produced imitations; three of them exhibited comparable imitation rates, despite large individual differences in level of cross-species interactivity. We also found that chimpanzees evidenced imitation recognition, yet only when visitors imitated their actions (as opposed to postures). Imitation recognition was expressed by returned imitation in 36% of the cases, and all four imitating chimpanzees engaged in so-called imitative games. Previously regarded as unique to early human socialization, such games serve to maintain social engagement. The results presented here indicate that nonhuman apes exhibit spontaneous imitation that can accomplish a communicative function. The study raises a number of novel questions for imitation research and highlights the imitation of familiar behaviours as a relevant-yet thus far understudied-research topic.

Gender affects body language reading.

PubMed

Sokolov, Arseny A; Krüger, Samuel; Enck, Paul; Krägeloh-Mann, Ingeborg; Pavlova, Marina A

2011-01-01

Body motion is a rich source of information for social cognition. However, gender effects in body language reading are largely unknown. Here we investigated whether, and, if so, how recognition of emotional expressions revealed by body motion is gender dependent. To this end, females and males were presented with point-light displays portraying knocking at a door performed with different emotional expressions. The findings show that gender affects accuracy rather than speed of body language reading. This effect, however, is modulated by emotional content of actions: males surpass in recognition accuracy of happy actions, whereas females tend to excel in recognition of hostile angry knocking. Advantage of women in recognition accuracy of neutral actions suggests that females are better tuned to the lack of emotional content in body actions. The study provides novel insights into understanding of gender effects in body language reading, and helps to shed light on gender vulnerability to neuropsychiatric and neurodevelopmental impairments in visual social cognition.

Action adaptation during natural unfolding social scenes influences action recognition and inferences made about actor beliefs.

PubMed

Keefe, Bruce D; Wincenciak, Joanna; Jellema, Tjeerd; Ward, James W; Barraclough, Nick E

2016-07-01

When observing another individual's actions, we can both recognize their actions and infer their beliefs concerning the physical and social environment. The extent to which visual adaptation influences action recognition and conceptually later stages of processing involved in deriving the belief state of the actor remains unknown. To explore this we used virtual reality (life-size photorealistic actors presented in stereoscopic three dimensions) to see how visual adaptation influences the perception of individuals in naturally unfolding social scenes at increasingly higher levels of action understanding. We presented scenes in which one actor picked up boxes (of varying number and weight), after which a second actor picked up a single box. Adaptation to the first actor's behavior systematically changed perception of the second actor. Aftereffects increased with the duration of the first actor's behavior, declined exponentially over time, and were independent of view direction. Inferences about the second actor's expectation of box weight were also distorted by adaptation to the first actor. Distortions in action recognition and actor expectations did not, however, extend across different actions, indicating that adaptation is not acting at an action-independent abstract level but rather at an action-dependent level. We conclude that although adaptation influences more complex inferences about belief states of individuals, this is likely to be a result of adaptation at an earlier action recognition stage rather than adaptation operating at a higher, more abstract level in mentalizing or simulation systems.

What puts the how in where? Tool use and the divided visual streams hypothesis.

PubMed

Frey, Scott H

2007-04-01

An influential theory suggests that the dorsal (occipito-parietal) visual stream computes representations of objects for purposes of guiding actions (determining 'how') independently of ventral (occipito-temporal) stream processes supporting object recognition and semantic processing (determining 'what'). Yet, the ability of the dorsal stream alone to account for one of the most common forms of human action, tool use, is limited. While experience-dependent modifications to existing dorsal stream representations may explain simple tool use behaviors (e.g., using sticks to extend reach) found among a variety of species, skillful use of manipulable artifacts (e.g., cups, hammers, pencils) requires in addition access to semantic representations of objects' functions and uses. Functional neuroimaging suggests that this latter information is represented in a left-lateralized network of temporal, frontal and parietal areas. I submit that the well-established dominance of the human left hemisphere in the representation of familiar skills stems from the ability for this acquired knowledge to influence the organization of actions within the dorsal pathway.

A Unified Framework for Activity Recognition-Based Behavior Analysis and Action Prediction in Smart Homes

PubMed Central

Fatima, Iram; Fahim, Muhammad; Lee, Young-Koo; Lee, Sungyoung

2013-01-01

In recent years, activity recognition in smart homes is an active research area due to its applicability in many applications, such as assistive living and healthcare. Besides activity recognition, the information collected from smart homes has great potential for other application domains like lifestyle analysis, security and surveillance, and interaction monitoring. Therefore, discovery of users common behaviors and prediction of future actions from past behaviors become an important step towards allowing an environment to provide personalized service. In this paper, we develop a unified framework for activity recognition-based behavior analysis and action prediction. For this purpose, first we propose kernel fusion method for accurate activity recognition and then identify the significant sequential behaviors of inhabitants from recognized activities of their daily routines. Moreover, behaviors patterns are further utilized to predict the future actions from past activities. To evaluate the proposed framework, we performed experiments on two real datasets. The results show a remarkable improvement of 13.82% in the accuracy on average of recognized activities along with the extraction of significant behavioral patterns and precise activity predictions with 6.76% increase in F-measure. All this collectively help in understanding the users” actions to gain knowledge about their habits and preferences. PMID:23435057

What Three-Year-Olds Remember from Their Past: Long-Term Memory for Persons, Objects, and Actions

ERIC Educational Resources Information Center

Hirte, Monika; Graf, Frauke; Kim, Ziyon; Knopf, Monika

2017-01-01

From birth on, infants show long-term recognition memory for persons. Furthermore, infants from six months onwards are able to store and retrieve demonstrated actions over long-term intervals in deferred imitation tasks. Thus, information about the model demonstrating the object-related actions is stored and recognition memory for the objects as…

Long-term behavior understanding based on the expert-based combination of short-term observations in high-resolution CCTV

NASA Astrophysics Data System (ADS)

Schutte, Klamer; Burghouts, Gertjan; van der Stap, Nanda; Westerwoudt, Victor; Bouma, Henri; Kruithof, Maarten; Baan, Jan; ten Hove, Johan-Martijn

2016-10-01

The bottleneck in situation awareness is no longer in the sensing domain but rather in the data interpretation domain, since the number of sensors is rapidly increasing and it is not affordable to increase human data-analysis capacity at the same rate. Automatic image analysis can assist a human analyst by alerting when an event of interest occurs. However, common state-of-the-art image recognition systems learn representations in high-dimensional feature spaces, which makes them less suitable to generate a user-comprehensive message. Such data-driven approaches rely on large amounts of training data, which is often not available for quite rare but high-impact incidents in the security domain. The key contribution of this paper is that we present a novel real-time system for image understanding based on generic instantaneous low-level processing components (symbols) and flexible user-definable and user-understandable combinations of these components (sentences) at a higher level for the recognition of specific relevant events in the security domain. We show that the detection of an event of interest can be enhanced by utilizing recognition of multiple short-term preparatory actions.

A Human Activity Recognition System Based on Dynamic Clustering of Skeleton Data.

PubMed

Manzi, Alessandro; Dario, Paolo; Cavallo, Filippo

2017-05-11

Human activity recognition is an important area in computer vision, with its wide range of applications including ambient assisted living. In this paper, an activity recognition system based on skeleton data extracted from a depth camera is presented. The system makes use of machine learning techniques to classify the actions that are described with a set of a few basic postures. The training phase creates several models related to the number of clustered postures by means of a multiclass Support Vector Machine (SVM), trained with Sequential Minimal Optimization (SMO). The classification phase adopts the X-means algorithm to find the optimal number of clusters dynamically. The contribution of the paper is twofold. The first aim is to perform activity recognition employing features based on a small number of informative postures, extracted independently from each activity instance; secondly, it aims to assess the minimum number of frames needed for an adequate classification. The system is evaluated on two publicly available datasets, the Cornell Activity Dataset (CAD-60) and the Telecommunication Systems Team (TST) Fall detection dataset. The number of clusters needed to model each instance ranges from two to four elements. The proposed approach reaches excellent performances using only about 4 s of input data (~100 frames) and outperforms the state of the art when it uses approximately 500 frames on the CAD-60 dataset. The results are promising for the test in real context.

Grasping synergies: A motor-control approach to the mirror neuron mechanism

NASA Astrophysics Data System (ADS)

D'Ausilio, Alessandro; Bartoli, Eleonora; Maffongelli, Laura

2015-03-01

The discovery of mirror neurons revived interest in motor theories of perception, fostering a number of new studies as well as controversies. In particular, the degree of motor specificity with which others' actions are simulated is highly debated. Human corticospinal excitability studies support the conjecture that a mirror mechanism encodes object-directed goals or low-level kinematic features of others' reaching and grasping actions. These interpretations lead to different experimental predictions and implications for the functional role of the simulation of others' actions. We propose that the representational granularity of the mirror mechanism cannot be any different from that of the motor system during action execution. Hence, drawing from motor control models, we propose that the building blocks of the mirror mechanism are the relatively few motor synergies explaining the variety of hand functions. The recognition of these synergies, from action observation, can be potentially very robust to visual noise and thus demonstrate a clear advantage of using motor knowledge for classifying others' action.

Seamless Tracing of Human Behavior Using Complementary Wearable and House-Embedded Sensors

PubMed Central

Augustyniak, Piotr; Smoleń, Magdalena; Mikrut, Zbigniew; Kańtoch, Eliasz

2014-01-01

This paper presents a multimodal system for seamless surveillance of elderly people in their living environment. The system uses simultaneously a wearable sensor network for each individual and premise-embedded sensors specific for each environment. The paper demonstrates the benefits of using complementary information from two types of mobility sensors: visual flow-based image analysis and an accelerometer-based wearable network. The paper provides results for indoor recognition of several elementary poses and outdoor recognition of complex movements. Instead of complete system description, particular attention was drawn to a polar histogram-based method of visual pose recognition, complementary use and synchronization of the data from wearable and premise-embedded networks and an automatic danger detection algorithm driven by two premise- and subject-related databases. The novelty of our approach also consists in feeding the databases with real-life recordings from the subject, and in using the dynamic time-warping algorithm for measurements of distance between actions represented as elementary poses in behavioral records. The main results of testing our method include: 95.5% accuracy of elementary pose recognition by the video system, 96.7% accuracy of elementary pose recognition by the accelerometer-based system, 98.9% accuracy of elementary pose recognition by the combined accelerometer and video-based system, and 80% accuracy of complex outdoor activity recognition by the accelerometer-based wearable system. PMID:24787640

Support vector machine-based facial-expression recognition method combining shape and appearance

NASA Astrophysics Data System (ADS)

Han, Eun Jung; Kang, Byung Jun; Park, Kang Ryoung; Lee, Sangyoun

2010-11-01

Facial expression recognition can be widely used for various applications, such as emotion-based human-machine interaction, intelligent robot interfaces, face recognition robust to expression variation, etc. Previous studies have been classified as either shape- or appearance-based recognition. The shape-based method has the disadvantage that the individual variance of facial feature points exists irrespective of similar expressions, which can cause a reduction of the recognition accuracy. The appearance-based method has a limitation in that the textural information of the face is very sensitive to variations in illumination. To overcome these problems, a new facial-expression recognition method is proposed, which combines both shape and appearance information, based on the support vector machine (SVM). This research is novel in the following three ways as compared to previous works. First, the facial feature points are automatically detected by using an active appearance model. From these, the shape-based recognition is performed by using the ratios between the facial feature points based on the facial-action coding system. Second, the SVM, which is trained to recognize the same and different expression classes, is proposed to combine two matching scores obtained from the shape- and appearance-based recognitions. Finally, a single SVM is trained to discriminate four different expressions, such as neutral, a smile, anger, and a scream. By determining the expression of the input facial image whose SVM output is at a minimum, the accuracy of the expression recognition is much enhanced. The experimental results showed that the recognition accuracy of the proposed method was better than previous researches and other fusion methods.

Action recognition using mined hierarchical compound features.

PubMed

Gilbert, Andrew; Illingworth, John; Bowden, Richard

2011-05-01

The field of Action Recognition has seen a large increase in activity in recent years. Much of the progress has been through incorporating ideas from single-frame object recognition and adapting them for temporal-based action recognition. Inspired by the success of interest points in the 2D spatial domain, their 3D (space-time) counterparts typically form the basic components used to describe actions, and in action recognition the features used are often engineered to fire sparsely. This is to ensure that the problem is tractable; however, this can sacrifice recognition accuracy as it cannot be assumed that the optimum features in terms of class discrimination are obtained from this approach. In contrast, we propose to initially use an overcomplete set of simple 2D corners in both space and time. These are grouped spatially and temporally using a hierarchical process, with an increasing search area. At each stage of the hierarchy, the most distinctive and descriptive features are learned efficiently through data mining. This allows large amounts of data to be searched for frequently reoccurring patterns of features. At each level of the hierarchy, the mined compound features become more complex, discriminative, and sparse. This results in fast, accurate recognition with real-time performance on high-resolution video. As the compound features are constructed and selected based upon their ability to discriminate, their speed and accuracy increase at each level of the hierarchy. The approach is tested on four state-of-the-art data sets, the popular KTH data set to provide a comparison with other state-of-the-art approaches, the Multi-KTH data set to illustrate performance at simultaneous multiaction classification, despite no explicit localization information provided during training. Finally, the recent Hollywood and Hollywood2 data sets provide challenging complex actions taken from commercial movie sequences. For all four data sets, the proposed hierarchical approach outperforms all other methods reported thus far in the literature and can achieve real-time operation.

Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data

PubMed Central

Munoz-Organero, Mario; Lotfi, Ahmad

2016-01-01

Human activity recognition algorithms based on information obtained from wearable sensors are successfully applied in detecting many basic activities. Identified activities with time-stationary features are characterised inside a predefined temporal window by using different machine learning algorithms on extracted features from the measured data. Better accuracy, precision and recall levels could be achieved by combining the information from different sensors. However, detecting short and sporadic human movements, gestures and actions is still a challenging task. In this paper, a novel algorithm to detect human basic movements from wearable measured data is proposed and evaluated. The proposed algorithm is designed to minimise computational requirements while achieving acceptable accuracy levels based on characterising some particular points in the temporal series obtained from a single sensor. The underlying idea is that this algorithm would be implemented in the sensor device in order to pre-process the sensed data stream before sending the information to a central point combining the information from different sensors to improve accuracy levels. Intra- and inter-person validation is used for two particular cases: single step detection and fall detection and classification using a single tri-axial accelerometer. Relevant results for the above cases and pertinent conclusions are also presented. PMID:27618063

Improved dense trajectories for action recognition based on random projection and Fisher vectors

NASA Astrophysics Data System (ADS)

Ai, Shihui; Lu, Tongwei; Xiong, Yudian

2018-03-01

As an important application of intelligent monitoring system, the action recognition in video has become a very important research area of computer vision. In order to improve the accuracy rate of the action recognition in video with improved dense trajectories, one advanced vector method is introduced. Improved dense trajectories combine Fisher Vector with Random Projection. The method realizes the reduction of the characteristic trajectory though projecting the high-dimensional trajectory descriptor into the low-dimensional subspace based on defining and analyzing Gaussian mixture model by Random Projection. And a GMM-FV hybrid model is introduced to encode the trajectory feature vector and reduce dimension. The computational complexity is reduced by Random Projection which can drop Fisher coding vector. Finally, a Linear SVM is used to classifier to predict labels. We tested the algorithm in UCF101 dataset and KTH dataset. Compared with existed some others algorithm, the result showed that the method not only reduce the computational complexity but also improved the accuracy of action recognition.

Recognition and localization of relevant human behavior in videos

NASA Astrophysics Data System (ADS)

Bouma, Henri; Burghouts, Gertjan; de Penning, Leo; Hanckmann, Patrick; ten Hove, Johan-Martijn; Korzec, Sanne; Kruithof, Maarten; Landsmeer, Sander; van Leeuwen, Coen; van den Broek, Sebastiaan; Halma, Arvid; den Hollander, Richard; Schutte, Klamer

2013-06-01

Ground surveillance is normally performed by human assets, since it requires visual intelligence. However, especially for military operations, this can be dangerous and is very resource intensive. Therefore, unmanned autonomous visualintelligence systems are desired. In this paper, we present an improved system that can recognize actions of a human and interactions between multiple humans. Central to the new system is our agent-based architecture. The system is trained on thousands of videos and evaluated on realistic persistent surveillance data in the DARPA Mind's Eye program, with hours of videos of challenging scenes. The results show that our system is able to track the people, detect and localize events, and discriminate between different behaviors, and it performs 3.4 times better than our previous system.

Real-Time Biologically Inspired Action Recognition from Key Poses Using a Neuromorphic Architecture.

PubMed

Layher, Georg; Brosch, Tobias; Neumann, Heiko

2017-01-01

Intelligent agents, such as robots, have to serve a multitude of autonomous functions. Examples are, e.g., collision avoidance, navigation and route planning, active sensing of its environment, or the interaction and non-verbal communication with people in the extended reach space. Here, we focus on the recognition of the action of a human agent based on a biologically inspired visual architecture of analyzing articulated movements. The proposed processing architecture builds upon coarsely segregated streams of sensory processing along different pathways which separately process form and motion information (Layher et al., 2014). Action recognition is performed in an event-based scheme by identifying representations of characteristic pose configurations (key poses) in an image sequence. In line with perceptual studies, key poses are selected unsupervised utilizing a feature-driven criterion which combines extrema in the motion energy with the horizontal and the vertical extendedness of a body shape. Per class representations of key pose frames are learned using a deep convolutional neural network consisting of 15 convolutional layers. The network is trained using the energy-efficient deep neuromorphic networks ( Eedn ) framework (Esser et al., 2016), which realizes the mapping of the trained synaptic weights onto the IBM Neurosynaptic System platform (Merolla et al., 2014). After the mapping, the trained network achieves real-time capabilities for processing input streams and classify input images at about 1,000 frames per second while the computational stages only consume about 70 mW of energy (without spike transduction). Particularly regarding mobile robotic systems, a low energy profile might be crucial in a variety of application scenarios. Cross-validation results are reported for two different datasets and compared to state-of-the-art action recognition approaches. The results demonstrate, that (I) the presented approach is on par with other key pose based methods described in the literature, which select key pose frames by optimizing classification accuracy, (II) compared to the training on the full set of frames, representations trained on key pose frames result in a higher confidence in class assignments, and (III) key pose representations show promising generalization capabilities in a cross-dataset evaluation.

Real-Time Biologically Inspired Action Recognition from Key Poses Using a Neuromorphic Architecture

PubMed Central

Layher, Georg; Brosch, Tobias; Neumann, Heiko

2017-01-01

Intelligent agents, such as robots, have to serve a multitude of autonomous functions. Examples are, e.g., collision avoidance, navigation and route planning, active sensing of its environment, or the interaction and non-verbal communication with people in the extended reach space. Here, we focus on the recognition of the action of a human agent based on a biologically inspired visual architecture of analyzing articulated movements. The proposed processing architecture builds upon coarsely segregated streams of sensory processing along different pathways which separately process form and motion information (Layher et al., 2014). Action recognition is performed in an event-based scheme by identifying representations of characteristic pose configurations (key poses) in an image sequence. In line with perceptual studies, key poses are selected unsupervised utilizing a feature-driven criterion which combines extrema in the motion energy with the horizontal and the vertical extendedness of a body shape. Per class representations of key pose frames are learned using a deep convolutional neural network consisting of 15 convolutional layers. The network is trained using the energy-efficient deep neuromorphic networks (Eedn) framework (Esser et al., 2016), which realizes the mapping of the trained synaptic weights onto the IBM Neurosynaptic System platform (Merolla et al., 2014). After the mapping, the trained network achieves real-time capabilities for processing input streams and classify input images at about 1,000 frames per second while the computational stages only consume about 70 mW of energy (without spike transduction). Particularly regarding mobile robotic systems, a low energy profile might be crucial in a variety of application scenarios. Cross-validation results are reported for two different datasets and compared to state-of-the-art action recognition approaches. The results demonstrate, that (I) the presented approach is on par with other key pose based methods described in the literature, which select key pose frames by optimizing classification accuracy, (II) compared to the training on the full set of frames, representations trained on key pose frames result in a higher confidence in class assignments, and (III) key pose representations show promising generalization capabilities in a cross-dataset evaluation. PMID:28381998

Amniotic Fluid or Its Fatty Acids Produce Actions Similar to Diazepam on Lateral Septal Neurons Firing Rate

PubMed Central

Gutiérrez-García, Ana G.; Vásquez-Hernández, Diana Idania

2013-01-01

Human amniotic fluid (AF) contains eight fatty acids (FATs), and both produce anxiolytic-like effects in adult rats and appetitive responses in human newborns. The medial amygdala and lateral septal nucleus function are related to social behavior, but the action of AF or its FATs in this circuit is known. We obtained 267 single-unit extracellular recordings in Wistar rats treated with vehicle (1 mL, s.c.; n = 12), human AF (1 mL, s.c.; n = 12), a FAT mixture (1 mL, s.c.; n = 13), diazepam (1 mg/kg, i.p.; n = 11), and fluoxetine (1 mg/kg, p.o.; n = 12). Compared with the vehicle group, the spontaneous septal firing rate in the AF, FAT mixture, and diazepam groups was the lowest and in the fluoxetine group the highest. Cumulative peristimulus histograms indicated that the significant change in septal firing occurred only in the AF and FAT mixture groups and exclusively in those neurons that increased their firing rate during amygdala stimulation. We conclude that human AF and its FATs produce actions comparable to anxiolytic drugs and are able to modify the responsivity of a circuit involved in social behavior, suggesting facilitation of social recognition processes by maternal-fetal fluids. PMID:23864826

The mirror mechanism and mu rhythm in social development.

PubMed

Vanderwert, Ross E; Fox, Nathan A; Ferrari, Pier F

2013-04-12

Since the discovery of mirror neurons (MNs) in the monkey there has been a renewed interest in motor theories of cognitive and social development in humans by providing a potential neural mechanism underlying an action observation/execution matching system. It has been proposed that this system plays a fundamental role in the development of complex social and cognitive behaviors such as imitation and action recognition. In this review we discuss what is known about MNs from the work using single-cell recordings in the adult monkey, the evidence for the putative MN system in humans, and the extent to which research using electroencephalography (EEG) methods has contributed to our understanding of the development of these motor systems and their role in the social behaviors postulated by the MN hypothesis. We conclude with directions for future research that will improve our understanding of the putative human MN system and the functional role of MNs in social development. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Comprehension of iconic gestures by chimpanzees and human children.

PubMed

Bohn, Manuel; Call, Josep; Tomasello, Michael

2016-02-01

Iconic gestures-communicative acts using hand or body movements that resemble their referent-figure prominently in theories of language evolution and development. This study contrasted the abilities of chimpanzees (N=11) and 4-year-old human children (N=24) to comprehend novel iconic gestures. Participants learned to retrieve rewards from apparatuses in two distinct locations, each requiring a different action. In the test, a human adult informed the participant where to go by miming the action needed to obtain the reward. Children used the iconic gestures (more than arbitrary gestures) to locate the reward, whereas chimpanzees did not. Some children also used arbitrary gestures in the same way, but only after they had previously shown comprehension for iconic gestures. Over time, chimpanzees learned to associate iconic gestures with the appropriate location faster than arbitrary gestures, suggesting at least some recognition of the iconicity involved. These results demonstrate the importance of iconicity in referential communication. Copyright © 2015 Elsevier Inc. All rights reserved.

The mirror mechanism in the parietal lobe.

PubMed

Rizzolatti, Giacomo; Rozzi, Stefano

2018-01-01

The mirror mechanism is a basic mechanism that transforms sensory representations of others' actions into motor representations of the same actions in the brain of the observer. The mirror mechanism plays an important role in understanding actions of others. In the present chapter we discuss first the basic organization of the posterior parietal lobe in the monkey, stressing that it is best characterized as a motor scaffold, on the top of which sensory information is organized. We then describe the location of the mirror mechanism in the posterior parietal cortex of the monkey, and its functional role in areas PFG, and anterior, ventral, and lateral intraparietal areas. We will then present evidence that a similar functional organization is present in humans. We will conclude by discussing the role of the mirror mechanism in the recognition of action performed with tools. Copyright © 2018 Elsevier B.V. All rights reserved.

Bayesian Action–Perception Computational Model: Interaction of Production and Recognition of Cursive Letters

PubMed Central

Gilet, Estelle; Diard, Julien; Bessière, Pierre

2011-01-01

In this paper, we study the collaboration of perception and action representations involved in cursive letter recognition and production. We propose a mathematical formulation for the whole perception–action loop, based on probabilistic modeling and Bayesian inference, which we call the Bayesian Action–Perception (BAP) model. Being a model of both perception and action processes, the purpose of this model is to study the interaction of these processes. More precisely, the model includes a feedback loop from motor production, which implements an internal simulation of movement. Motor knowledge can therefore be involved during perception tasks. In this paper, we formally define the BAP model and show how it solves the following six varied cognitive tasks using Bayesian inference: i) letter recognition (purely sensory), ii) writer recognition, iii) letter production (with different effectors), iv) copying of trajectories, v) copying of letters, and vi) letter recognition (with internal simulation of movements). We present computer simulations of each of these cognitive tasks, and discuss experimental predictions and theoretical developments. PMID:21674043

Recognition memory, self-other source memory, and theory-of-mind in children with autism spectrum disorder.

PubMed

Lind, Sophie E; Bowler, Dermot M

2009-09-01

This study investigated semantic and episodic memory in autism spectrum disorder (ASD), using a task which assessed recognition and self-other source memory. Children with ASD showed undiminished recognition memory but significantly diminished source memory, relative to age- and verbal ability-matched comparison children. Both children with and without ASD showed an "enactment effect", demonstrating significantly better recognition and source memory for self-performed actions than other-person-performed actions. Within the comparison group, theory-of-mind (ToM) task performance was significantly correlated with source memory, specifically for other-person-performed actions (after statistically controlling for verbal ability). Within the ASD group, ToM task performance was not significantly correlated with source memory (after controlling for verbal ability). Possible explanations for these relations between source memory and ToM are considered.

Evasion of adaptive immunity by HIV through the action of host APOBEC3G/F enzymes.

PubMed

Grant, Michael; Larijani, Mani

2017-09-12

APOBEC3G (A3G) and APOBEC3F (A3F) are DNA-mutating enzymes expressed in T cells, dendritic cells and macrophages. A3G/F have been considered innate immune host factors, based on reports that they lethally mutate the HIV genome in vitro. In vivo, A3G/F effectiveness is limited by viral proteins, entrapment in inactive complexes and filtration of mutations during viral life cycle. We hypothesized that the impact of sub-lethal A3G/F action could extend beyond the realm of innate immunity confined to the cytoplasm of infected cells. We measured recognition of wild type and A3G/F-mutated epitopes by cytotoxic T lymphocytes (CTL) from HIV-infected individuals and found that A3G/F-induced mutations overwhelmingly diminished CTL recognition of HIV peptides, in a human histocompatibility-linked leukocyte antigen (HLA)-dependent manner. Furthermore, we found corresponding enrichment of A3G/F-favored motifs in CTL epitope-encoding sequences within the HIV genome. These findings illustrate that A3G/F-mediated mutations mediate immune evasion by HIV in vivo. Therefore, we suggest that vaccine strategies target T cell or antibody epitopes that are not poised for mutation into escape variants by A3G/F action.

A Spatiotemporal Profile of In Vivo Cerebral Blood Flow Changes Following Intranasal Oxytocin in Humans.

PubMed

Paloyelis, Yannis; Doyle, Orla M; Zelaya, Fernando O; Maltezos, Stefanos; Williams, Steven C; Fotopoulou, Aikaterini; Howard, Matthew A

2016-04-15

Animal and human studies highlight the role of oxytocin in social cognition and behavior and the potential of intranasal oxytocin (IN-OT) to treat social impairment in individuals with neuropsychiatric disorders such as autism. However, extensive efforts to evaluate the central actions and therapeutic efficacy of IN-OT may be marred by the absence of data regarding its temporal dynamics and sites of action in the living human brain. In a placebo-controlled study, we used arterial spin labeling to measure IN-OT-induced changes in resting regional cerebral blood flow (rCBF) in 32 healthy men. Volunteers were blinded regarding the nature of the compound they received. The rCBF data were acquired 15 min before and up to 78 min after onset of treatment onset (40 IU of IN-OT or placebo). The data were analyzed using mass univariate and multivariate pattern recognition techniques. We obtained robust evidence delineating an oxytocinergic network comprising regions expected to express oxytocin receptors, based on histologic evidence, and including core regions of the brain circuitry underpinning social cognition and emotion processing. Pattern recognition on rCBF maps indicated that IN-OT-induced changes were sustained over the entire posttreatment observation interval (25-78 min) and consistent with a pharmacodynamic profile showing a peak response at 39-51 min. Our study provides the first visualization and quantification of IN-OT-induced changes in rCBF in the living human brain unaffected by cognitive, affective, or social manipulations. Our findings can inform theoretical and mechanistic models regarding IN-OT effects on typical and atypical social behavior and guide future experiments (e.g., regarding the timing of experimental manipulations). Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Predictive Coding Strategies for Invariant Object Recognition and Volitional Motion Control in Neuromorphic Agents

DTIC Science & Technology

2015-09-02

human behavior. In this project, we hypothesized that visual memory of past motion trajectories may be used for selecting future behavior. In other...34Decoding sequence of actions using fMRI ", Society for Neuroscience Annual Meeting, San Diego, CA, USA, Nov 9-13 2013 (only abstract) 3. Hansol Choi, Dae...Shik Kim, "Planning as inference in a Hierarchical Predictive Memory ", Proceedings of International Conference on Neural Information Processing

The neural correlates of ‘vitality form’ recognition: an fMRI study

PubMed Central

Di Cesare, Giuseppe; Di Dio, Cinzia; Rochat, Magali J.; Sinigaglia, Corrado; Bruschweiler-Stern, Nadia; Stern, Daniel N.

2014-01-01

The observation of goal-directed actions performed by another individual allows one to understand what that individual is doing and why he/she is doing it. Important information about others’ behaviour is also carried out by the dynamics of the observed action. Action dynamics characterize the ‘vitality form’ of an action describing the cognitive and affective relation between the performing agent and the action recipient. Here, using the fMRI technique, we assessed the neural correlates of vitality form recognition presenting participants with videos showing two actors executing actions with different vitality forms: energetic and gentle. The participants viewed the actions in two tasks. In one task (what), they had to focus on the goal of the presented action; in the other task (how), they had to focus on the vitality form. For both tasks, activations were found in the action observation/execution circuit. Most interestingly, the contrast how vs what revealed activation in right dorso-central insula, highlighting the involvement, in the recognition of vitality form, of an anatomical region connecting somatosensory areas with the medial temporal region and, in particular, with the hippocampus. This somatosensory-insular-limbic circuit could underlie the observers’ capacity to understand the vitality forms conveyed by the observed action. PMID:23740868

Self-recognition of avatar motion: how do I know it's me?

PubMed

Cook, Richard; Johnston, Alan; Heyes, Cecilia

2012-02-22

When motion is isolated from form cues and viewed from third-person perspectives, individuals are able to recognize their own whole body movements better than those of friends. Because we rarely see our own bodies in motion from third-person viewpoints, this self-recognition advantage may indicate a contribution to perception from the motor system. Our first experiment provides evidence that recognition of self-produced and friends' motion dissociate, with only the latter showing sensitivity to orientation. Through the use of selectively disrupted avatar motion, our second experiment shows that self-recognition of facial motion is mediated by knowledge of the local temporal characteristics of one's own actions. Specifically, inverted self-recognition was unaffected by disruption of feature configurations and trajectories, but eliminated by temporal distortion. While actors lack third-person visual experience of their actions, they have a lifetime of proprioceptive, somatosensory, vestibular and first-person-visual experience. These sources of contingent feedback may provide actors with knowledge about the temporal properties of their actions, potentially supporting recognition of characteristic rhythmic variation when viewing self-produced motion. In contrast, the ability to recognize the motion signatures of familiar others may be dependent on configural topographic cues.

A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks.

PubMed

Ponce, Hiram; Martínez-Villaseñor, María de Lourdes; Miralles-Pechuán, Luis

2016-07-05

Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.

Uniform Local Binary Pattern Based Texture-Edge Feature for 3D Human Behavior Recognition.

PubMed

Ming, Yue; Wang, Guangchao; Fan, Chunxiao

2015-01-01

With the rapid development of 3D somatosensory technology, human behavior recognition has become an important research field. Human behavior feature analysis has evolved from traditional 2D features to 3D features. In order to improve the performance of human activity recognition, a human behavior recognition method is proposed, which is based on a hybrid texture-edge local pattern coding feature extraction and integration of RGB and depth videos information. The paper mainly focuses on background subtraction on RGB and depth video sequences of behaviors, extracting and integrating historical images of the behavior outlines, feature extraction and classification. The new method of 3D human behavior recognition has achieved the rapid and efficient recognition of behavior videos. A large number of experiments show that the proposed method has faster speed and higher recognition rate. The recognition method has good robustness for different environmental colors, lightings and other factors. Meanwhile, the feature of mixed texture-edge uniform local binary pattern can be used in most 3D behavior recognition.

Lifelong learning of human actions with deep neural network self-organization.

PubMed

Parisi, German I; Tani, Jun; Weber, Cornelius; Wermter, Stefan

2017-12-01

Lifelong learning is fundamental in autonomous robotics for the acquisition and fine-tuning of knowledge through experience. However, conventional deep neural models for action recognition from videos do not account for lifelong learning but rather learn a batch of training data with a predefined number of action classes and samples. Thus, there is the need to develop learning systems with the ability to incrementally process available perceptual cues and to adapt their responses over time. We propose a self-organizing neural architecture for incrementally learning to classify human actions from video sequences. The architecture comprises growing self-organizing networks equipped with recurrent neurons for processing time-varying patterns. We use a set of hierarchically arranged recurrent networks for the unsupervised learning of action representations with increasingly large spatiotemporal receptive fields. Lifelong learning is achieved in terms of prediction-driven neural dynamics in which the growth and the adaptation of the recurrent networks are driven by their capability to reconstruct temporally ordered input sequences. Experimental results on a classification task using two action benchmark datasets show that our model is competitive with state-of-the-art methods for batch learning also when a significant number of sample labels are missing or corrupted during training sessions. Additional experiments show the ability of our model to adapt to non-stationary input avoiding catastrophic interference. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

What happens to the motor theory of perception when the motor system is damaged?

PubMed

Stasenko, Alena; Garcea, Frank E; Mahon, Bradford Z

2013-09-01

Motor theories of perception posit that motor information is necessary for successful recognition of actions. Perhaps the most well known of this class of proposals is the motor theory of speech perception, which argues that speech recognition is fundamentally a process of identifying the articulatory gestures (i.e. motor representations) that were used to produce the speech signal. Here we review neuropsychological evidence from patients with damage to the motor system, in the context of motor theories of perception applied to both manual actions and speech. Motor theories of perception predict that patients with motor impairments will have impairments for action recognition. Contrary to that prediction, the available neuropsychological evidence indicates that recognition can be spared despite profound impairments to production. These data falsify strong forms of the motor theory of perception, and frame new questions about the dynamical interactions that govern how information is exchanged between input and output systems.

What happens to the motor theory of perception when the motor system is damaged?

PubMed Central

Stasenko, Alena; Garcea, Frank E.; Mahon, Bradford Z.

2016-01-01

Motor theories of perception posit that motor information is necessary for successful recognition of actions. Perhaps the most well known of this class of proposals is the motor theory of speech perception, which argues that speech recognition is fundamentally a process of identifying the articulatory gestures (i.e. motor representations) that were used to produce the speech signal. Here we review neuropsychological evidence from patients with damage to the motor system, in the context of motor theories of perception applied to both manual actions and speech. Motor theories of perception predict that patients with motor impairments will have impairments for action recognition. Contrary to that prediction, the available neuropsychological evidence indicates that recognition can be spared despite profound impairments to production. These data falsify strong forms of the motor theory of perception, and frame new questions about the dynamical interactions that govern how information is exchanged between input and output systems. PMID:26823687

Activity Recognition in Egocentric video using SVM, kNN and Combined SVMkNN Classifiers

NASA Astrophysics Data System (ADS)

Sanal Kumar, K. P.; Bhavani, R., Dr.

2017-08-01

Egocentric vision is a unique perspective in computer vision which is human centric. The recognition of egocentric actions is a challenging task which helps in assisting elderly people, disabled patients and so on. In this work, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. Here, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Motion Boundary Histogram (MBH) and Trajectory. The features are fused together and it acts as a single feature. The extracted features are reduced using Principal Component Analysis (PCA). The features that are reduced are provided as input to the classifiers like Support Vector Machine (SVM), k nearest neighbor (kNN) and combined Support Vector Machine (SVM) and k Nearest Neighbor (kNN) (combined SVMkNN). These classifiers are evaluated and the combined SVMkNN provided better results than other classifiers in the literature.

Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition.

PubMed

Yuan, Chunfeng; Li, Xi; Hu, Weiming; Ling, Haibin; Maybank, Stephen J

2014-02-01

In this paper, we present a new geometric-temporal representation for visual action recognition based on local spatio-temporal features. First, we propose a modified covariance descriptor under the log-Euclidean Riemannian metric to represent the spatio-temporal cuboids detected in the video sequences. Compared with previously proposed covariance descriptors, our descriptor can be measured and clustered in Euclidian space. Second, to capture the geometric-temporal contextual information, we construct a directional pyramid co-occurrence matrix (DPCM) to describe the spatio-temporal distribution of the vector-quantized local feature descriptors extracted from a video. DPCM characterizes the co-occurrence statistics of local features as well as the spatio-temporal positional relationships among the concurrent features. These statistics provide strong descriptive power for action recognition. To use DPCM for action recognition, we propose a directional pyramid co-occurrence matching kernel to measure the similarity of videos. The proposed method achieves the state-of-the-art performance and improves on the recognition performance of the bag-of-visual-words (BOVWs) models by a large margin on six public data sets. For example, on the KTH data set, it achieves 98.78% accuracy while the BOVW approach only achieves 88.06%. On both Weizmann and UCF CIL data sets, the highest possible accuracy of 100% is achieved.

A Specific Role for Efferent Information in Self-Recognition

ERIC Educational Resources Information Center

Tsakiris, M.; Haggard, P.; Franck, N.; Mainy, N.; Sirigu, A.

2005-01-01

We investigated the specific contribution of efferent information in a self-recognition task. Subjects experienced a passive extension of the right index finger, either as an effect of moving their left hand via a lever ('self-generated action'), or imposed externally by the experimenter ('externally-generated action'). The visual feedback was…

A Fast Goal Recognition Technique Based on Interaction Estimates

NASA Technical Reports Server (NTRS)

E-Martin, Yolanda; R-Moreno, Maria D.; Smith, David E.

2015-01-01

Goal Recognition is the task of inferring an actor's goals given some or all of the actor's observed actions. There is considerable interest in Goal Recognition for use in intelligent personal assistants, smart environments, intelligent tutoring systems, and monitoring user's needs. In much of this work, the actor's observed actions are compared against a generated library of plans. Recent work by Ramirez and Geffner makes use of AI planning to determine how closely a sequence of observed actions matches plans for each possible goal. For each goal, this is done by comparing the cost of a plan for that goal with the cost of a plan for that goal that includes the observed actions. This approach yields useful rankings, but is impractical for real-time goal recognition in large domains because of the computational expense of constructing plans for each possible goal. In this paper, we introduce an approach that propagates cost and interaction information in a plan graph, and uses this information to estimate goal probabilities. We show that this approach is much faster, but still yields high quality results.

Deep Learning for Computer Vision: A Brief Review

PubMed Central

Doulamis, Nikolaos; Doulamis, Anastasios; Protopapadakis, Eftychios

2018-01-01

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein. PMID:29487619

Opportunities for Launch Site Integrated System Health Engineering and Management

NASA Technical Reports Server (NTRS)

Waterman, Robert D.; Langwost, Patricia E.; Waterman, Susan J.

2005-01-01

The launch site processing flow involves operations such as functional verification, preflight servicing and launch. These operations often include hazards that must be controlled to protect human life and critical space hardware assets. Existing command and control capabilities are limited to simple limit checking durig automated monitoring. Contingency actions are highly dependent on human recognition, decision making, and execution. Many opportunities for Integrated System Health Engineering and Management (ISHEM) exist throughout the processing flow. This paper will present the current human-centered approach to health management as performed today for the shuttle and space station programs. In addition, it will address some of the more critical ISHEM needs, and provide recommendations for future implementation of ISHEM at the launch site.

Interplay of oxytocin, vasopressin, and sex hormones in the regulation of social recognition.

PubMed

Gabor, Christopher S; Phan, Anna; Clipperton-Allen, Amy E; Kavaliers, Martin; Choleris, Elena

2012-02-01

Social Recognition is a fundamental skill that forms the basis of behaviors essential to the proper functioning of pair or group living in most social species. We review here various neurobiological and genetic studies that point to an interplay of oxytocin (OT), arginine-vasopressin (AVP), and the gonadal hormones, estrogens and testosterone, in the mediation of social recognition. Results of a number of studies have shown that OT and its actions at the medial amygdala seem to be essential for social recognition in both sexes. Estrogens facilitate social recognition, possibly by regulating OT production in the hypothalamus and the OT receptors at the medial amygdala. Estrogens also affect social recognition on a rapid time scale, likely through nongenomic actions. The mechanisms of these rapid effects are currently unknown but available evidence points at the hippocampus as the possible site of action. Male rodents seem to be more dependent on AVP acting at the level of the lateral septum for social recognition than female rodents. Results of various studies suggest that testosterone and its metabolites (including estradiol) influence social recognition in males primarily through the AVP V1a receptor. Overall, it appears that gonadal hormone modulation of OT and AVP regulates and fine tunes social recognition and those behaviors that depend upon it (e.g., social bonds, social hierarchies) in a sex specific manner. This points at an important role for these neuroendocrine systems in the regulation of the sex differences that are evident in social behavior and of sociality as a whole.

Neurofeedback Training for BCI Control

NASA Astrophysics Data System (ADS)

Neuper, Christa; Pfurtscheller, Gert

Brain-computer interface (BCI) systems detect changes in brain signals that reflect human intention, then translate these signals to control monitors or external devices (for a comprehensive review, see [1]). BCIs typically measure electrical signals resulting from neural firing (i.e. neuronal action potentials, Electroencephalogram (ECoG), or Electroencephalogram (EEG)). Sophisticated pattern recognition and classification algorithms convert neural activity into the required control signals. BCI research has focused heavily on developing powerful signal processing and machine learning techniques to accurately classify neural activity [2-4].

Differences between Children and Adults in the Recognition of Enjoyment Smiles

ERIC Educational Resources Information Center

Del Giudice, Marco; Colle, Livia

2007-01-01

The authors investigated the differences between 8-year-olds (n = 80) and adults (n = 80) in recognition of felt versus faked enjoyment smiles by using a newly developed picture set that is based on the Facial Action Coding System. The authors tested the effect of different facial action units (AUs) on judgments of smile authenticity. Multiple…

Partnership and Recognition in Action Research: Understanding the Practices and Practice Architectures for Participation and Change

ERIC Educational Resources Information Center

Edwards-Groves, Christine; Olin, Anette; Karlberg-Granlund, Gunilla

2016-01-01

This article is the first and introductory article of this special issue. The article gives a societist account of the principles of partnership and recognition as they are encountered and experienced in practices in action research. A societist account of practices requires a social theory for understanding practices. Therefore, the article…

Bayesian Action-Perception loop modeling: Application to trajectory generation and recognition using internal motor simulation

NASA Astrophysics Data System (ADS)

Gilet, Estelle; Diard, Julien; Palluel-Germain, Richard; Bessière, Pierre

2011-03-01

This paper is about modeling perception-action loops and, more precisely, the study of the influence of motor knowledge during perception tasks. We use the Bayesian Action-Perception (BAP) model, which deals with the sensorimotor loop involved in reading and writing cursive isolated letters and includes an internal simulation of movement loop. By using this probabilistic model we simulate letter recognition, both with and without internal motor simulation. Comparison of their performance yields an experimental prediction, which we set forth.

Retinoic acid biosynthesis catalyzed by retinal dehydrogenases relies on a rate-limiting conformational transition associated with substrate recognition

PubMed Central

Bchini, Raphaël; Vasiliou, Vasilis; Branlant, Guy; Talfournier, François; Rahuel-Clermont, Sophie

2012-01-01

Retinoic acid (RA), a metabolite of vitamin A, exerts pleiotropic effects throughout life in vertebrate organisms. Thus, RA action must be tightly regulated through the coordinated action of biosynthetic and degradating enzymes. The last step of retinoic acid biosynthesis is irreversibly catalyzed by the NAD-dependent retinal dehydrogenases (RALDH), which are members of the aldehyde dehydrogenase (ALDH) superfamily. Low intracellular retinal concentrations imply efficient substrate molecular recognition to ensure high affinity and specificity of RALDHs for retinal. This study addresses the molecular basis of retinal recognition in human ALDH1A1 (or RALDH1) and rat ALDH1A2 (or RALDH2), through the comparison of the catalytic behavior of retinal analogs and use of the fluorescence properties of retinol. We show that, in contrast to long chain unsaturated substrates, the rate-limiting step of retinal oxidation by RALDHs is associated with acylation. Use of the fluorescence resonance energy transfer upon retinol interaction with RALDHs provides evidence that retinal recognition occurs in two steps: binding into the substrate access channel, and a slower structural reorganization with a rate constant of the same magnitude as the kcat for retinal oxidation: 0.18 vs. 0.07 s−1 and 0.25 vs. 0.1 s−1 for ALDH1A1 and ALDH1A2, respectively. This suggests that the conformational transition of the RALDH-retinal complex significantly contributes to the rate-limiting step that controls the kinetics of retinal oxidation, as a prerequisite for the formation of a catalytically competent Michaelis complex. This conclusion is consistent with the general notion that structural flexibility within the active site of ALDH enzymes has been shown to be an integral component of catalysis. PMID:23220587

Cortical network differences in the sighted versus early blind for recognition of human-produced action sounds

PubMed Central

Lewis, James W.; Frum, Chris; Brefczynski-Lewis, Julie A.; Talkington, William J.; Walker, Nathan A.; Rapuano, Kristina M.; Kovach, Amanda L.

2012-01-01

Both sighted and blind individuals can readily interpret meaning behind everyday real-world sounds. In sighted listeners, we previously reported that regions along the bilateral posterior superior temporal sulci (pSTS) and middle temporal gyri (pMTG) are preferentially activated when presented with recognizable action sounds. These regions have generally been hypothesized to represent primary loci for complex motion processing, including visual biological motion processing and audio-visual integration. However, it remained unclear whether, or to what degree, life-long visual experience might impact functions related to hearing perception or memory of sound-source actions. Using functional magnetic resonance imaging (fMRI), we compared brain regions activated in congenitally blind versus sighted listeners in response to hearing a wide range of recognizable human-produced action sounds (excluding vocalizations) versus unrecognized, backward-played versions of those sounds. Here we show that recognized human action sounds commonly evoked activity in both groups along most of the left pSTS/pMTG complex, though with relatively greater activity in the right pSTS/pMTG by the blind group. These results indicate that portions of the postero-lateral temporal cortices contain domain-specific hubs for biological and/or complex motion processing independent of sensory-modality experience. Contrasting the two groups, the sighted listeners preferentially activated bilateral parietal plus medial and lateral frontal networks, while the blind listeners preferentially activated left anterior insula plus bilateral anterior calcarine and medial occipital regions, including what would otherwise have been visual-related cortex. These global-level network differences suggest that blind and sighted listeners may preferentially use different memory retrieval strategies when attempting to recognize action sounds. PMID:21305666

29 CFR 29.13 - Recognition of State Apprenticeship Agencies.

Code of Federal Regulations, 2011 CFR

2011-07-01

... its authority to grant recognition to a State Apprenticeship Agency. Recognition confers non-exclusive... carry out the functions of a Registration Agency, including: Outreach and education; registration of... the areas of non-conformity, require corrective action, and offer technical assistance. After the...

Action Research to Improve Phonological Recognition at Key Stage 1 with Reference to Pupils with Special Educational Needs

ERIC Educational Resources Information Center

Clark, Charlotte

2017-01-01

Given the focus on phonological attainment in the National Phonics Screening Check, small-scale school-based action research was undertaken to improve phonological recognition and assess the impact on progress and attainment in a sample drawn from Key Stage 1 which included pupils on the Special Educational Needs (SEN) Register. The research…

Thematic knowledge, artifact concepts, and the left posterior temporal lobe: Where action and object semantics converge

PubMed Central

Kalénine, Solène; Buxbaum, Laurel J.

2016-01-01

Converging evidence supports the existence of functionally and neuroanatomically distinct taxonomic (similarity-based; e.g., hammer-screwdriver) and thematic (event-based; e.g., hammer-nail) semantic systems. Processing of thematic relations between objects has been shown to selectively recruit the left posterior temporoparietal cortex. Similar posterior regions have been also been shown to be critical for knowledge of relationships between actions and manipulable human-made objects (artifacts). Based on the hypothesis that thematic relationships for artifacts are based, at least in part, on action relationships, we assessed the prediction that the same regions of the left posterior temporoparietal cortex would be critical for conceptual processing of artifact-related actions and thematic relations for artifacts. To test this hypothesis, we evaluated processing of taxonomic and thematic relations for artifact and natural objects as well as artifact action knowledge (gesture recognition) abilities in a large sample of 48 stroke patients with a range of lesion foci in the left hemisphere. Like control participants, patients identified thematic relations faster than taxonomic relations for artifacts, whereas they identified taxonomic relations faster than thematic relations for natural objects. Moreover, response times for identifying thematic relations for artifacts selectively predicted performance in gesture recognition. Whole brain Voxel Based Lesion-Symptom Mapping (VLSM) analyses and Region of Interest (ROI) regression analyses further demonstrated that lesions to the left posterior temporal cortex, overlapping with LTO and visual motion area hMT+, were associated both with relatively slower response times in identifying thematic relations for artifacts and poorer artifact action knowledge in patients. These findings provide novel insights into the functional role of left posterior temporal cortex in thematic knowledge, and suggest that the close association between thematic relations for artifacts and action representations may reflect their common dependence on visual motion and manipulation information. PMID:27389801

Recognizing flu-like symptoms from videos.

PubMed

Thi, Tuan Hue; Wang, Li; Ye, Ning; Zhang, Jian; Maurer-Stroh, Sebastian; Cheng, Li

2014-09-12

Vision-based surveillance and monitoring is a potential alternative for early detection of respiratory disease outbreaks in urban areas complementing molecular diagnostics and hospital and doctor visit-based alert systems. Visible actions representing typical flu-like symptoms include sneeze and cough that are associated with changing patterns of hand to head distances, among others. The technical difficulties lie in the high complexity and large variation of those actions as well as numerous similar background actions such as scratching head, cell phone use, eating, drinking and so on. In this paper, we make a first attempt at the challenging problem of recognizing flu-like symptoms from videos. Since there was no related dataset available, we created a new public health dataset for action recognition that includes two major flu-like symptom related actions (sneeze and cough) and a number of background actions. We also developed a suitable novel algorithm by introducing two types of Action Matching Kernels, where both types aim to integrate two aspects of local features, namely the space-time layout and the Bag-of-Words representations. In particular, we show that the Pyramid Match Kernel and Spatial Pyramid Matching are both special cases of our proposed kernels. Besides experimenting on standard testbed, the proposed algorithm is evaluated also on the new sneeze and cough set. Empirically, we observe that our approach achieves competitive performance compared to the state-of-the-arts, while recognition on the new public health dataset is shown to be a non-trivial task even with simple single person unobstructed view. Our sneeze and cough video dataset and newly developed action recognition algorithm is the first of its kind and aims to kick-start the field of action recognition of flu-like symptoms from videos. It will be challenging but necessary in future developments to consider more complex real-life scenario of detecting these actions simultaneously from multiple persons in possibly crowded environments.

[Comparative studies of face recognition].

PubMed

Kawai, Nobuyuki

2012-07-01

Every human being is proficient in face recognition. However, the reason for and the manner in which humans have attained such an ability remain unknown. These questions can be best answered-through comparative studies of face recognition in non-human animals. Studies in both primates and non-primates show that not only primates, but also non-primates possess the ability to extract information from their conspecifics and from human experimenters. Neural specialization for face recognition is shared with mammals in distant taxa, suggesting that face recognition evolved earlier than the emergence of mammals. A recent study indicated that a social insect, the golden paper wasp, can distinguish their conspecific faces, whereas a closely related species, which has a less complex social lifestyle with just one queen ruling a nest of underlings, did not show strong face recognition for their conspecifics. Social complexity and the need to differentiate between one another likely led humans to evolve their face recognition abilities.

Transgenerational Effects of Prenatal Bisphenol A on Social Recognition

PubMed Central

Wolstenholme, Jennifer T.; Goldsby, Jessica A.; Rissman, Emilie F.

2014-01-01

Bisphenol A (BPA) is a man-made endocrine disrupting compound used to manufacture polycarbonate plastics. It is found in plastic bottles, canned food linings, thermal receipts and other commonly used items. Over 93% of people have detectable BPA levels in their urine. Epidemiological studies report correlations between BPA levels during pregnancy and activity, anxiety, and depression in children. We fed female mice control or BPA–containing diets that produced plasma BPA concentrations similar to concentrations in humans. Females were mated and at birth, pups were fostered to control dams to limit BPA exposure to gestation in the first generation. Sibling pairs were bred to the third generation with no further BPA exposure. First (F1) and third (F3) generation juveniles were tested for social recognition and in the open field. Adult F3 mice were tested for olfactory discrimination. In both generations, BPA exposed juvenile mice displayed higher levels of investigation than controls in a social recognition task. In F3 BPA exposed mice, dishabituation to a novel female was impaired. In the open field, no differences were noted in F1 mice, while in F3, BPA lineage mice were more active than controls. No impairments were detected in F3 mice, all were able to discriminate different male urine pools and urine from water. No sex differences were found in any task. These results demonstrate that BPA exposure during gestation has long lasting, transgenerational effects on social recognition and activity in mice. These findings show that BPA exposure has transgenerational actions on behavior and have implications for human neurodevelopmental behavioral disorders. PMID:24100195

Transgenerational effects of prenatal bisphenol A on social recognition.

PubMed

Wolstenholme, Jennifer T; Goldsby, Jessica A; Rissman, Emilie F

2013-11-01

Bisphenol A (BPA) is a man-made endocrine disrupting compound used to manufacture polycarbonate plastics. It is found in plastic bottles, canned food linings, thermal receipts and other commonly used items. Over 93% of people have detectable BPA levels in their urine. Epidemiological studies report correlations between BPA levels during pregnancy and activity, anxiety, and depression in children. We fed female mice control or BPA-containing diets that produced plasma BPA concentrations similar to concentrations in humans. Females were mated and at birth, pups were fostered to control dams to limit BPA exposure to gestation in the first generation. Sibling pairs were bred to the third generation with no further BPA exposure. First (F1) and third (F3) generation juveniles were tested for social recognition and in the open field. Adult F3 mice were tested for olfactory discrimination. In both generations, BPA exposed juvenile mice displayed higher levels of investigation than controls in a social recognition task. In F3 BPA exposed mice, dishabituation to a novel female was impaired. In the open field, no differences were noted in F1 mice, while in F3, BPA lineage mice were more active than controls. No impairments were detected in F3 mice, all were able to discriminate different male urine pools and urine from water. No sex differences were found in any task. These results demonstrate that BPA exposure during gestation has long lasting, transgenerational effects on social recognition and activity in mice. These findings show that BPA exposure has transgenerational actions on behavior and have implications for human neurodevelopmental behavioral disorders. © 2013.

False recollection of the role played by an actor in an event

PubMed Central

Earles, Julie L.; Upshaw, Christin

2013-01-01

Two experiments demonstrated that eyewitnesses more frequently associate an actor with the actions of another person when those two people had appeared together in the same event, rather than in different events. This greater likelihood of binding an actor with the actions of another person from the same event was associated with high-confidence recognition judgments and “remember” responses in a remember–know task, suggesting that viewing an actor together with the actions of another person led participants to falsely recollect having seen that actor perform those actions. An analysis of age differences provided evidence that familiarity also contributed to false recognition independently of a false-recollection mechanism. In particular, older adults were more likely than young adults to falsely recognize a novel conjunction of a familiar actor and action, regardless of whether that actor and action were from the same or from different events. Older adults’ elevated rate of false recognition was associated with intermediate confidence levels, suggesting that it stemmed from increased reliance on familiarity rather than from false recollection. The implications of these results are discussed for theories of conjunction errors in memory and of unconscious transference in eyewitness testimony. PMID:23722927

Effects of level of processing but not of task enactment on recognition memory in a case of developmental amnesia.

PubMed

Gardiner, John M; Brandt, Karen R; Vargha-Khadem, Faraneh; Baddeley, Alan; Mishkin, Mortimer

2006-09-01

We report the performance in four recognition memory experiments of Jon, a young adult with early-onset developmental amnesia whose episodic memory is gravely impaired in tests of recall, but seems relatively preserved in tests of recognition, and who has developed normal levels of performance in tests of intelligence and general knowledge. Jon's recognition performance was enhanced by deeper levels of processing in comparing a more meaningful study task with a less meaningful one, but not by task enactment in comparing performance of an action with reading an action phrase. Both of these variables normally enhance episodic remembering, which Jon claimed to experience. But Jon was unable to support that claim by recollecting what it was that he remembered. Taken altogether, the findings strongly imply that Jon's recognition performance entailed little genuine episodic remembering and that the levels-of-processing effects in Jon reflected semantic, not episodic, memory.

Electro-optical seasonal weather and gender data collection

NASA Astrophysics Data System (ADS)

McCoppin, Ryan; Koester, Nathan; Rude, Howard N.; Rizki, Mateen; Tamburino, Louis; Freeman, Andrew; Mendoza-Schrock, Olga

2013-05-01

This paper describes the process used to collect the Seasonal Weather And Gender (SWAG) dataset; an electro-optical dataset of human subjects that can be used to develop advanced gender classification algorithms. Several novel features characterize this ongoing effort (1) the human subjects self-label their gender by performing a specific action during the data collection and (2) the data collection will span months and even years resulting in a dataset containing realistic levels and types of clothing corresponding to the various seasons and weather conditions. It is envisioned that this type of data will support the development and evaluation of more robust gender classification systems that are capable of accurate gender recognition under extended operating conditions.

The neural correlates of 'vitality form' recognition: an fMRI study: this work is dedicated to Daniel Stern, whose immeasurable contribution to science has inspired our research.

PubMed

Di Cesare, Giuseppe; Di Dio, Cinzia; Rochat, Magali J; Sinigaglia, Corrado; Bruschweiler-Stern, Nadia; Stern, Daniel N; Rizzolatti, Giacomo

2014-07-01

The observation of goal-directed actions performed by another individual allows one to understand what that individual is doing and why he/she is doing it. Important information about others' behaviour is also carried out by the dynamics of the observed action. Action dynamics characterize the 'vitality form' of an action describing the cognitive and affective relation between the performing agent and the action recipient. Here, using the fMRI technique, we assessed the neural correlates of vitality form recognition presenting participants with videos showing two actors executing actions with different vitality forms: energetic and gentle. The participants viewed the actions in two tasks. In one task (what), they had to focus on the goal of the presented action; in the other task (how), they had to focus on the vitality form. For both tasks, activations were found in the action observation/execution circuit. Most interestingly, the contrast how vs what revealed activation in right dorso-central insula, highlighting the involvement, in the recognition of vitality form, of an anatomical region connecting somatosensory areas with the medial temporal region and, in particular, with the hippocampus. This somatosensory-insular-limbic circuit could underlie the observers' capacity to understand the vitality forms conveyed by the observed action. © The Author (2013). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Continuous Timescale Long-Short Term Memory Neural Network for Human Intent Understanding

PubMed Central

Yu, Zhibin; Moirangthem, Dennis S.; Lee, Minho

2017-01-01

Understanding of human intention by observing a series of human actions has been a challenging task. In order to do so, we need to analyze longer sequences of human actions related with intentions and extract the context from the dynamic features. The multiple timescales recurrent neural network (MTRNN) model, which is believed to be a kind of solution, is a useful tool for recording and regenerating a continuous signal for dynamic tasks. However, the conventional MTRNN suffers from the vanishing gradient problem which renders it impossible to be used for longer sequence understanding. To address this problem, we propose a new model named Continuous Timescale Long-Short Term Memory (CTLSTM) in which we inherit the multiple timescales concept into the Long-Short Term Memory (LSTM) recurrent neural network (RNN) that addresses the vanishing gradient problem. We design an additional recurrent connection in the LSTM cell outputs to produce a time-delay in order to capture the slow context. Our experiments show that the proposed model exhibits better context modeling ability and captures the dynamic features on multiple large dataset classification tasks. The results illustrate that the multiple timescales concept enhances the ability of our model to handle longer sequences related with human intentions and hence proving to be more suitable for complex tasks, such as intention recognition. PMID:28878646

Multimodal integration of micro-Doppler sonar and auditory signals for behavior classification with convolutional networks.

PubMed

Dura-Bernal, Salvador; Garreau, Guillaume; Georgiou, Julius; Andreou, Andreas G; Denham, Susan L; Wennekers, Thomas

2013-10-01

The ability to recognize the behavior of individuals is of great interest in the general field of safety (e.g. building security, crowd control, transport analysis, independent living for the elderly). Here we report a new real-time acoustic system for human action and behavior recognition that integrates passive audio and active micro-Doppler sonar signatures over multiple time scales. The system architecture is based on a six-layer convolutional neural network, trained and evaluated using a dataset of 10 subjects performing seven different behaviors. Probabilistic combination of system output through time for each modality separately yields 94% (passive audio) and 91% (micro-Doppler sonar) correct behavior classification; probabilistic multimodal integration increases classification performance to 98%. This study supports the efficacy of micro-Doppler sonar systems in characterizing human actions, which can then be efficiently classified using ConvNets. It also demonstrates that the integration of multiple sources of acoustic information can significantly improve the system's performance.

Handling Real-World Context Awareness, Uncertainty and Vagueness in Real-Time Human Activity Tracking and Recognition with a Fuzzy Ontology-Based Hybrid Method

PubMed Central

Díaz-Rodríguez, Natalia; Cadahía, Olmo León; Cuéllar, Manuel Pegalajar; Lilius, Johan; Calvo-Flores, Miguel Delgado

2014-01-01

Human activity recognition is a key task in ambient intelligence applications to achieve proper ambient assisted living. There has been remarkable progress in this domain, but some challenges still remain to obtain robust methods. Our goal in this work is to provide a system that allows the modeling and recognition of a set of complex activities in real life scenarios involving interaction with the environment. The proposed framework is a hybrid model that comprises two main modules: a low level sub-activity recognizer, based on data-driven methods, and a high-level activity recognizer, implemented with a fuzzy ontology to include the semantic interpretation of actions performed by users. The fuzzy ontology is fed by the sub-activities recognized by the low level data-driven component and provides fuzzy ontological reasoning to recognize both the activities and their influence in the environment with semantics. An additional benefit of the approach is the ability to handle vagueness and uncertainty in the knowledge-based module, which substantially outperforms the treatment of incomplete and/or imprecise data with respect to classic crisp ontologies. We validate these advantages with the public CAD-120 dataset (Cornell Activity Dataset), achieving an accuracy of 90.1% and 91.07% for low-level and high-level activities, respectively. This entails an improvement over fully data-driven or ontology-based approaches. PMID:25268914

Extricating Manual and Non-Manual Features for Subunit Level Medical Sign Modelling in Automatic Sign Language Classification and Recognition.

PubMed

R, Elakkiya; K, Selvamani

2017-09-22

Subunit segmenting and modelling in medical sign language is one of the important studies in linguistic-oriented and vision-based Sign Language Recognition (SLR). Many efforts were made in the precedent to focus the functional subunits from the view of linguistic syllables but the problem is implementing such subunit extraction using syllables is not feasible in real-world computer vision techniques. And also, the present recognition systems are designed in such a way that it can detect the signer dependent actions under restricted and laboratory conditions. This research paper aims at solving these two important issues (1) Subunit extraction and (2) Signer independent action on visual sign language recognition. Subunit extraction involved in the sequential and parallel breakdown of sign gestures without any prior knowledge on syllables and number of subunits. A novel Bayesian Parallel Hidden Markov Model (BPaHMM) is introduced for subunit extraction to combine the features of manual and non-manual parameters to yield better results in classification and recognition of signs. Signer independent action aims in using a single web camera for different signer behaviour patterns and for cross-signer validation. Experimental results have proved that the proposed signer independent subunit level modelling for sign language classification and recognition has shown improvement and variations when compared with other existing works.

Face Recognition in Humans and Machines

NASA Astrophysics Data System (ADS)

O'Toole, Alice; Tistarelli, Massimo

The study of human face recognition by psychologists and neuroscientists has run parallel to the development of automatic face recognition technologies by computer scientists and engineers. In both cases, there are analogous steps of data acquisition, image processing, and the formation of representations that can support the complex and diverse tasks we accomplish with faces. These processes can be understood and compared in the context of their neural and computational implementations. In this chapter, we present the essential elements of face recognition by humans and machines, taking a perspective that spans psychological, neural, and computational approaches. From the human side, we overview the methods and techniques used in the neurobiology of face recognition, the underlying neural architecture of the system, the role of visual attention, and the nature of the representations that emerges. From the computational side, we discuss face recognition technologies and the strategies they use to overcome challenges to robust operation over viewing parameters. Finally, we conclude the chapter with a look at some recent studies that compare human and machine performances at face recognition.

Monkeys recall and reproduce simple shapes from memory.

PubMed

Basile, Benjamin M; Hampton, Robert R

2011-05-10

If you draw from memory a picture of the front of your childhood home, you will have demonstrated recall. You could also recognize this house upon seeing it. Unlike recognition, recall demonstrates memory for things that are not present. Recall is necessary for planning and imagining, and it can increase the flexibility of navigation, social behavior, and other cognitive skills. Without recall, memory is more limited to recognition of the immediate environment. Amnesic patients are impaired on recall tests [1, 2], and recall performance often declines with aging [3]. Despite its importance, we know relatively little about nonhuman animals' ability to recall information; we lack suitable recall tests for them and depend instead on recognition tests to measure nonhuman memory. Here we report that rhesus monkeys can recall simple shapes from memory and reproduce them on a touchscreen. As in humans [4, 5], monkeys remembered less in recall than recognition tests, and their recall performance deteriorated more slowly. Transfer tests showed that monkeys used a flexible memory mechanism rather than memorizing specific actions for each shape. Observation of recall in Old World monkeys suggests that it has been adaptive for over 30 million years [6] and does not depend on language. Copyright © 2011 Elsevier Ltd. All rights reserved.

Participatory Action Research.

ERIC Educational Resources Information Center

Walker, Martha Lentz

1993-01-01

Describes aspects of participatory action research and considers advantages of using participatory action research in research by disabilities and rehabilitation researchers. Notes that participatory action research can be built into any rehabilitation research design but that it rests upon the recognition of persons with disabilities as integral…

Social skills training for children with autism spectrum disorder using a robotic behavioral intervention system.

PubMed

Yun, Sang-Seok; Choi, JongSuk; Park, Sung-Kee; Bong, Gui-Young; Yoo, HeeJeong

2017-07-01

We designed a robot system that assisted in behavioral intervention programs of children with autism spectrum disorder (ASD). The eight-session intervention program was based on the discrete trial teaching protocol and focused on two basic social skills: eye contact and facial emotion recognition. The robotic interactions occurred in four modules: training element query, recognition of human activity, coping-mode selection, and follow-up action. Children with ASD who were between 4 and 7 years old and who had verbal IQ ≥ 60 were recruited and randomly assigned to the treatment group (TG, n = 8, 5.75 ± 0.89 years) or control group (CG, n = 7; 6.32 ± 1.23 years). The therapeutic robot facilitated the treatment intervention in the TG, and the human assistant facilitated the treatment intervention in the CG. The intervention procedures were identical in both groups. The primary outcome measures included parent-completed questionnaires, the Autism Diagnostic Observation Schedule (ADOS), and frequency of eye contact, which was measured with the partial interval recording method. After completing treatment, the eye contact percentages were significantly increased in both groups. For facial emotion recognition, the percentages of correct answers were increased in similar patterns in both groups compared to baseline (P > 0.05), with no difference between the TG and CG (P > 0.05). The subjects' ability to play, general behavioral and emotional symptoms were significantly diminished after treatment (p < 0.05). These results showed that the robot-facilitated and human-facilitated behavioral interventions had similar positive effects on eye contact and facial emotion recognition, which suggested that robots are useful mediators of social skills training for children with ASD. Autism Res 2017,. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 1306-1323. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

A Vision-Based System for Intelligent Monitoring: Human Behaviour Analysis and Privacy by Context

PubMed Central

Chaaraoui, Alexandros Andre; Padilla-López, José Ramón; Ferrández-Pastor, Francisco Javier; Nieto-Hidalgo, Mario; Flórez-Revuelta, Francisco

2014-01-01

Due to progress and demographic change, society is facing a crucial challenge related to increased life expectancy and a higher number of people in situations of dependency. As a consequence, there exists a significant demand for support systems for personal autonomy. This article outlines the vision@home project, whose goal is to extend independent living at home for elderly and impaired people, providing care and safety services by means of vision-based monitoring. Different kinds of ambient-assisted living services are supported, from the detection of home accidents, to telecare services. In this contribution, the specification of the system is presented, and novel contributions are made regarding human behaviour analysis and privacy protection. By means of a multi-view setup of cameras, people's behaviour is recognised based on human action recognition. For this purpose, a weighted feature fusion scheme is proposed to learn from multiple views. In order to protect the right to privacy of the inhabitants when a remote connection occurs, a privacy-by-context method is proposed. The experimental results of the behaviour recognition method show an outstanding performance, as well as support for multi-view scenarios and real-time execution, which are required in order to provide the proposed services. PMID:24854209

A vision-based system for intelligent monitoring: human behaviour analysis and privacy by context.

PubMed

Chaaraoui, Alexandros Andre; Padilla-López, José Ramón; Ferrández-Pastor, Francisco Javier; Nieto-Hidalgo, Mario; Flórez-Revuelta, Francisco

2014-05-20

Due to progress and demographic change, society is facing a crucial challenge related to increased life expectancy and a higher number of people in situations of dependency. As a consequence, there exists a significant demand for support systems for personal autonomy. This article outlines the vision@home project, whose goal is to extend independent living at home for elderly and impaired people, providing care and safety services by means of vision-based monitoring. Different kinds of ambient-assisted living services are supported, from the detection of home accidents, to telecare services. In this contribution, the specification of the system is presented, and novel contributions are made regarding human behaviour analysis and privacy protection. By means of a multi-view setup of cameras, people's behaviour is recognised based on human action recognition. For this purpose, a weighted feature fusion scheme is proposed to learn from multiple views. In order to protect the right to privacy of the inhabitants when a remote connection occurs, a privacy-by-context method is proposed. The experimental results of the behaviour recognition method show an outstanding performance, as well as support for multi-view scenarios and real-time execution, which are required in order to provide the proposed services.

Divergent Human Cortical Regions for Processing Distinct Acoustic-Semantic Categories of Natural Sounds: Animal Action Sounds vs. Vocalizations

PubMed Central

Webster, Paula J.; Skipper-Kallal, Laura M.; Frum, Chris A.; Still, Hayley N.; Ward, B. Douglas; Lewis, James W.

2017-01-01

A major gap in our understanding of natural sound processing is knowledge of where or how in a cortical hierarchy differential processing leads to categorical perception at a semantic level. Here, using functional magnetic resonance imaging (fMRI) we sought to determine if and where cortical pathways in humans might diverge for processing action sounds vs. vocalizations as distinct acoustic-semantic categories of real-world sound when matched for duration and intensity. This was tested by using relatively less semantically complex natural sounds produced by non-conspecific animals rather than humans. Our results revealed a striking double-dissociation of activated networks bilaterally. This included a previously well described pathway preferential for processing vocalization signals directed laterally from functionally defined primary auditory cortices to the anterior superior temporal gyri, and a less well-described pathway preferential for processing animal action sounds directed medially to the posterior insulae. We additionally found that some of these regions and associated cortical networks showed parametric sensitivity to high-order quantifiable acoustic signal attributes and/or to perceptual features of the natural stimuli, such as the degree of perceived recognition or intentional understanding. Overall, these results supported a neurobiological theoretical framework for how the mammalian brain may be fundamentally organized to process acoustically and acoustic-semantically distinct categories of ethologically valid, real-world sounds. PMID:28111538

Representation, Classification and Information Fusion for Robust and Efficient Multimodal Human States Recognition

ERIC Educational Resources Information Center

Li, Ming

2013-01-01

The goal of this work is to enhance the robustness and efficiency of the multimodal human states recognition task. Human states recognition can be considered as a joint term for identifying/verifing various kinds of human related states, such as biometric identity, language spoken, age, gender, emotion, intoxication level, physical activity, vocal…

Hough transform for human action recognition

NASA Astrophysics Data System (ADS)

Siemon, Mia S. N.

2016-09-01

Nowadays, the demand of computer analysis, especially regarding team sports, continues drastically growing. More and more decisions are made by electronic devices for the live to become `easier' to a certain context. There already exist application areas in sports, during which critical situations are being handled by means of digital software. This paper aims at the evaluation and introduction to the necessary foundation which would make it possible to develop a concept similar to that of `hawk-eye', a decision-making program to evaluate the impact of a ball with respect to a target line and to apply it to the sport of volleyball. The pattern recognition process is in this case performed by means of the mathematical model of Hough transform which is able of identifying relevant lines and circles in the image in order to later on use them for the necessary evaluation of the image and the decision-making process.

Low-Rank Tensor Subspace Learning for RGB-D Action Recognition.

PubMed

Jia, Chengcheng; Fu, Yun

2016-07-09

Since RGB-D action data inherently equip with extra depth information compared with RGB data, recently many works employ RGB-D data in a third-order tensor representation containing spatio-temporal structure to find a subspace for action recognition. However, there are two main challenges of these methods. First, the dimension of subspace is usually fixed manually. Second, preserving local information by finding intraclass and inter-class neighbors from a manifold is highly timeconsuming. In this paper, we learn a tensor subspace, whose dimension is learned automatically by low-rank learning, for RGB-D action recognition. Particularly, the tensor samples are factorized to obtain three Projection Matrices (PMs) by Tucker Decomposition, where all the PMs are performed by nuclear norm in a close-form to obtain the tensor ranks which are used as tensor subspace dimension. Additionally, we extract the discriminant and local information from a manifold using a graph constraint. This graph preserves the local knowledge inherently, which is faster than the previous way by calculating both the intra-class and inter-class neighbors of each sample. We evaluate the proposed method on four widely used RGB-D action datasets including MSRDailyActivity3D, MSRActionPairs, MSRActionPairs skeleton and UTKinect-Action3D datasets, and the experimental results show higher accuracy and efficiency of the proposed method.

Cross-View Action Recognition via Transferable Dictionary Learning.

PubMed

Zheng, Jingjing; Jiang, Zhuolin; Chellappa, Rama

2016-05-01

Discriminative appearance features are effective for recognizing actions in a fixed view, but may not generalize well to a new view. In this paper, we present two effective approaches to learn dictionaries for robust action recognition across views. In the first approach, we learn a set of view-specific dictionaries where each dictionary corresponds to one camera view. These dictionaries are learned simultaneously from the sets of correspondence videos taken at different views with the aim of encouraging each video in the set to have the same sparse representation. In the second approach, we additionally learn a common dictionary shared by different views to model view-shared features. This approach represents the videos in each view using a view-specific dictionary and the common dictionary. More importantly, it encourages the set of videos taken from the different views of the same action to have the similar sparse representations. The learned common dictionary not only has the capability to represent actions from unseen views, but also makes our approach effective in a semi-supervised setting where no correspondence videos exist and only a few labeled videos exist in the target view. The extensive experiments using three public datasets demonstrate that the proposed approach outperforms recently developed approaches for cross-view action recognition.

Native American Mobilization and the Power of Recognition: Theorizing the Effects of Political Acknowledgment

ERIC Educational Resources Information Center

Stokes, DaShanne

2012-01-01

How recognition may empower or restrain Native American mobilization has not received sufficient scholarly attention and remains largely unexplored and under-theorized. This paper contributes a partial remedy to this oversight by explicitly theorizing how political recognition can mediate Native American collective action and lead to differential…

Good Practices for Learning to Recognize Actions Using FV and VLAD.

PubMed

Wu, Jianxin; Zhang, Yu; Lin, Weiyao

2016-12-01

High dimensional representations such as Fisher vectors (FV) and vectors of locally aggregated descriptors (VLAD) have shown state-of-the-art accuracy for action recognition in videos. The high dimensionality, on the other hand, also causes computational difficulties when scaling up to large-scale video data. This paper makes three lines of contributions to learning to recognize actions using high dimensional representations. First, we reviewed several existing techniques that improve upon FV or VLAD in image classification, and performed extensive empirical evaluations to assess their applicability for action recognition. Our analyses of these empirical results show that normality and bimodality are essential to achieve high accuracy. Second, we proposed a new pooling strategy for VLAD and three simple, efficient, and effective transformations for both FV and VLAD. Both proposed methods have shown higher accuracy than the original FV/VLAD method in extensive evaluations. Third, we proposed and evaluated new feature selection and compression methods for the FV and VLAD representations. This strategy uses only 4% of the storage of the original representation, but achieves comparable or even higher accuracy. Based on these contributions, we recommend a set of good practices for action recognition in videos for practitioners in this field.

Neural mechanisms and models underlying joint action.

PubMed

Chersi, Fabian

2011-06-01

Humans, in particular, and to a lesser extent also other species of animals, possess the impressive capability of smoothly coordinating their actions with those of others. The great amount of work done in recent years in neuroscience has provided new insights into the processes involved in joint action, intention understanding, and task sharing. In particular, the discovery of mirror neurons, which fire both when animals execute actions and when they observe the same actions done by other individuals, has shed light on the intimate relationship between perception and action elucidating the direct contribution of motor knowledge to action understanding. Up to date, however, a detailed description of the neural processes involved in these phenomena is still mostly lacking. Building upon data from single neuron recordings in monkeys observing the actions of a demonstrator and then executing the same or a complementary action, this paper describes the functioning of a biologically constraint neural network model of the motor and mirror systems during joint action. In this model, motor sequences are encoded as independent neuronal chains that represent concatenations of elementary motor acts leading to a specific goal. Action execution and recognition are achieved through the propagation of activity within specific chains. Due to the dual property of mirror neurons, the same architecture is capable of smoothly integrating and switching between observed and self-generated action sequences, thus allowing to evaluate multiple hypotheses simultaneously, understand actions done by others, and to respond in an appropriate way.

Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action Sequences

PubMed Central

Jung, Minju; Hwang, Jungsik; Tani, Jun

2015-01-01

It is well known that the visual cortex efficiently processes high-dimensional spatial information by using a hierarchical structure. Recently, computational models that were inspired by the spatial hierarchy of the visual cortex have shown remarkable performance in image recognition. Up to now, however, most biological and computational modeling studies have mainly focused on the spatial domain and do not discuss temporal domain processing of the visual cortex. Several studies on the visual cortex and other brain areas associated with motor control support that the brain also uses its hierarchical structure as a processing mechanism for temporal information. Based on the success of previous computational models using spatial hierarchy and temporal hierarchy observed in the brain, the current report introduces a novel neural network model for the recognition of dynamic visual image patterns based solely on the learning of exemplars. This model is characterized by the application of both spatial and temporal constraints on local neural activities, resulting in the self-organization of a spatio-temporal hierarchy necessary for the recognition of complex dynamic visual image patterns. The evaluation with the Weizmann dataset in recognition of a set of prototypical human movement patterns showed that the proposed model is significantly robust in recognizing dynamically occluded visual patterns compared to other baseline models. Furthermore, an evaluation test for the recognition of concatenated sequences of those prototypical movement patterns indicated that the model is endowed with a remarkable capability for the contextual recognition of long-range dynamic visual image patterns. PMID:26147887

Self-Organization of Spatio-Temporal Hierarchy via Learning of Dynamic Visual Image Patterns on Action Sequences.

PubMed

Jung, Minju; Hwang, Jungsik; Tani, Jun

2015-01-01

It is well known that the visual cortex efficiently processes high-dimensional spatial information by using a hierarchical structure. Recently, computational models that were inspired by the spatial hierarchy of the visual cortex have shown remarkable performance in image recognition. Up to now, however, most biological and computational modeling studies have mainly focused on the spatial domain and do not discuss temporal domain processing of the visual cortex. Several studies on the visual cortex and other brain areas associated with motor control support that the brain also uses its hierarchical structure as a processing mechanism for temporal information. Based on the success of previous computational models using spatial hierarchy and temporal hierarchy observed in the brain, the current report introduces a novel neural network model for the recognition of dynamic visual image patterns based solely on the learning of exemplars. This model is characterized by the application of both spatial and temporal constraints on local neural activities, resulting in the self-organization of a spatio-temporal hierarchy necessary for the recognition of complex dynamic visual image patterns. The evaluation with the Weizmann dataset in recognition of a set of prototypical human movement patterns showed that the proposed model is significantly robust in recognizing dynamically occluded visual patterns compared to other baseline models. Furthermore, an evaluation test for the recognition of concatenated sequences of those prototypical movement patterns indicated that the model is endowed with a remarkable capability for the contextual recognition of long-range dynamic visual image patterns.

76 FR 55709 - Wyle Laboratories, Inc.; Revocation of Recognition

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2011-09-08

... DEPARTMENT OF LABOR Occupational Safety and Health Administration [Docket No. OSHA-2006-0029] Wyle Laboratories, Inc.; Revocation of Recognition AGENCY: Occupational Safety and Health Administration (OSHA), Labor. ACTION: Notice. [[Page 55710

Effect of speech-intrinsic variations on human and automatic recognition of spoken phonemes.

PubMed

Meyer, Bernd T; Brand, Thomas; Kollmeier, Birger

2011-01-01

The aim of this study is to quantify the gap between the recognition performance of human listeners and an automatic speech recognition (ASR) system with special focus on intrinsic variations of speech, such as speaking rate and effort, altered pitch, and the presence of dialect and accent. Second, it is investigated if the most common ASR features contain all information required to recognize speech in noisy environments by using resynthesized ASR features in listening experiments. For the phoneme recognition task, the ASR system achieved the human performance level only when the signal-to-noise ratio (SNR) was increased by 15 dB, which is an estimate for the human-machine gap in terms of the SNR. The major part of this gap is attributed to the feature extraction stage, since human listeners achieve comparable recognition scores when the SNR difference between unaltered and resynthesized utterances is 10 dB. Intrinsic variabilities result in strong increases of error rates, both in human speech recognition (HSR) and ASR (with a relative increase of up to 120%). An analysis of phoneme duration and recognition rates indicates that human listeners are better able to identify temporal cues than the machine at low SNRs, which suggests incorporating information about the temporal dynamics of speech into ASR systems.

Vision based assistive technology for people with dementia performing activities of daily living (ADLs): an overview

NASA Astrophysics Data System (ADS)

As'ari, M. A.; Sheikh, U. U.

2012-04-01

The rapid development of intelligent assistive technology for replacing a human caregiver in assisting people with dementia performing activities of daily living (ADLs) promises in the reduction of care cost especially in training and hiring human caregiver. The main problem however, is the various kinds of sensing agents used in such system and is dependent on the intent (types of ADLs) and environment where the activity is performed. In this paper on overview of the potential of computer vision based sensing agent in assistive system and how it can be generalized and be invariant to various kind of ADLs and environment. We find that there exists a gap from the existing vision based human action recognition method in designing such system due to cognitive and physical impairment of people with dementia.

Physical environment virtualization for human activities recognition

NASA Astrophysics Data System (ADS)

Poshtkar, Azin; Elangovan, Vinayak; Shirkhodaie, Amir; Chan, Alex; Hu, Shuowen

2015-05-01

Human activity recognition research relies heavily on extensive datasets to verify and validate performance of activity recognition algorithms. However, obtaining real datasets are expensive and highly time consuming. A physics-based virtual simulation can accelerate the development of context based human activity recognition algorithms and techniques by generating relevant training and testing videos simulating diverse operational scenarios. In this paper, we discuss in detail the requisite capabilities of a virtual environment to aid as a test bed for evaluating and enhancing activity recognition algorithms. To demonstrate the numerous advantages of virtual environment development, a newly developed virtual environment simulation modeling (VESM) environment is presented here to generate calibrated multisource imagery datasets suitable for development and testing of recognition algorithms for context-based human activities. The VESM environment serves as a versatile test bed to generate a vast amount of realistic data for training and testing of sensor processing algorithms. To demonstrate the effectiveness of VESM environment, we present various simulated scenarios and processed results to infer proper semantic annotations from the high fidelity imagery data for human-vehicle activity recognition under different operational contexts.

From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems

PubMed Central

Yildiz, Izzet B.; von Kriegstein, Katharina; Kiebel, Stefan J.

2013-01-01

Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents—an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments. PMID:24068902

From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.

PubMed

Yildiz, Izzet B; von Kriegstein, Katharina; Kiebel, Stefan J

2013-01-01

Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents-an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments.

From mirror self-recognition to the looking-glass self: exploring the Justification Hypothesis.

PubMed

Shaffer, Leigh S

2005-01-01

In his Tree of Knowledge (ToK) System, Henriques (2003) posits that the human ego or "self" has evolved because human beings are the only animals that have had to justify their behavior to others. This essay provides evidence for this Justification Hypothesis (JH) from everyday life sociology, starting with the work of George Herbert Mead and Charles Horton Cooley, and focuses on research related to the concept of the "looking-glass self." Special emphasis is given to the pragmatics of speech acts, the presentation of self in interaction rituals, the accounts given by actors in justification of their actions, and the role of social norms and conformity in the large-scale justification systems commonly called "culture."

Human activity recognition based on feature selection in smart home using back-propagation algorithm.

PubMed

Fang, Hongqing; He, Lei; Si, Hao; Liu, Peng; Xie, Xiaolei

2014-09-01

In this paper, Back-propagation(BP) algorithm has been used to train the feed forward neural network for human activity recognition in smart home environments, and inter-class distance method for feature selection of observed motion sensor events is discussed and tested. And then, the human activity recognition performances of neural network using BP algorithm have been evaluated and compared with other probabilistic algorithms: Naïve Bayes(NB) classifier and Hidden Markov Model(HMM). The results show that different feature datasets yield different activity recognition accuracy. The selection of unsuitable feature datasets increases the computational complexity and degrades the activity recognition accuracy. Furthermore, neural network using BP algorithm has relatively better human activity recognition performances than NB classifier and HMM. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Muecas: A Multi-Sensor Robotic Head for Affective Human Robot Interaction and Imitation

PubMed Central

Cid, Felipe; Moreno, Jose; Bustos, Pablo; Núñez, Pedro

2014-01-01

This paper presents a multi-sensor humanoid robotic head for human robot interaction. The design of the robotic head, Muecas, is based on ongoing research on the mechanisms of perception and imitation of human expressions and emotions. These mechanisms allow direct interaction between the robot and its human companion through the different natural language modalities: speech, body language and facial expressions. The robotic head has 12 degrees of freedom, in a human-like configuration, including eyes, eyebrows, mouth and neck, and has been designed and built entirely by IADeX (Engineering, Automation and Design of Extremadura) and RoboLab. A detailed description of its kinematics is provided along with the design of the most complex controllers. Muecas can be directly controlled by FACS (Facial Action Coding System), the de facto standard for facial expression recognition and synthesis. This feature facilitates its use by third party platforms and encourages the development of imitation and of goal-based systems. Imitation systems learn from the user, while goal-based ones use planning techniques to drive the user towards a final desired state. To show the flexibility and reliability of the robotic head, the paper presents a software architecture that is able to detect, recognize, classify and generate facial expressions in real time using FACS. This system has been implemented using the robotics framework, RoboComp, which provides hardware-independent access to the sensors in the head. Finally, the paper presents experimental results showing the real-time functioning of the whole system, including recognition and imitation of human facial expressions. PMID:24787636

Effects of constructivist teaching methods on bioethics education for nursing students: a quasi-experimental study.

PubMed

Choe, Kwisoon; Park, Sunghee; Yoo, So Yeon

2014-05-01

In order to help nurses advocate for the patient's human rights and ensure respect for life in clinical situations, it is of utmost importance to improve nursing students' capacity to make ethical decisions. This study compares the effects of two constructivist teaching strategies (action learning and cross-examination debate) on nursing students' recognition of bioethical issues, experience of bioethical issues, and attainment of ethical competence. This study used a quasi-experimental (two-group pretest-posttest) design. A nursing college in South Korea. A total of 93 Korean nursing students participated in the study (46 in the action learning group and 47 in the cross-examination debate group). Participants took a bioethics class employing one or the other of the strategies mentioned, 2h a week for 15 weeks. All participants responded twice to a set of questionnaires, at the beginning of the first session and at the end of the last session. After their bioethics education, the students' recognition of bioethical issues improved for both classes; however, the knowledge of students who had participated in action learning improved more than that of the students in the debate-based class. Students in both groups reported more experience of bioethics and exposure to better-quality instruction in bioethics after their classes than previously. Students in both groups also reported improved ethical competency after this education. Positive effects of action learning and cross-examination debate implemented as teaching strategies on nursing students' understanding of bioethical issues and their ethical competency were identified; these findings will be important in the essential task of teaching bioethics to nursing students in order to foster more ethical decision-making and other ethical behavior. © 2013.

Violating instructed human agency: An fMRI study on ocular tracking of biological and nonbiological motion stimuli.

PubMed

Gertz, Hanna; Hilger, Maximilian; Hegele, Mathias; Fiehler, Katja

2016-09-01

Previous studies have shown that beliefs about the human origin of a stimulus are capable of modulating the coupling of perception and action. Such beliefs can be based on top-down recognition of the identity of an actor or bottom-up observation of the behavior of the stimulus. Instructed human agency has been shown to lead to superior tracking performance of a moving dot as compared to instructed computer agency, especially when the dot followed a biological velocity profile and thus matched the predicted movement, whereas a violation of instructed human agency by a nonbiological dot motion impaired oculomotor tracking (Zwickel et al., 2012). This suggests that the instructed agency biases the selection of predictive models on the movement trajectory of the dot motion. The aim of the present fMRI study was to examine the neural correlates of top-down and bottom-up modulations of perception-action couplings by manipulating the instructed agency (human action vs. computer-generated action) and the observable behavior of the stimulus (biological vs. nonbiological velocity profile). To this end, participants performed an oculomotor tracking task in an MRI environment. Oculomotor tracking activated areas of the eye movement network. A right-hemisphere occipito-temporal cluster comprising the motion-sensitive area V5 showed a preference for the biological as compared to the nonbiological velocity profile. Importantly, a mismatch between instructed human agency and a nonbiological velocity profile primarily activated medial-frontal areas comprising the frontal pole, the paracingulate gyrus, and the anterior cingulate gyrus, as well as the cerebellum and the supplementary eye field as part of the eye movement network. This mismatch effect was specific to the instructed human agency and did not occur in conditions with a mismatch between instructed computer agency and a biological velocity profile. Our results support the hypothesis that humans activate a specific predictive model for biological movements based on their own motor expertise. A violation of this predictive model causes costs as the movement needs to be corrected in accordance with incoming (nonbiological) sensory information. Copyright © 2016 Elsevier Inc. All rights reserved.

Using GOMS and Bayesian plan recognition to develop recognition models of operator behavior

NASA Astrophysics Data System (ADS)

Zaientz, Jack D.; DeKoven, Elyon; Piegdon, Nicholas; Wood, Scott D.; Huber, Marcus J.

2006-05-01

Trends in combat technology research point to an increasing role for uninhabited vehicles in modern warfare tactics. To support increased span of control over these vehicles human responsibilities need to be transformed from tedious, error-prone and cognition intensive operations into tasks that are more supervisory and manageable, even under intensely stressful conditions. The goal is to move away from only supporting human command of low-level system functions to intention-level human-system dialogue about the operator's tasks and situation. A critical element of this process is developing the means to identify when human operators need automated assistance and to identify what assistance they need. Toward this goal, we are developing an unmanned vehicle operator task recognition system that combines work in human behavior modeling and Bayesian plan recognition. Traditionally, human behavior models have been considered generative, meaning they describe all possible valid behaviors. Basing behavior recognition on models designed for behavior generation can offers advantages in improved model fidelity and reuse. It is not clear, however, how to reconcile the structural differences between behavior recognition and behavior modeling approaches. Our current work demonstrates that by pairing a cognitive psychology derived human behavior modeling approach, GOMS, with a Bayesian plan recognition engine, ASPRN, we can translate a behavior generation model into a recognition model. We will discuss the implications for using human performance models in this manner as well as suggest how this kind of modeling may be used to support the real-time control of multiple, uninhabited battlefield vehicles and other semi-autonomous systems.

78 FR 70327 - TÜV SÜD Product Services GmbH: Request for Renewal of Recognition

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-25

...[Uuml]V S[Uuml]D Product Services GmbH: Request for Renewal of Recognition AGENCY: Occupational Safety and Health Administration (OSHA), Labor ACTION: Notice. SUMMARY: This notice announces T[Uuml]V S[Uuml..., or denying the renewal of recognition. T[Uuml]V S[Uuml]D Product Services GmbH (TUVPSG) initially...

Recognition of tennis serve performed by a digital player: comparison among polygon, shadow, and stick-figure models.

PubMed

Ida, Hirofumi; Fukuhara, Kazunobu; Ishii, Motonobu

2012-01-01

The objective of this study was to assess the cognitive effect of human character models on the observer's ability to extract relevant information from computer graphics animation of tennis serve motions. Three digital human models (polygon, shadow, and stick-figure) were used to display the computationally simulated serve motions, which were perturbed at the racket-arm by modulating the speed (slower or faster) of one of the joint rotations (wrist, elbow, or shoulder). Twenty-one experienced tennis players and 21 novices made discrimination responses about the modulated joint and also specified the perceived swing speeds on a visual analogue scale. The result showed that the discrimination accuracies of the experienced players were both above and below chance level depending on the modulated joint whereas those of the novices mostly remained at chance or guessing levels. As far as the experienced players were concerned, the polygon model decreased the discrimination accuracy as compared with the stick-figure model. This suggests that the complicated pictorial information may have a distracting effect on the recognition of the observed action. On the other hand, the perceived swing speed of the perturbed motion relative to the control was lower for the stick-figure model than for the polygon model regardless of the skill level. This result suggests that the simplified visual information can bias the perception of the motion speed toward slower. It was also shown that the increasing the joint rotation speed increased the perceived swing speed, although the resulting racket velocity had little correlation with this speed sensation. Collectively, observer's recognition of the motion pattern and perception of the motion speed can be affected by the pictorial information of the human model as well as by the perturbation processing applied to the observed motion.

Real-time action recognition using a multilayer descriptor with variable size

NASA Astrophysics Data System (ADS)

Alcantara, Marlon F.; Moreira, Thierry P.; Pedrini, Helio

2016-01-01

Video analysis technology has become less expensive and more powerful in terms of storage resources and resolution capacity, promoting progress in a wide range of applications. Video-based human action detection has been used for several tasks in surveillance environments, such as forensic investigation, patient monitoring, medical training, accident prevention, and traffic monitoring, among others. We present a method for action identification based on adaptive training of a multilayer descriptor applied to a single classifier. Cumulative motion shapes (CMSs) are extracted according to the number of frames present in the video. Each CMS is employed as a self-sufficient layer in the training stage but belongs to the same descriptor. A robust classification is achieved through individual responses of classifiers for each layer, and the dominant result is used as a final outcome. Experiments are conducted on five public datasets (Weizmann, KTH, MuHAVi, IXMAS, and URADL) to demonstrate the effectiveness of the method in terms of accuracy in real time.

Recognition of complex human behaviours using 3D imaging for intelligent surveillance applications

NASA Astrophysics Data System (ADS)

Yao, Bo; Lepley, Jason J.; Peall, Robert; Butler, Michael; Hagras, Hani

2016-10-01

We introduce a system that exploits 3-D imaging technology as an enabler for the robust recognition of the human form. We combine this with pose and feature recognition capabilities from which we can recognise high-level human behaviours. We propose a hierarchical methodology for the recognition of complex human behaviours, based on the identification of a set of atomic behaviours, individual and sequential poses (e.g. standing, sitting, walking, drinking and eating) that provides a framework from which we adopt time-based machine learning techniques to recognise complex behaviour patterns.

Human-assisted sound event recognition for home service robots.

PubMed

Do, Ha Manh; Sheng, Weihua; Liu, Meiqin

This paper proposes and implements an open framework of active auditory learning for a home service robot to serve the elderly living alone at home. The framework was developed to realize the various auditory perception capabilities while enabling a remote human operator to involve in the sound event recognition process for elderly care. The home service robot is able to estimate the sound source position and collaborate with the human operator in sound event recognition while protecting the privacy of the elderly. Our experimental results validated the proposed framework and evaluated auditory perception capabilities and human-robot collaboration in sound event recognition.

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras.

PubMed

Nguyen, Dat Tien; Hong, Hyung Gil; Kim, Ki Wan; Park, Kang Ryoung

2017-03-16

The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG) Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body.

PubMed

Nguyen, Dat Tien; Park, Kang Ryoung

2016-07-21

With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG) method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG), which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images.

Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG) Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body

PubMed Central

Nguyen, Dat Tien; Park, Kang Ryoung

2016-01-01

With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG) method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG), which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images. PMID:27455264

Do pattern recognition skills transfer across sports? A preliminary analysis.

PubMed

Smeeton, Nicholas J; Ward, Paul; Williams, A Mark

2004-02-01

The ability to recognize patterns of play is fundamental to performance in team sports. While typically assumed to be domain-specific, pattern recognition skills may transfer from one sport to another if similarities exist in the perceptual features and their relations and/or the strategies used to encode and retrieve relevant information. A transfer paradigm was employed to compare skilled and less skilled soccer, field hockey and volleyball players' pattern recognition skills. Participants viewed structured and unstructured action sequences from each sport, half of which were randomly represented with clips not previously seen. The task was to identify previously viewed action sequences quickly and accurately. Transfer of pattern recognition skill was dependent on the participant's skill, sport practised, nature of the task and degree of structure. The skilled soccer and hockey players were quicker than the skilled volleyball players at recognizing structured soccer and hockey action sequences. Performance differences were not observed on the structured volleyball trials between the skilled soccer, field hockey and volleyball players. The skilled field hockey and soccer players were able to transfer perceptual information or strategies between their respective sports. The less skilled participants' results were less clear. Implications for domain-specific expertise, transfer and diversity across domains are discussed.

Graded Mirror Self-Recognition by Clark's Nutcrackers.

PubMed

Clary, Dawson; Kelly, Debbie M

2016-11-04

The traditional 'mark test' has shown some large-brained species are capable of mirror self-recognition. During this test a mark is inconspicuously placed on an animal's body where it can only be seen with the aid of a mirror. If the animal increases the number of actions directed to the mark region when presented with a mirror, the animal is presumed to have recognized the mirror image as its reflection. However, the pass/fail nature of the mark test presupposes self-recognition exists in entirety or not at all. We developed a novel mirror-recognition task, to supplement the mark test, which revealed gradation in the self-recognition of Clark's nutcrackers, a large-brained corvid. To do so, nutcrackers cached food alone, observed by another nutcracker, or with a regular or blurry mirror. The nutcrackers suppressed caching with a regular mirror, a behavioural response to prevent cache theft by conspecifics, but did not suppress caching with a blurry mirror. Likewise, during the mark test, most nutcrackers made more self-directed actions to the mark with a blurry mirror than a regular mirror. Both results suggest self-recognition was more readily achieved with the blurry mirror and that self-recognition may be more broadly present among animals than currently thought.

Exploring Techniques for Vision Based Human Activity Recognition: Methods, Systems, and Evaluation

PubMed Central

Xu, Xin; Tang, Jinshan; Zhang, Xiaolong; Liu, Xiaoming; Zhang, Hong; Qiu, Yimin

2013-01-01

With the wide applications of vision based intelligent systems, image and video analysis technologies have attracted the attention of researchers in the computer vision field. In image and video analysis, human activity recognition is an important research direction. By interpreting and understanding human activities, we can recognize and predict the occurrence of crimes and help the police or other agencies react immediately. In the past, a large number of papers have been published on human activity recognition in video and image sequences. In this paper, we provide a comprehensive survey of the recent development of the techniques, including methods, systems, and quantitative evaluation of the performance of human activity recognition. PMID:23353144

Linking water quality and well-being for improved assessment and valuation of ecosystem services

PubMed Central

Keeler, Bonnie L.; Polasky, Stephen; Brauman, Kate A.; Johnson, Kris A.; Finlay, Jacques C.; O’Neill, Ann; Kovacs, Kent; Dalzell, Brent

2012-01-01

Despite broad recognition of the value of the goods and services provided by nature, existing tools for assessing and valuing ecosystem services often fall short of the needs and expectations of decision makers. Here we address one of the most important missing components in the current ecosystem services toolbox: a comprehensive and generalizable framework for describing and valuing water quality-related services. Water quality is often misrepresented as a final ecosystem service. We argue that it is actually an important contributor to many different services, from recreation to human health. We present a valuation approach for water quality-related services that is sensitive to different actions that affect water quality, identifies aquatic endpoints where the consequences of changing water quality on human well-being are realized, and recognizes the unique groups of beneficiaries affected by those changes. We describe the multiple biophysical and economic pathways that link actions to changes in water quality-related ecosystem goods and services and provide guidance to researchers interested in valuing these changes. Finally, we present a valuation template that integrates biophysical and economic models, links actions to changes in service provision and value estimates, and considers multiple sources of water quality-related ecosystem service values without double counting. PMID:23091018

Linking water quality and well-being for improved assessment and valuation of ecosystem services.

PubMed

Keeler, Bonnie L; Polasky, Stephen; Brauman, Kate A; Johnson, Kris A; Finlay, Jacques C; O'Neill, Ann; Kovacs, Kent; Dalzell, Brent

2012-11-06

Despite broad recognition of the value of the goods and services provided by nature, existing tools for assessing and valuing ecosystem services often fall short of the needs and expectations of decision makers. Here we address one of the most important missing components in the current ecosystem services toolbox: a comprehensive and generalizable framework for describing and valuing water quality-related services. Water quality is often misrepresented as a final ecosystem service. We argue that it is actually an important contributor to many different services, from recreation to human health. We present a valuation approach for water quality-related services that is sensitive to different actions that affect water quality, identifies aquatic endpoints where the consequences of changing water quality on human well-being are realized, and recognizes the unique groups of beneficiaries affected by those changes. We describe the multiple biophysical and economic pathways that link actions to changes in water quality-related ecosystem goods and services and provide guidance to researchers interested in valuing these changes. Finally, we present a valuation template that integrates biophysical and economic models, links actions to changes in service provision and value estimates, and considers multiple sources of water quality-related ecosystem service values without double counting.

Somatotopic Semantic Priming and Prediction in the Motor System

PubMed Central

Grisoni, Luigi; Dreyer, Felix R.; Pulvermüller, Friedemann

2016-01-01

The recognition of action-related sounds and words activates motor regions, reflecting the semantic grounding of these symbols in action information; in addition, motor cortex exerts causal influences on sound perception and language comprehension. However, proponents of classic symbolic theories still dispute the role of modality-preferential systems such as the motor cortex in the semantic processing of meaningful stimuli. To clarify whether the motor system carries semantic processes, we investigated neurophysiological indexes of semantic relationships between action-related sounds and words. Event-related potentials revealed that action-related words produced significantly larger stimulus-evoked (Mismatch Negativity-like) and predictive brain responses (Readiness Potentials) when presented in body-part-incongruent sound contexts (e.g., “kiss” in footstep sound context; “kick” in whistle context) than in body-part-congruent contexts, a pattern reminiscent of neurophysiological correlates of semantic priming. Cortical generators of the semantic relatedness effect were localized in areas traditionally associated with semantic memory, including left inferior frontal cortex and temporal pole, and, crucially, in motor areas, where body-part congruency of action sound–word relationships was indexed by a somatotopic pattern of activation. As our results show neurophysiological manifestations of action-semantic priming in the motor cortex, they prove semantic processing in the motor system and thus in a modality-preferential system of the human brain. PMID:26908635

Gender equality and sustainable human development are key issues.

PubMed

Ando, H

1995-01-01

In a message to the Indochina Women's Parliamentarians Meeting, Hirofumi Ando, Deputy Executive Director of the United Nations Family Planning Association (UNFPA), encouraged participants to link gender equality and development issues. Ando noted that many of the goals of the 1994 International Conference on Population and Development imply recognition of the need to redress gender inequalities and empower women. The Program of Action adopted in Cairo requires countries to achieve universal access to primary education and reproductive health care services. Parliamentarians in attendance were urged to mobilize the financial resources and political will necessary to implement programs in these areas.

Struggle for life, struggle for love and recognition: the neglected self in social cognitive neuroscience

PubMed Central

Paradiso, Sergio; Rudrauf, David

2012-01-01

In the following article we present a view that social cognition and social neuroscience, as shaped by the current research paradigms, are not sufficient to improve our understanding of psychopathological phenomena. We hold that the self, self-awareness, and inter-subjectivity are integral to social perception and actions. In addition, we emphasize that the self and self-awareness are, by their very nature and function, involved over the entire lifespan with the way the individual is perceived in the social environment. Likewise, the modes of operation and identification of the self and self-awareness receive strong developmental contributions from social interactions with parental figures, siblings, peers, and significant others. These contributions are framed by a competitive and cooperative struggle for love and recognition. We suggest that in humans social cognitive neuroscience should be informed by a thoughtful appreciation of the equal significance of the struggle for “life” and that for love and recognition. In order to be better positioned to improve the research agenda and practice of clinical psychiatry, we propose that cognitive and social neurosciences explicitly incorporate in their models phenomena relative to the self, self-awareness, and inter-subjectivity. PMID:22577306

Struggle for life, struggle for love and recognition: the neglected self in social cognitive neuroscience.

PubMed

Paradiso, Sergio; Rudrauf, David

2012-03-01

In the following article we present a view that social cognition and social neuroscience, as shaped by the current research paradigms, are not sufficient to improve our understanding of psychopathological phenomena. We hold that the self, self-awareness, and inter-subjectivity are integral to social perception and actions. In addition, we emphasize that the self and self-awareness are, by their very nature and function, involved over the entire lifespan with the way the individual is perceived in the social environment. Likewise, the modes of operation and identification of the self and self-awareness receive strong developmental contributions from social interactions with parental figures, siblings, peers, and significant others. These contributions are framed by a competitive and cooperative struggle for love and recognition. We suggest that in humans social cognitive neuroscience should be informed by a thoughtful appreciation of the equal significance of the struggle for "life" and that for love and recognition. In order to be better positioned to improve the research agenda and practice of clinical psychiatry, we propose that cognitive and social neurosciences explicitly incorporate in their models phenomena relative to the self, self-awareness, and inter-subjectivity.

Bioavailability of Dietary Polyphenols and Gut Microbiota Metabolism: Antimicrobial Properties

PubMed Central

Miguélez, Elisa M.; Villar, Claudio J.

2015-01-01

Polyphenolic compounds are plant nutraceuticals showing a huge structural diversity, including chlorogenic acids, hydrolyzable tannins, and flavonoids (flavonols, flavanones, flavan-3-ols, anthocyanidins, isoflavones, and flavones). Most of them occur as glycosylated derivatives in plants and foods. In order to become bioactive at human body, these polyphenols must undergo diverse intestinal transformations, due to the action of digestive enzymes, but also by the action of microbiota metabolism. After elimination of sugar tailoring (generating the corresponding aglycons) and diverse hydroxyl moieties, as well as further backbone reorganizations, the final absorbed compounds enter the portal vein circulation towards liver (where other enzymatic transformations take place) and from there to other organs, including behind the digestive tract or via blood towards urine excretion. During this transit along diverse tissues and organs, they are able to carry out strong antiviral, antibacterial, and antiparasitic activities. This paper revises and discusses these antimicrobial activities of dietary polyphenols and their relevance for human health, shedding light on the importance of polyphenols structure recognition by specific enzymes produced by intestinal microbial taxa. PMID:25802870

Free will and determinism in first Isaiah: secular hermeneutics, the poetics of contingency, and Emile Durkheim's Homo duplex.

PubMed

Berlinerblau, Jacques

2003-01-01

Are human beings the sovereign authors of their own thoughts and actions? Or are thought and action determined by external forces beyond their comprehension and control? For the biblical document known to exegetes as First Isaiah (chapters 1-39 of the Book of Isaiah) the answer to both queries seems to be yes. In this article various solutions are advanced to explain why this text equivocates on the question of free will and determinism. One possibility is that the document's collective, transhistorical composition may have scrambled its once coherent message beyond all recognition. Following Emile Durkheim's discussions of homo duplex, it will also be suggested that First Isaiah's confusion may be a manifestation of a deeper contradiction inherent to human consciousness-one that thus recurs across sociological time and space. Both solutions are united by their rejection of traditional theological approaches that have been brought to bear on First Isaiah and the Hebrew Bible in general. It is the movement away from such apologetic exegesis that characterizes the inchoate interpretive orientation that I call "secular hermeneutics."

The Influence of Action Perception on Object Recognition: A Developmental Study

ERIC Educational Resources Information Center

Mounoud, Pierre; Duscherer, Katia; Moy, Guenael; Perraudin, Sandrine

2007-01-01

Two experiments explored the existence and the development of relations between action representations and object representations. A priming paradigm was used in which participants viewed an action pantomime followed by the picture of a tool, the tool being either associated or unassociated with the preceding action. Overall, we observed that the…

Impairments in Monkey and Human Face Recognition in 2-Year-Old Toddlers with Autism Spectrum Disorder and Developmental Delay

ERIC Educational Resources Information Center

Chawarska, Katarzyna; Volkmar, Fred

2007-01-01

Face recognition impairments are well documented in older children with Autism Spectrum Disorders (ASD); however, the developmental course of the deficit is not clear. This study investigates the progressive specialization of face recognition skills in children with and without ASD. Experiment 1 examines human and monkey face recognition in…

Vision Systems with the Human in the Loop

NASA Astrophysics Data System (ADS)

Bauckhage, Christian; Hanheide, Marc; Wrede, Sebastian; Käster, Thomas; Pfeiffer, Michael; Sagerer, Gerhard

2005-12-01

The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed.

Molecular mechanism of peroxisome proliferator-activated receptor α activation by WY14643: a new mode of ligand recognition and receptor stabilization.

PubMed

Bernardes, Amanda; Souza, Paulo C T; Muniz, João R C; Ricci, Clarisse G; Ayers, Stephen D; Parekh, Nili M; Godoy, André S; Trivella, Daniela B B; Reinach, Peter; Webb, Paul; Skaf, Munir S; Polikarpov, Igor

2013-08-23

Peroxisome proliferator-activated receptors (PPARs) are members of a superfamily of nuclear transcription factors. They are involved in mediating numerous physiological effects in humans, including glucose and lipid metabolism. PPARα ligands effectively treat dyslipidemia and have significant antiinflammatory and anti-atherosclerotic activities. These effects and their ligand-dependent activity make nuclear receptors obvious targets for drug design. Here, we present the structure of the human PPARα in complex with WY14643, a member of fibrate class of drug, and a widely used PPAR activator. The crystal structure of this complex suggests that WY14643 induces activation of PPARα in an unusual bipartite mechanism involving conventional direct helix 12 stabilization and an alternative mode that involves a second ligand in the pocket. We present structural observations, molecular dynamics and activity assays that support the importance of the second site in WY14643 action. The unique binding mode of WY14643 reveals a new pattern of nuclear receptor ligand recognition and suggests a novel basis for ligand design, offering clues for improving the binding affinity and selectivity of ligand. We show that binding of WY14643 to PPARα was associated with antiinflammatory disease in a human corneal cell model, suggesting possible applications for PPARα ligands. Copyright © 2013 Elsevier Ltd. All rights reserved.

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

PubMed Central

Nguyen, Dat Tien; Hong, Hyung Gil; Kim, Ki Wan; Park, Kang Ryoung

2017-01-01

The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body. PMID:28300783

Suspicious Behavior Detection System for an Open Space Parking Based on Recognition of Human Elemental Actions

NASA Astrophysics Data System (ADS)

Inomata, Teppei; Kimura, Kouji; Hagiwara, Masafumi

Studies for video surveillance applications for preventing various crimes such as stealing and violence have become a hot topic. This paper proposes a new video surveillance system that can detect suspicious behaviors such as a car break-in and vandalization in an open space parking, and that is based on image processing. The proposed system has the following features: it 1)deals time series data flow, 2)recognizes “human elemental actions” using statistic features, and 3)detects suspicious behavior using Subspace method and AdaBoost. We conducted the experiments to test the performance of the proposed system using open space parking scenes. As a result, we obtained about 10.0% for false positive rate, and about 4.6% for false negative rate.

Associations between awareness of beyondblue and mental health literacy in Australian youth: Results from a national survey.

PubMed

Yap, Marie B; Reavley, Nicola J; Jorm, Anthony F

2012-06-01

The aim of this paper is to examine whether Australian young people's awareness of beyondblue is associated with better recognition of depression and anxiety disorders, and better quality of beliefs about possible interventions and first-aid actions for these problems. In 2011, a telephone interview was conducted with a national sample of 3021 Australians aged between 15 and 25 years. Participants were presented with a vignette portraying depression, depression with suicidal thoughts, social phobia, post-traumatic stress disorder or psychosis in a young person. They were then asked about recognition of the disorder portrayed, their beliefs about the helpfulness or harmfulness of various interventions and first-aid actions, and their awareness of beyondblue. The quality of youths' beliefs was scored against health professionals' ratings of the same list of interventions and first-aid actions. Beyondblue awareness was associated with more accurate recognition of the disorder portrayed in all vignettes except social phobia. It was also associated with beliefs about the helpfulness of first-aid actions that were more closely aligned with professional ratings for the depression, psychosis and social phobia vignettes. However, it was associated with beliefs about interventions for the psychosis vignette only. Overall, the associations of beyondblue awareness with better mental health literacy were not specific to depression and anxiety disorders, which are their main focus. Beyondblue awareness is mostly unrelated to treatment beliefs, but seems to have non-specific associations with recognition of disorders and first-aid beliefs.

Bridging automatic speech recognition and psycholinguistics: Extending Shortlist to an end-to-end model of human speech recognition (L)

NASA Astrophysics Data System (ADS)

Scharenborg, Odette; ten Bosch, Louis; Boves, Lou; Norris, Dennis

2003-12-01

This letter evaluates potential benefits of combining human speech recognition (HSR) and automatic speech recognition by building a joint model of an automatic phone recognizer (APR) and a computational model of HSR, viz., Shortlist [Norris, Cognition 52, 189-234 (1994)]. Experiments based on ``real-life'' speech highlight critical limitations posed by some of the simplifying assumptions made in models of human speech recognition. These limitations could be overcome by avoiding hard phone decisions at the output side of the APR, and by using a match between the input and the internal lexicon that flexibly copes with deviations from canonical phonemic representations.

Different categories of living and non-living sound-sources activate distinct cortical networks

PubMed Central

Engel, Lauren R.; Frum, Chris; Puce, Aina; Walker, Nathan A.; Lewis, James W.

2009-01-01

With regard to hearing perception, it remains unclear as to whether, or the extent to which, different conceptual categories of real-world sounds and related categorical knowledge are differentially represented in the brain. Semantic knowledge representations are reported to include the major divisions of living versus non-living things, plus more specific categories including animals, tools, biological motion, faces, and places—categories typically defined by their characteristic visual features. Here, we used functional magnetic resonance imaging (fMRI) to identify brain regions showing preferential activity to four categories of action sounds, which included non-vocal human and animal actions (living), plus mechanical and environmental sound-producing actions (non-living). The results showed a striking antero-posterior division in cortical representations for sounds produced by living versus non-living sources. Additionally, there were several significant differences by category, depending on whether the task was category-specific (e.g. human or not) versus non-specific (detect end-of-sound). In general, (1) human-produced sounds yielded robust activation in the bilateral posterior superior temporal sulci independent of task. Task demands modulated activation of left-lateralized fronto-parietal regions, bilateral insular cortices, and subcortical regions previously implicated in observation-execution matching, consistent with “embodied” and mirror-neuron network representations subserving recognition. (2) Animal action sounds preferentially activated the bilateral posterior insulae. (3) Mechanical sounds activated the anterior superior temporal gyri and parahippocampal cortices. (4) Environmental sounds preferentially activated dorsal occipital and medial parietal cortices. Overall, this multi-level dissociation of networks for preferentially representing distinct sound-source categories provides novel support for grounded cognition models that may underlie organizational principles for hearing perception. PMID:19465134

Feedforward object-vision models only tolerate small image variations compared to human

PubMed Central

Ghodrati, Masoud; Farzmahdi, Amirhossein; Rajaei, Karim; Ebrahimpour, Reza; Khaligh-Razavi, Seyed-Mahdi

2014-01-01

Invariant object recognition is a remarkable ability of primates' visual system that its underlying mechanism has constantly been under intense investigations. Computational modeling is a valuable tool toward understanding the processes involved in invariant object recognition. Although recent computational models have shown outstanding performances on challenging image databases, they fail to perform well in image categorization under more complex image variations. Studies have shown that making sparse representation of objects by extracting more informative visual features through a feedforward sweep can lead to higher recognition performances. Here, however, we show that when the complexity of image variations is high, even this approach results in poor performance compared to humans. To assess the performance of models and humans in invariant object recognition tasks, we built a parametrically controlled image database consisting of several object categories varied in different dimensions and levels, rendered from 3D planes. Comparing the performance of several object recognition models with human observers shows that only in low-level image variations the models perform similar to humans in categorization tasks. Furthermore, the results of our behavioral experiments demonstrate that, even under difficult experimental conditions (i.e., briefly presented masked stimuli with complex image variations), human observers performed outstandingly well, suggesting that the models are still far from resembling humans in invariant object recognition. Taken together, we suggest that learning sparse informative visual features, although desirable, is not a complete solution for future progresses in object-vision modeling. We show that this approach is not of significant help in solving the computational crux of object recognition (i.e., invariant object recognition) when the identity-preserving image variations become more complex. PMID:25100986

Novel images and novel locations of familiar images as sensitive translational cognitive tests in humans.

PubMed

Raber, Jacob

2015-05-15

Object recognition is a sensitive cognitive test to detect effects of genetic and environmental factors on cognition in rodents. There are various versions of object recognition that have been used since the original test was reported by Ennaceur and Delacour in 1988. There are nonhuman primate and human primate versions of object recognition as well, allowing cross-species comparisons. As no language is required for test performance, object recognition is a very valuable test for human research studies in distinct parts of the world, including areas where there might be less years of formal education. The main focus of this review is to illustrate how object recognition can be used to assess cognition in humans under normal physiological and neurological conditions. Copyright © 2015 Elsevier B.V. All rights reserved.

Posture recognition based on fuzzy logic for home monitoring of the elderly.

PubMed

Brulin, Damien; Benezeth, Yannick; Courtial, Estelle

2012-09-01

We propose in this paper a computer vision-based posture recognition method for home monitoring of the elderly. The proposed system performs human detection prior to the posture analysis; posture recognition is performed only on a human silhouette. The human detection approach has been designed to be robust to different environmental stimuli. Thus, posture is analyzed with simple and efficient features that are not designed to manage constraints related to the environment but only designed to describe human silhouettes. The posture recognition method, based on fuzzy logic, identifies four static postures and is robust to variation in the distance between the camera and the person, and to the person's morphology. With an accuracy of 74.29% of satisfactory posture recognition, this approach can detect emergency situations such as a fall within a health smart home.

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks

PubMed Central

Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

2016-01-01

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches. PMID:27792136

A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks.

PubMed

Ponce, Hiram; Miralles-Pechuán, Luis; Martínez-Villaseñor, María de Lourdes

2016-10-25

Physical activity recognition based on sensors is a growing area of interest given the great advances in wearable sensors. Applications in various domains are taking advantage of the ease of obtaining data to monitor personal activities and behavior in order to deliver proactive and personalized services. Although many activity recognition systems have been developed for more than two decades, there are still open issues to be tackled with new techniques. We address in this paper one of the main challenges of human activity recognition: Flexibility. Our goal in this work is to present artificial hydrocarbon networks as a novel flexible approach in a human activity recognition system. In order to evaluate the performance of artificial hydrocarbon networks based classifier, experimentation was designed for user-independent, and also for user-dependent case scenarios. Our results demonstrate that artificial hydrocarbon networks classifier is flexible enough to be used when building a human activity recognition system with either user-dependent or user-independent approaches.

The Mirror Neuron System and Action Recognition

ERIC Educational Resources Information Center

Buccino, Giovanni; Binkofski, Ferdinand; Riggio, Lucia

2004-01-01

Mirror neurons, first described in the rostral part of monkey ventral premotor cortex (area F5), discharge both when the animal performs a goal-directed hand action and when it observes another individual performing the same or a similar action. More recently, in the same area mirror neurons responding to the observation of mouth actions have been…

Action at Its Place: Contextual Settings Enhance Action Recognition in 4- to 8-Year-Old Children

ERIC Educational Resources Information Center

Wurm, Moritz F.; Artemenko, Christina; Giuliani, Daniela; Schubotz, Ricarda I.

2017-01-01

Actions are recognized faster and with higher accuracy when they take place in their typical environments. It is unclear, however, when contextual cues from the environment become effectively exploited during childhood and whether contextual integration interacts with other factors such as children's perceptual or motor experience with an action.…

How We Recognize Our Own Actions

NASA Astrophysics Data System (ADS)

Blakemore, Sarah-Jayne

This chapter first describes how predicting the sensory consequences of action contributes to the recognition of one's own actions. Second, the chapter discusses three symptoms in which this prediction mechanism is proposed to be impaired: the consequences of parietal lobe damage, passivity experiences associated with schizophrenia, and phantom limbs.

Participatory Action Research: International Contexts and Consequences.

ERIC Educational Resources Information Center

McTaggart, Robin, Ed.

The collection of essays in this book illustrate commonalties and differences among the theories, practices, and forms of organization of participatory action research in different countries. Participatory action research expresses the recognition that all research methodologies are implicitly political in nature, and this is reflected in the…

Decoding the Functional Roles of Cationic Side Chains of the Major Antimicrobial Region of Human Cathelicidin LL-37

PubMed Central

Epand, Raquel F.; Mishra, Biswajit; Lushnikova, Tamara; Thomas, Vinai Chittezham; Bayles, Kenneth W.; Epand, Richard M.

2012-01-01

Human cathelicidin LL-37 is a critical cationic antimicrobial peptide for host defense against infection, immune modulation, and wound healing. This article elucidates the functional roles of the cationic side chains of the major antimicrobial region of LL-37, corresponding to residues 17 to 32 (designated GF-17). Antimicrobial assays, killing kinetics studies, and vesicle leakage experiments all indicate that a conversion of lysines to arginines affected the ability of the peptide to kill the Gram-positive Staphylococcus aureus strain USA300. Alanine scanning experiments show that S. aureus is less sensitive than Escherichia coli to a single cationic residue mutation of GF-17. Among the five cationic residues, R23 appears to be somewhat important in killing S. aureus. However, R23 and K25 of GF-17 are of prime importance in killing the Gram-negative organism E. coli. In particular, R23 is essential for (i) rapid recognition, (ii) permeation of the E. coli outer membrane, (iii) clustering of anionic lipids in a membrane system mimicking the E. coli inner membrane, and (iv) membrane disruption. Bacterial aggregation (i.e., rapid recognition via charge neutralization) is the first step of the peptide action. Structurally, R23 is located in the interface (i.e., the first action layer), a situation ideal for the interactions listed above. In contrast, residues K18, R19, and R29 are on the hydrophilic surface of the amphipathic helix and play only a secondary role. Mapping of the functional spectrum of cationic residues of GF-17 provides a solid basis for engineering bacterium-specific antimicrobials using this highly potent template. PMID:22083479

Lack of discrimination between DNA ligases I and III by two classes of inhibitors, anthracyclines and distamycins.

PubMed

Montecucco, A; Lestingi, M; Rossignol, J M; Elder, R H; Ciarrocchi, G

1993-04-06

We have measured the effects of eight distamycin and two anthracycline derivatives on polynucleotide joining and self-adenylating activities of human DNA ligase I and rat DNA ligases I and III. All test drugs show good inhibitory activity against the three enzymes in the poly[d(A-T)] joining assay. Several distamycins also inhibit the DNA-independent self-adenylation reaction catalysed by the human enzyme and, to a lesser extent, by rat DNA ligases. These results confirm that anthracyclines and distamycins express their inhibitory action against DNA joining activities mainly via specific interactions with the substrate, and suggest that the three test DNA ligases utilize similar, if not identical, mechanisms of recognition and interaction with DNA-drug complexes. Our findings also indicate that distamycins have a greater affinity for human DNA ligase I than for rat enzymes, suggesting that, in this respect, rat DNA ligase I is more similar to rat DNA ligase III than to human DNA ligase I.

Advancing sexual health through human rights: The role of the law

PubMed Central

Kismödi, Eszter; Cottingham, Jane; Gruskin, Sofia; Miller, Alice M.

2015-01-01

Since the International Conference on Population and Development, definitions of sexuality and sexual health have been greatly elaborated alongside widely accepted recognition that sexual health requires respect, protection and fulfilment of human rights. Considerable progress has also been made in enacting or changing laws that affect sexuality and sexual health, in line with human rights standards. These measures include legal guarantees against non-discrimination and violence, decriminalisation of consensual sexual conduct and guaranteeing availability, accessibility, acceptability and quality of sexual health information and services to all. Such legal actions have had positive effects on health and specifically on sexual health, particularly for marginalised populations. Yet in all regions of the world, laws still exist which jeopardise health, including sexual health, and violate human rights. In order to ensure accountability for the rights and health of their populations, states have an obligation to bring their laws into line with international, regional and national human rights standards. These rights-based legal guarantees, while insufficient alone, are essential for effective systems of accountability, achieving positive sexual health outcomes and the respect and protection of human rights. PMID:25539286

Advancing sexual health through human rights: the role of the law.

PubMed

Kismödi, Eszter; Cottingham, Jane; Gruskin, Sofia; Miller, Alice M

2015-01-01

Since the International Conference on Population and Development, definitions of sexuality and sexual health have been greatly elaborated alongside widely accepted recognition that sexual health requires respect, protection and fulfilment of human rights. Considerable progress has also been made in enacting or changing laws that affect sexuality and sexual health, in line with human rights standards. These measures include legal guarantees against non-discrimination and violence, decriminalisation of consensual sexual conduct and guaranteeing availability, accessibility, acceptability and quality of sexual health information and services to all. Such legal actions have had positive effects on health and specifically on sexual health, particularly for marginalised populations. Yet in all regions of the world, laws still exist which jeopardise health, including sexual health, and violate human rights. In order to ensure accountability for the rights and health of their populations, states have an obligation to bring their laws into line with international, regional and national human rights standards. These rights-based legal guarantees, while insufficient alone, are essential for effective systems of accountability, achieving positive sexual health outcomes and the respect and protection of human rights.

Functional differentiation of macaque visual temporal cortical neurons using a parametric action space.

PubMed

Vangeneugden, Joris; Pollick, Frank; Vogels, Rufin

2009-03-01

Neurons in the rostral superior temporal sulcus (STS) are responsive to displays of body movements. We employed a parametric action space to determine how similarities among actions are represented by visual temporal neurons and how form and motion information contributes to their responses. The stimulus space consisted of a stick-plus-point-light figure performing arm actions and their blends. Multidimensional scaling showed that the responses of temporal neurons represented the ordinal similarity between these actions. Further tests distinguished neurons responding equally strongly to static presentations and to actions ("snapshot" neurons), from those responding much less strongly to static presentations, but responding well when motion was present ("motion" neurons). The "motion" neurons were predominantly found in the upper bank/fundus of the STS, and "snapshot" neurons in the lower bank of the STS and inferior temporal convexity. Most "motion" neurons showed strong response modulation during the course of an action, thus responding to action kinematics. "Motion" neurons displayed a greater average selectivity for these simple arm actions than did "snapshot" neurons. We suggest that the "motion" neurons code for visual kinematics, whereas the "snapshot" neurons code for form/posture, and that both can contribute to action recognition, in agreement with computation models of action recognition.

Human sperm bind to the N-terminal domain of ZP2 in humanized zonae pellucidae in transgenic mice

PubMed Central

Baibakov, Boris; Boggs, Nathan A.; Yauger, Belinda; Baibakov, Galina

2012-01-01

Fertilization requires taxon-specific gamete recognition, and human sperm do not bind to zonae pellucidae (ZP1–3) surrounding mouse eggs. Using transgenesis to replace endogenous mouse proteins with human homologues, gain-of-function sperm-binding assays were established to evaluate human gamete recognition. Human sperm bound only to zonae pellucidae containing human ZP2, either alone or coexpressed with other human zona proteins. Binding to the humanized matrix was a dominant effect that resulted in human sperm penetration of the zona pellucida and accumulation in the perivitelline space, where they were unable to fuse with mouse eggs. Using recombinant peptides, the site of gamete recognition was located to a defined domain in the N terminus of ZP2. These results provide experimental evidence for the role of ZP2 in mediating sperm binding to the zona pellucida and support a model in which human sperm–egg recognition is dependent on an N-terminal domain of ZP2, which is degraded after fertilization to provide a definitive block to polyspermy. PMID:22734000

President's Report on AACC Strategic Action Areas and Initiatives.

ERIC Educational Resources Information Center

American Association of Community Colleges, Washington, DC.

This is a summary of the American Association of Community College's (AACC) Strategic Action Areas and corresponding initiatives. Strategies for Action Area I (National and International Recognition and Advocacy for Community Colleges) focus primarily on the creation of task forces to address key legislative issues in higher education. Examples…

Overview of the membrane-associated RING-CH (MARCH) E3 ligase family.

PubMed

Bauer, Johannes; Bakke, Oddmund; Morth, J Preben

2017-09-25

E3 ligases are critical checkpoints for protein ubiquitination, a signal that often results in protein sorting and degradation but has also been linked to regulation of transcription and DNA repair. In line with their key role in cellular trafficking and cell-cycle control, malfunction of E3 ligases is often linked to human disease. Thus, they have emerged as prime drug targets. However, the molecular basis of action of membrane-bound E3 ligases is still unknown. Here, we review the current knowledge on the membrane-embedded MARCH E3 ligases (MARCH-1-6,7,8,11) with a focus on how the transmembrane regions can contribute via GxxxG-motifs to the selection and recognition of other membrane proteins as substrates for ubiquitination. Further understanding of the molecular parameters that govern target protein recognition of MARCH E3 ligases will contribute to development of strategies for therapeutic regulation of MARCH-induced ubiquitination. Copyright © 2016 Elsevier B.V. All rights reserved.

Human Activity Recognition from Body Sensor Data using Deep Learning.

PubMed

Hassan, Mohammad Mehedi; Huda, Shamsul; Uddin, Md Zia; Almogren, Ahmad; Alrubaian, Majed

2018-04-16

In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart home to improve their rehabilitation processes. However, it is not easy to accurately and automatically recognize physical human activity through wearable sensors due to the complexity and variety of body activities. In this paper, we address the human activity recognition problem as a classification problem using wearable body sensor data. In particular, we propose to utilize a Deep Belief Network (DBN) model for successful human activity recognition. First, we extract the important initial features from the raw body sensor data. Then, a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) are performed to further process the features and make them more robust to be useful for fast activity recognition. Finally, the DBN is trained by these features. Various experiments were performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN outperformed other algorithms and achieves satisfactory activity recognition performance.

Interaction in Spoken Word Recognition Models: Feedback Helps.

PubMed

Magnuson, James S; Mirman, Daniel; Luthra, Sahil; Strauss, Ted; Harris, Harlan D

2018-01-01

Human perception, cognition, and action requires fast integration of bottom-up signals with top-down knowledge and context. A key theoretical perspective in cognitive science is the interactive activation hypothesis: forward and backward flow in bidirectionally connected neural networks allows humans and other biological systems to approximate optimal integration of bottom-up and top-down information under real-world constraints. An alternative view is that online feedback is neither necessary nor helpful; purely feed forward alternatives can be constructed for any feedback system, and online feedback could not improve processing and would preclude veridical perception. In the domain of spoken word recognition, the latter view was apparently supported by simulations using the interactive activation model, TRACE, with and without feedback: as many words were recognized more quickly without feedback as were recognized faster with feedback, However, these simulations used only a small set of words and did not address a primary motivation for interaction: making a model robust in noise. We conducted simulations using hundreds of words, and found that the majority were recognized more quickly with feedback than without. More importantly, as we added noise to inputs, accuracy and recognition times were better with feedback than without. We follow these simulations with a critical review of recent arguments that online feedback in interactive activation models like TRACE is distinct from other potentially helpful forms of feedback. We conclude that in addition to providing the benefits demonstrated in our simulations, online feedback provides a plausible means of implementing putatively distinct forms of feedback, supporting the interactive activation hypothesis.

Interaction in Spoken Word Recognition Models: Feedback Helps

PubMed Central

Magnuson, James S.; Mirman, Daniel; Luthra, Sahil; Strauss, Ted; Harris, Harlan D.

2018-01-01

Human perception, cognition, and action requires fast integration of bottom-up signals with top-down knowledge and context. A key theoretical perspective in cognitive science is the interactive activation hypothesis: forward and backward flow in bidirectionally connected neural networks allows humans and other biological systems to approximate optimal integration of bottom-up and top-down information under real-world constraints. An alternative view is that online feedback is neither necessary nor helpful; purely feed forward alternatives can be constructed for any feedback system, and online feedback could not improve processing and would preclude veridical perception. In the domain of spoken word recognition, the latter view was apparently supported by simulations using the interactive activation model, TRACE, with and without feedback: as many words were recognized more quickly without feedback as were recognized faster with feedback, However, these simulations used only a small set of words and did not address a primary motivation for interaction: making a model robust in noise. We conducted simulations using hundreds of words, and found that the majority were recognized more quickly with feedback than without. More importantly, as we added noise to inputs, accuracy and recognition times were better with feedback than without. We follow these simulations with a critical review of recent arguments that online feedback in interactive activation models like TRACE is distinct from other potentially helpful forms of feedback. We conclude that in addition to providing the benefits demonstrated in our simulations, online feedback provides a plausible means of implementing putatively distinct forms of feedback, supporting the interactive activation hypothesis. PMID:29666593

Memory for Self-Performed Actions in Individuals with Asperger Syndrome

PubMed Central

Zalla, Tiziana; Daprati, Elena; Sav, Anca-Maria; Chaste, Pauline; Nico, Daniele; Leboyer, Marion

2010-01-01

Memory for action is enhanced if individuals are allowed to perform the corresponding movements, compared to when they simply listen to them (enactment effect). Previous studies have shown that individuals with Autism Spectrum Disorders (ASD) have difficulties with processes involving the self, such as autobiographical memories and self performed actions. The present study aimed at assessing memory for action in Asperger Syndrome (AS). We investigated whether adults with AS would benefit from the enactment effect when recalling a list of previously performed items vs. items that were only visually and verbally experienced through three experimental tasks (Free Recall, Old/New Recognition and Source Memory). The results showed that while performance on Recognition and Source Memory tasks was preserved in individuals with AS, the enactment effect for self-performed actions was not consistently present, as revealed by the lower number of performed actions being recalled on the Free Recall test, as compared to adults with typical development. Subtle difficulties in encoding specific motor and proprioceptive signals during action execution in individuals with AS might affect retrieval of relevant personal episodic information. These disturbances might be associated to an impaired action monitoring system. PMID:20967277

Memory for self-performed actions in individuals with Asperger syndrome.

PubMed

Zalla, Tiziana; Daprati, Elena; Sav, Anca-Maria; Chaste, Pauline; Nico, Daniele; Leboyer, Marion

2010-10-12

Memory for action is enhanced if individuals are allowed to perform the corresponding movements, compared to when they simply listen to them (enactment effect). Previous studies have shown that individuals with Autism Spectrum Disorders (ASD) have difficulties with processes involving the self, such as autobiographical memories and self performed actions. The present study aimed at assessing memory for action in Asperger Syndrome (AS). We investigated whether adults with AS would benefit from the enactment effect when recalling a list of previously performed items vs. items that were only visually and verbally experienced through three experimental tasks (Free Recall, Old/New Recognition and Source Memory). The results showed that while performance on Recognition and Source Memory tasks was preserved in individuals with AS, the enactment effect for self-performed actions was not consistently present, as revealed by the lower number of performed actions being recalled on the Free Recall test, as compared to adults with typical development. Subtle difficulties in encoding specific motor and proprioceptive signals during action execution in individuals with AS might affect retrieval of relevant personal episodic information. These disturbances might be associated to an impaired action monitoring system.

Destination memory for self-generated actions.

PubMed

El Haj, Mohamad

2016-10-01

There is a substantial body of literature showing memory enhancement for self-generated information in normal aging. The present paper investigated this outcome for destination memory or memory for outputted information. In Experiment 1, younger adults and older adults had to place (self-generated actions) and observe an experimenter placing (experiment-generated actions) items into two different destinations (i.e., a black circular box and a white square box). On a subsequent recognition task, the participants had to decide into which box each item had originally been placed. These procedures showed better destination memory for self- than experimenter-generated actions. In Experiment 2, destination and source memory were assessed for self-generated actions. Younger adults and older adults had to place items into the two boxes (self-generated actions), take items out of the boxes (self-generated actions), and observe an experimenter taking items out of the boxes (experiment-generated actions). On a subsequent recognition task, they had to decide into which box (destination memory)/from which box (source memory) each item had originally been placed/taken. For both populations, source memory was better than destination memory for self-generated actions, and both were better than source memory for experimenter-generated actions. Taken together, these findings highlight the beneficial effect of self-generation on destination memory in older adults.

A preliminary analysis of human factors affecting the recognition accuracy of a discrete word recognizer for C3 systems

NASA Astrophysics Data System (ADS)

Yellen, H. W.

1983-03-01

Literature pertaining to Voice Recognition abounds with information relevant to the assessment of transitory speech recognition devices. In the past, engineering requirements have dictated the path this technology followed. But, other factors do exist that influence recognition accuracy. This thesis explores the impact of Human Factors on the successful recognition of speech, principally addressing the differences or variability among users. A Threshold Technology T-600 was used for a 100 utterance vocubalary to test 44 subjects. A statistical analysis was conducted on 5 generic categories of Human Factors: Occupational, Operational, Psychological, Physiological and Personal. How the equipment is trained and the experience level of the speaker were found to be key characteristics influencing recognition accuracy. To a lesser extent computer experience, time or week, accent, vital capacity and rate of air flow, speaker cooperativeness and anxiety were found to affect overall error rates.

Modal-Power-Based Haptic Motion Recognition

NASA Astrophysics Data System (ADS)

Kasahara, Yusuke; Shimono, Tomoyuki; Kuwahara, Hiroaki; Sato, Masataka; Ohnishi, Kouhei

Motion recognition based on sensory information is important for providing assistance to human using robots. Several studies have been carried out on motion recognition based on image information. However, in the motion of humans contact with an object can not be evaluated precisely by image-based recognition. This is because the considering force information is very important for describing contact motion. In this paper, a modal-power-based haptic motion recognition is proposed; modal power is considered to reveal information on both position and force. Modal power is considered to be one of the defining features of human motion. A motion recognition algorithm based on linear discriminant analysis is proposed to distinguish between similar motions. Haptic information is extracted using a bilateral master-slave system. Then, the observed motion is decomposed in terms of primitive functions in a modal space. The experimental results show the effectiveness of the proposed method.

An adaptive Hidden Markov Model for activity recognition based on a wearable multi-sensor device

USDA-ARS?s Scientific Manuscript database

Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based o...

Errare machinale est: the use of error-related potentials in brain-machine interfaces

PubMed Central

Chavarriaga, Ricardo; Sobolewski, Aleksander; Millán, José del R.

2014-01-01

The ability to recognize errors is crucial for efficient behavior. Numerous studies have identified electrophysiological correlates of error recognition in the human brain (error-related potentials, ErrPs). Consequently, it has been proposed to use these signals to improve human-computer interaction (HCI) or brain-machine interfacing (BMI). Here, we present a review of over a decade of developments toward this goal. This body of work provides consistent evidence that ErrPs can be successfully detected on a single-trial basis, and that they can be effectively used in both HCI and BMI applications. We first describe the ErrP phenomenon and follow up with an analysis of different strategies to increase the robustness of a system by incorporating single-trial ErrP recognition, either by correcting the machine's actions or by providing means for its error-based adaptation. These approaches can be applied both when the user employs traditional HCI input devices or in combination with another BMI channel. Finally, we discuss the current challenges that have to be overcome in order to fully integrate ErrPs into practical applications. This includes, in particular, the characterization of such signals during real(istic) applications, as well as the possibility of extracting richer information from them, going beyond the time-locked decoding that dominates current approaches. PMID:25100937

Other ways of seeing: From behavior to neural mechanisms in the online “visual” control of action with sensory substitution

PubMed Central

Proulx, Michael J.; Gwinnutt, James; Dell’Erba, Sara; Levy-Tzedek, Shelly; de Sousa, Alexandra A.; Brown, David J.

2015-01-01

Vision is the dominant sense for perception-for-action in humans and other higher primates. Advances in sight restoration now utilize the other intact senses to provide information that is normally sensed visually through sensory substitution to replace missing visual information. Sensory substitution devices translate visual information from a sensor, such as a camera or ultrasound device, into a format that the auditory or tactile systems can detect and process, so the visually impaired can see through hearing or touch. Online control of action is essential for many daily tasks such as pointing, grasping and navigating, and adapting to a sensory substitution device successfully requires extensive learning. Here we review the research on sensory substitution for vision restoration in the context of providing the means of online control for action in the blind or blindfolded. It appears that the use of sensory substitution devices utilizes the neural visual system; this suggests the hypothesis that sensory substitution draws on the same underlying mechanisms as unimpaired visual control of action. Here we review the current state of the art for sensory substitution approaches to object recognition, localization, and navigation, and the potential these approaches have for revealing a metamodal behavioral and neural basis for the online control of action. PMID:26599473

Neural reuse of action perception circuits for language, concepts and communication.

PubMed

Pulvermüller, Friedemann

2018-01-01

Neurocognitive and neurolinguistics theories make explicit statements relating specialized cognitive and linguistic processes to specific brain loci. These linking hypotheses are in need of neurobiological justification and explanation. Recent mathematical models of human language mechanisms constrained by fundamental neuroscience principles and established knowledge about comparative neuroanatomy offer explanations for where, when and how language is processed in the human brain. In these models, network structure and connectivity along with action- and perception-induced correlation of neuronal activity co-determine neurocognitive mechanisms. Language learning leads to the formation of action perception circuits (APCs) with specific distributions across cortical areas. Cognitive and linguistic processes such as speech production, comprehension, verbal working memory and prediction are modelled by activity dynamics in these APCs, and combinatorial and communicative-interactive knowledge is organized in the dynamics within, and connections between APCs. The network models and, in particular, the concept of distributionally-specific circuits, can account for some previously not well understood facts about the cortical 'hubs' for semantic processing and the motor system's role in language understanding and speech sound recognition. A review of experimental data evaluates predictions of the APC model and alternative theories, also providing detailed discussion of some seemingly contradictory findings. Throughout, recent disputes about the role of mirror neurons and grounded cognition in language and communication are assessed critically. Copyright © 2017 The Author. Published by Elsevier Ltd.. All rights reserved.

Relating dynamic brain states to dynamic machine states: Human and machine solutions to the speech recognition problem

PubMed Central

Liu, Xunying; Zhang, Chao; Woodland, Phil; Fonteneau, Elisabeth

2017-01-01

There is widespread interest in the relationship between the neurobiological systems supporting human cognition and emerging computational systems capable of emulating these capacities. Human speech comprehension, poorly understood as a neurobiological process, is an important case in point. Automatic Speech Recognition (ASR) systems with near-human levels of performance are now available, which provide a computationally explicit solution for the recognition of words in continuous speech. This research aims to bridge the gap between speech recognition processes in humans and machines, using novel multivariate techniques to compare incremental ‘machine states’, generated as the ASR analysis progresses over time, to the incremental ‘brain states’, measured using combined electro- and magneto-encephalography (EMEG), generated as the same inputs are heard by human listeners. This direct comparison of dynamic human and machine internal states, as they respond to the same incrementally delivered sensory input, revealed a significant correspondence between neural response patterns in human superior temporal cortex and the structural properties of ASR-derived phonetic models. Spatially coherent patches in human temporal cortex responded selectively to individual phonetic features defined on the basis of machine-extracted regularities in the speech to lexicon mapping process. These results demonstrate the feasibility of relating human and ASR solutions to the problem of speech recognition, and suggest the potential for further studies relating complex neural computations in human speech comprehension to the rapidly evolving ASR systems that address the same problem domain. PMID:28945744

Right-wing extremist violence among adolescents in Germany.

PubMed

Sitzer, Peter; Heitmeyer, Wilhelm

2008-01-01

What are the preconditions for right-wing extremist violence among German youths? For several years, the rate of this violence has been increasing in Germany, and the same can be observed for right-wing extremist orientations characterized by the coming together of ideologies of unequal worth and the acceptance of violence as a mode of action. And although it is emphasized that approval of and willingness to use violence do not automatically lead to actual acts of violence, this article suggests that the existence of these convictions in society helps to legitimize attitudes that become expressed in violence, in particular among youths.This article presents a five-stage process model that portrays the underlying preconditions for acts of right-wing extremist violence, the contexts in which such violence takes place, and the factors that cause it to escalate. This structural model is used to outline central empirical findings of recent German quantitative and especially qualitative studies about right-wing extremist violent offenders. For analytical reasons, the basic elements of the process model (socialization, organization, legitimation, interaction, and escalation) are treated separately. The authors also examine right-wing extremist violence from a disintegrative perspective. Given that intersubjective recognition is an existential human need, right-wing extremist violence is understood as a "productive" way of dealing with individual recognition deficits. On the basis of the integration dimensions of social disintegration theory, three fundamental recognition needs are distinguished. Right-wing extremist violence can best be explained as a consequence of recognition deficits in all three central integration dimensions.

Modulation of α power and functional connectivity during facial affect recognition.

PubMed

Popov, Tzvetan; Miller, Gregory A; Rockstroh, Brigitte; Weisz, Nathan

2013-04-03

Research has linked oscillatory activity in the α frequency range, particularly in sensorimotor cortex, to processing of social actions. Results further suggest involvement of sensorimotor α in the processing of facial expressions, including affect. The sensorimotor face area may be critical for perception of emotional face expression, but the role it plays is unclear. The present study sought to clarify how oscillatory brain activity contributes to or reflects processing of facial affect during changes in facial expression. Neuromagnetic oscillatory brain activity was monitored while 30 volunteers viewed videos of human faces that changed their expression from neutral to fearful, neutral, or happy expressions. Induced changes in α power during the different morphs, source analysis, and graph-theoretic metrics served to identify the role of α power modulation and cross-regional coupling by means of phase synchrony during facial affect recognition. Changes from neutral to emotional faces were associated with a 10-15 Hz power increase localized in bilateral sensorimotor areas, together with occipital power decrease, preceding reported emotional expression recognition. Graph-theoretic analysis revealed that, in the course of a trial, the balance between sensorimotor power increase and decrease was associated with decreased and increased transregional connectedness as measured by node degree. Results suggest that modulations in α power facilitate early registration, with sensorimotor cortex including the sensorimotor face area largely functionally decoupled and thereby protected from additional, disruptive input and that subsequent α power decrease together with increased connectedness of sensorimotor areas facilitates successful facial affect recognition.

Developmental differences in episodic memory across school ages: evidence from enacted events performed by self and others.

PubMed

Badinlou, Farzaneh; Kormi-Nouri, Reza; Mousavi Nasab, S M Hossein; Knopf, Monika

2017-01-01

The aim of this study was to examine action memory as a form of episodic memory among school-aged subjects. Most research on action memory has focused on memory changes in adult populations. This study explored the action memory of children over time. A total of 410 school-aged child participants, comprising 201 girls and 208 boys in four age groups (8, 10, 12, and 14), were included in this study. We studied two forms of action encoding, subject-performed tasks (SPTs) and experimenter-performed tasks (EPTs), which were compared with one verbal encoding task as a control condition. At retrieval, we used three memory tests (free recall, cued recall, and recognition). We observed significant differences in memory performance in children aged 8-14 years with respect to free recall and cued recall but not recognition. The largest memory enhancement was observed for the SPTs in the 8-14-year-old participants under all test conditions. Participants performed equally well on the free recall of SPTs and EPTs, whereas they displayed better performances on the cued recall and recognition of SPTs compared to EPTs. The strategic nature of SPTs and the distinction between item-specific information and relational information are discussed.

78 FR 48907 - Intertek Testing Services NA, Inc.: Grant of Expansion of Recognition and Request To Remove a...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-12

... DEPARTMENT OF LABOR Occupational Safety and Health Administration [Docket No. OSHA-2007-0039... Recognition AGENCY: Occupational Safety and Health Administration (OSHA), Labor. ACTION: Notice. SUMMARY: This notice announces the Occupational Safety and Health Administration's final decision expanding the scope...

Context and Spoken Word Recognition in a Novel Lexicon

ERIC Educational Resources Information Center

Revill, Kathleen Pirog; Tanenhaus, Michael K.; Aslin, Richard N.

2008-01-01

Three eye movement studies with novel lexicons investigated the role of semantic context in spoken word recognition, contrasting 3 models: restrictive access, access-selection, and continuous integration. Actions directed at novel shapes caused changes in motion (e.g., looming, spinning) or state (e.g., color, texture). Across the experiments,…

77 FR 27125 - Periodicals-Recognition of Distribution of Periodicals via Electronic Copies

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-09

... Electronic Copies AGENCY: Postal Service\\TM\\. ACTION: Final rule. SUMMARY: The Postal Service will revise the... limited reporting of electronic copies of Periodicals publications to satisfy the circulation standards...--Recognition of Distribution of Periodicals via Electronic Copies (77 FR 5470-5471) revising DMM 707.6 by...

78 FR 32472 - Intertek Testing Services NA, Inc.: Application for Expansion of Recognition and Request To...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-30

... DEPARTMENT OF LABOR Occupational Safety and Health Administration [Docket No. OSHA-2007-0039... Condition of Recognition AGENCY: Occupational Safety and Health Administration (OSHA), Labor. ACTION: Notice. SUMMARY: In this notice, OSHA announces the application of Intertek Testing Services NA, Inc. for...

Memory for positive, negative and neutral events in younger and older adults: Does emotion influence binding in event memory?

PubMed

Earles, Julie L; Kersten, Alan W; Vernon, Laura L; Starkings, Rachel

2016-01-01

When remembering an event, it is important to remember both the features of the event (e.g., a person and an action) and the connections among features (e.g., who performed which action). Emotion often enhances memory for stimulus features, but the relationship between emotion and the binding of features in memory is unclear. Younger and older adults attempted to remember events in which a person performed a negative, positive or neutral action. Memory for the action was enhanced by emotion, but emotion did not enhance the ability of participants to remember which person performed which action. Older adults were more likely than younger adults to make binding errors in which they incorrectly remembered a familiar actor performing a familiar action that had actually been performed by someone else, and this age-related associative deficit was found for both neutral and emotional actions. Emotion not only increased correct recognition of old events for older and younger adults but also increased false recognition of events in which a familiar actor performed a familiar action that had been performed by someone else. Thus, although emotion may enhance memory for the features of an event, it does not increase the accuracy of remembering who performed which action.

Multiview fusion for activity recognition using deep neural networks

NASA Astrophysics Data System (ADS)

Kavi, Rahul; Kulathumani, Vinod; Rohit, Fnu; Kecojevic, Vlad

2016-07-01

Convolutional neural networks (ConvNets) coupled with long short term memory (LSTM) networks have been recently shown to be effective for video classification as they combine the automatic feature extraction capabilities of a neural network with additional memory in the temporal domain. This paper shows how multiview fusion can be applied to such a ConvNet LSTM architecture. Two different fusion techniques are presented. The system is first evaluated in the context of a driver activity recognition system using data collected in a multicamera driving simulator. These results show significant improvement in accuracy with multiview fusion and also show that deep learning performs better than a traditional approach using spatiotemporal features even without requiring any background subtraction. The system is also validated on another publicly available multiview action recognition dataset that has 12 action classes and 8 camera views.

Younger and Older Users’ Recognition of Virtual Agent Facial Expressions

PubMed Central

Beer, Jenay M.; Smarr, Cory-Ann; Fisk, Arthur D.; Rogers, Wendy A.

2015-01-01

As technology advances, robots and virtual agents will be introduced into the home and healthcare settings to assist individuals, both young and old, with everyday living tasks. Understanding how users recognize an agent’s social cues is therefore imperative, especially in social interactions. Facial expression, in particular, is one of the most common non-verbal cues used to display and communicate emotion in on-screen agents (Cassell, Sullivan, Prevost, & Churchill, 2000). Age is important to consider because age-related differences in emotion recognition of human facial expression have been supported (Ruffman et al., 2008), with older adults showing a deficit for recognition of negative facial expressions. Previous work has shown that younger adults can effectively recognize facial emotions displayed by agents (Bartneck & Reichenbach, 2005; Courgeon et al. 2009; 2011; Breazeal, 2003); however, little research has compared in-depth younger and older adults’ ability to label a virtual agent’s facial emotions, an import consideration because social agents will be required to interact with users of varying ages. If such age-related differences exist for recognition of virtual agent facial expressions, we aim to understand if those age-related differences are influenced by the intensity of the emotion, dynamic formation of emotion (i.e., a neutral expression developing into an expression of emotion through motion), or the type of virtual character differing by human-likeness. Study 1 investigated the relationship between age-related differences, the implication of dynamic formation of emotion, and the role of emotion intensity in emotion recognition of the facial expressions of a virtual agent (iCat). Study 2 examined age-related differences in recognition expressed by three types of virtual characters differing by human-likeness (non-humanoid iCat, synthetic human, and human). Study 2 also investigated the role of configural and featural processing as a possible explanation for age-related differences in emotion recognition. First, our findings show age-related differences in the recognition of emotions expressed by a virtual agent, with older adults showing lower recognition for the emotions of anger, disgust, fear, happiness, sadness, and neutral. These age-related difference might be explained by older adults having difficulty discriminating similarity in configural arrangement of facial features for certain emotions; for example, older adults often mislabeled the similar emotions of fear as surprise. Second, our results did not provide evidence for the dynamic formation improving emotion recognition; but, in general, the intensity of the emotion improved recognition. Lastly, we learned that emotion recognition, for older and younger adults, differed by character type, from best to worst: human, synthetic human, and then iCat. Our findings provide guidance for design, as well as the development of a framework of age-related differences in emotion recognition. PMID:25705105

The species recognition system: a new corollary for the human fetoembryonic defense system hypothesis.

PubMed

Clark, G F; Dell, A; Morris, H R; Patankar, M S; Easton, R L

2001-01-01

We have previously suggested that the human fetus is protected during human development by a system of both soluble and cell surface associated glycoconjugates that utilize their carbohydrate sequences as functional groups to enable them to evoke tolerance. The proposed model has been referred to as the human fetoembryonic defense system hypothesis (hu-FEDS). In this paradigm, it has previously been proposed that similar oligosaccharides are used to mediate crucial recognition events required during both human sperm-egg binding and immune-inflammatory cell interactions. This vertical integration suggested to us that the sperm-egg binding itself is related to universal recognition events that occur between immune and inflammatory cells, except that in this case recognition of 'species' rather than recognition of 'self' is being manifested. In this paper, we have designated this component of hu-FEDS as the species recognition system (SRS). We propose that the SRS is an integral component of the hu-FEDS used to enable sperm-egg recognition and protection of the gametes from potential immune responses. Recent structural data indicates that the glycan sequences implicated in mediating murine gamete recognition are also expressed on CD45 in activated murine T lymphocytes and cytotoxic T lymphocytes. This overlap supports our contention that there is an overlap between the immune and gamete recognition systems. Therefore the hu-FEDS paradigm may be a subset of a larger model that also applies to other placental mammals. We therefore propose that the hu-FEDS model for protection should in the future be referred to as the eutherian fetoembryonic defense system hypothesis (eu-FEDS) to account for this extension. The possibility exists that the SRS component of eu-FEDS could predate eutherians and extend to all sexually reproducing organisms. Future investigation of the interactions between the immune and gamete recognition system will be required to determine the degree of overlap. Copyright 2001 S. Karger AG, Basel

Capturing specific abilities as a window into human individuality: the example of face recognition.

PubMed

Wilmer, Jeremy B; Germine, Laura; Chabris, Christopher F; Chatterjee, Garga; Gerbasi, Margaret; Nakayama, Ken

2012-01-01

Proper characterization of each individual's unique pattern of strengths and weaknesses requires good measures of diverse abilities. Here, we advocate combining our growing understanding of neural and cognitive mechanisms with modern psychometric methods in a renewed effort to capture human individuality through a consideration of specific abilities. We articulate five criteria for the isolation and measurement of specific abilities, then apply these criteria to face recognition. We cleanly dissociate face recognition from more general visual and verbal recognition. This dissociation stretches across ability as well as disability, suggesting that specific developmental face recognition deficits are a special case of a broader specificity that spans the entire spectrum of human face recognition performance. Item-by-item results from 1,471 web-tested participants, included as supplementary information, fuel item analyses, validation, norming, and item response theory (IRT) analyses of our three tests: (a) the widely used Cambridge Face Memory Test (CFMT); (b) an Abstract Art Memory Test (AAMT), and (c) a Verbal Paired-Associates Memory Test (VPMT). The availability of this data set provides a solid foundation for interpreting future scores on these tests. We argue that the allied fields of experimental psychology, cognitive neuroscience, and vision science could fuel the discovery of additional specific abilities to add to face recognition, thereby providing new perspectives on human individuality.

Gender recognition from unconstrained and articulated human body.

PubMed

Wu, Qin; Guo, Guodong

2014-01-01

Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition.

Gender Recognition from Unconstrained and Articulated Human Body

PubMed Central

Wu, Qin; Guo, Guodong

2014-01-01

Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition. PMID:24977203

The Joint Role of Trained, Untrained, and Observed Actions at the Origins of Goal Recognition

PubMed Central

Gerson, Sarah A.; Woodward, Amanda L.

2014-01-01

Recent findings across a variety of domains reveal the benefits of self-produced experience on object exploration, object knowledge, attention, and action perception. The influence of active experience may be particularly important in infancy, when motor development is undergoing great changes. Despite the importance of self-produced experience, we know that infants and young children are eventually able to gain knowledge through purely observational experience. In the current work, three-month-old infants were given experience with object-directed actions in one of three forms and their recognition of the goal of grasping actions was then assessed in a habituation paradigm. All infants were given the chance to manually interact with the toys without assistance (a difficult task for most three-month-olds). Two of the three groups were then given additional experience with object-directed actions, either through active training (in which Velcro mittens helped infants act more efficiently) or observational training. Findings support the conclusion that self-produced experience is uniquely informative for action perception and suggest that individual differences in spontaneous motor activity may interact with observational experience to inform action perception early in life. PMID:24468646

Face recognition in the thermal infrared domain

NASA Astrophysics Data System (ADS)

Kowalski, M.; Grudzień, A.; Palka, N.; Szustakowski, M.

2017-10-01

Biometrics refers to unique human characteristics. Each unique characteristic may be used to label and describe individuals and for automatic recognition of a person based on physiological or behavioural properties. One of the most natural and the most popular biometric trait is a face. The most common research methods on face recognition are based on visible light. State-of-the-art face recognition systems operating in the visible light spectrum achieve very high level of recognition accuracy under controlled environmental conditions. Thermal infrared imagery seems to be a promising alternative or complement to visible range imaging due to its relatively high resistance to illumination changes. A thermal infrared image of the human face presents its unique heat-signature and can be used for recognition. The characteristics of thermal images maintain advantages over visible light images, and can be used to improve algorithms of human face recognition in several aspects. Mid-wavelength or far-wavelength infrared also referred to as thermal infrared seems to be promising alternatives. We present the study on 1:1 recognition in thermal infrared domain. The two approaches we are considering are stand-off face verification of non-moving person as well as stop-less face verification on-the-move. The paper presents methodology of our studies and challenges for face recognition systems in the thermal infrared domain.

Human activities recognition by head movement using partial recurrent neural network

NASA Astrophysics Data System (ADS)

Tan, Henry C. C.; Jia, Kui; De Silva, Liyanage C.

2003-06-01

Traditionally, human activities recognition has been achieved mainly by the statistical pattern recognition methods or the Hidden Markov Model (HMM). In this paper, we propose a novel use of the connectionist approach for the recognition of ten simple human activities: walking, sitting down, getting up, squatting down and standing up, in both lateral and frontal views, in an office environment. By means of tracking the head movement of the subjects over consecutive frames from a database of different color image sequences, and incorporating the Elman model of the partial recurrent neural network (RNN) that learns the sequential patterns of relative change of the head location in the images, the proposed system is able to robustly classify all the ten activities performed by unseen subjects from both sexes, of different race and physique, with a recognition rate as high as 92.5%. This demonstrates the potential of employing partial RNN to recognize complex activities in the increasingly popular human-activities-based applications.

Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance

PubMed Central

Hong, Ha; Solomon, Ethan A.; DiCarlo, James J.

2015-01-01

To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT (“face patches”) did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. SIGNIFICANCE STATEMENT We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. PMID:26424887

Know thy sound: perceiving self and others in musical contexts.

PubMed

Sevdalis, Vassilis; Keller, Peter E

2014-10-01

This review article provides a summary of the findings from empirical studies that investigated recognition of an action's agent by using music and/or other auditory information. Embodied cognition accounts ground higher cognitive functions in lower level sensorimotor functioning. Action simulation, the recruitment of an observer's motor system and its neural substrates when observing actions, has been proposed to be particularly potent for actions that are self-produced. This review examines evidence for such claims from the music domain. It covers studies in which trained or untrained individuals generated and/or perceived (musical) sounds, and were subsequently asked to identify who was the author of the sounds (e.g., the self or another individual) in immediate (online) or delayed (offline) research designs. The review is structured according to the complexity of auditory-motor information available and includes sections on: 1) simple auditory information (e.g., clapping, piano, drum sounds), 2) complex instrumental sound sequences (e.g., piano/organ performances), and 3) musical information embedded within audiovisual performance contexts, when action sequences are both viewed as movements and/or listened to in synchrony with sounds (e.g., conductors' gestures, dance). This work has proven to be informative in unraveling the links between perceptual-motor processes, supporting embodied accounts of human cognition that address action observation. The reported findings are examined in relation to cues that contribute to agency judgments, and their implications for research concerning action understanding and applied musical practice. Copyright © 2014 Elsevier B.V. All rights reserved.

Morphological and population genomic evidence that human faces have evolved to signal individual identity.

PubMed

Sheehan, Michael J; Nachman, Michael W

2014-09-16

Facial recognition plays a key role in human interactions, and there has been great interest in understanding the evolution of human abilities for individual recognition and tracking social relationships. Individual recognition requires sufficient cognitive abilities and phenotypic diversity within a population for discrimination to be possible. Despite the importance of facial recognition in humans, the evolution of facial identity has received little attention. Here we demonstrate that faces evolved to signal individual identity under negative frequency-dependent selection. Faces show elevated phenotypic variation and lower between-trait correlations compared with other traits. Regions surrounding face-associated single nucleotide polymorphisms show elevated diversity consistent with frequency-dependent selection. Genetic variation maintained by identity signalling tends to be shared across populations and, for some loci, predates the origin of Homo sapiens. Studies of human social evolution tend to emphasize cognitive adaptations, but we show that social evolution has shaped patterns of human phenotypic and genetic diversity as well.

Recognition Memory and Source Memory in Autism Spectrum Disorder: A Study of the Intention Superiority and Enactment Effects

ERIC Educational Resources Information Center

Grainger, Catherine; Williams, David M.; Lind, Sophie E.

2017-01-01

It is well established that neurotypical individuals generally show better memory for actions they have performed than actions they have observed others perform or merely read about, a so-called "enactment effect." Strikingly, research has also shown that neurotypical individuals demonstrate superior memory for actions they…

Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring.

PubMed

Ghose, Soumya; Mitra, Jhimli; Karunanithi, Mohan; Dowling, Jason

2015-01-01

Home monitoring of chronically ill or elderly patient can reduce frequent hospitalisations and hence provide improved quality of care at a reduced cost to the community, therefore reducing the burden on the healthcare system. Activity recognition of such patients is of high importance in such a design. In this work, a system for automatic human physical activity recognition from smart-phone inertial sensors data is proposed. An ensemble of decision trees framework is adopted to train and predict the multi-class human activity system. A comparison of our proposed method with a multi-class traditional support vector machine shows significant improvement in activity recognition accuracies.

Post interaural neural net-based vowel recognition

NASA Astrophysics Data System (ADS)

Jouny, Ismail I.

2001-10-01

Interaural head related transfer functions are used to process speech signatures prior to neural net based recognition. Data representing the head related transfer function of a dummy has been collected at MIT and made available on the Internet. This data is used to pre-process vowel signatures to mimic the effects of human ear on speech perception. Signatures representing various vowels of the English language are then presented to a multi-layer perceptron trained using the back propagation algorithm for recognition purposes. The focus in this paper is to assess the effects of human interaural system on vowel recognition performance particularly when using a classification system that mimics the human brain such as a neural net.

Movement Contributes to Infants' Recognition of the Human Form

ERIC Educational Resources Information Center

Christie, Tamara; Slaughter, Virginia

2010-01-01

Three experiments demonstrate that biological movement facilitates young infants' recognition of the whole human form. A body discrimination task was used in which 6-, 9-, and 12-month-old infants were habituated to typical human bodies and then shown scrambled human bodies at the test. Recovery of interest to the scrambled bodies was observed in…

Animal welfare and the human-animal bond: considerations for veterinary faculty, students, and practitioners.

PubMed

Wensley, Sean P

2008-01-01

Consideration of the human-animal bond typically focuses on the benefits of companion animals to human health and well-being, but it is essential that in realizing these benefits the welfare needs of the animals, both physical and mental, are also met. Positive emotional relationships with animals are likely to increase recognition of animal sentience and so help create positive attitudes toward animals at the societal level, but, at the individual level, the animals to which humans are bonded should also benefit from the human-animal relationship. A strong human-animal bond may benefit animal welfare (e.g., by motivating an owner to commit time and funds to necessary veterinary medical treatment), but may also be the source of compromised welfare. Highly bonded owners may, for example, be reluctant to permit euthanasia on humane grounds, and the anthropomorphic nature of many human-companion animal bonds can contribute to the development of problem behaviors and obesity. The challenge for the veterinary profession is to ensure that widespread positive sentiment toward animals, which the human-animal bond generates, is translated in to human behavior and actions that are conducive to good animal welfare. This, it is suggested, can be achieved through adequate veterinary education in veterinary and animal welfare science, ethics, and communication.

Achieving Our Potential: An Action Plan for Prior Learning Assessment and Recognition (PLAR) in Canada

ERIC Educational Resources Information Center

Morrissey, Mary; Myers, Douglas; Belanger, Paul; Robitaille, Magali; Davison, Phil; Van Kleef, Joy; Williams, Rick

2008-01-01

This comprehensive publication assesses the status of prior learning assessment and recognition (PLAR) across Canada and offers insights and recommendations into the processes necessary for employers, post-secondary institutions and government to recognize and value experiential and informal learning. Acknowledging economic trends in Canada's job…

Between Private and Public: Recognition, Revolution and Political Renewal

ERIC Educational Resources Information Center

Stillwaggon, James

2011-01-01

This paper deals with some issues underlying the role of education in the preparation of students for democratic participation. Throughout, I maintain two basic ideas: first, that a political action undertaken to obtain practical ends reflects a set of privately held values whose recognition is therefore essential to any idea of the political;…

Recognition Memory for Movement in Photographs: A Developmental Study.

ERIC Educational Resources Information Center

Futterweit, Lorelle R.; Beilin, Harry

1994-01-01

Investigated whether children's recognition memory for movement in photographs is distorted forward in the direction of implied motion. When asked whether the second photograph was the same as or different from the first, subjects made more errors for test photographs showing the action slightly forward in time, compared with slightly backward in…

78 FR 58865 - National POW/MIA Recognition Day, 2013

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-25

..., our veterans, our military families, and all those who placed themselves in harm's way to sustain the... symbolizing America's Missing in Action and Prisoners of War will be flown over the White House; the United... National POW/MIA Recognition Day, 2013 By the President of the United States of America A Proclamation Our...

Comparison of Object Recognition Behavior in Human and Monkey

PubMed Central

Rajalingham, Rishi; Schmidt, Kailyn

2015-01-01

Although the rhesus monkey is used widely as an animal model of human visual processing, it is not known whether invariant visual object recognition behavior is quantitatively comparable across monkeys and humans. To address this question, we systematically compared the core object recognition behavior of two monkeys with that of human subjects. To test true object recognition behavior (rather than image matching), we generated several thousand naturalistic synthetic images of 24 basic-level objects with high variation in viewing parameters and image background. Monkeys were trained to perform binary object recognition tasks on a match-to-sample paradigm. Data from 605 human subjects performing the same tasks on Mechanical Turk were aggregated to characterize “pooled human” object recognition behavior, as well as 33 separate Mechanical Turk subjects to characterize individual human subject behavior. Our results show that monkeys learn each new object in a few days, after which they not only match mean human performance but show a pattern of object confusion that is highly correlated with pooled human confusion patterns and is statistically indistinguishable from individual human subjects. Importantly, this shared human and monkey pattern of 3D object confusion is not shared with low-level visual representations (pixels, V1+; models of the retina and primary visual cortex) but is shared with a state-of-the-art computer vision feature representation. Together, these results are consistent with the hypothesis that rhesus monkeys and humans share a common neural shape representation that directly supports object perception. SIGNIFICANCE STATEMENT To date, several mammalian species have shown promise as animal models for studying the neural mechanisms underlying high-level visual processing in humans. In light of this diversity, making tight comparisons between nonhuman and human primates is particularly critical in determining the best use of nonhuman primates to further the goal of the field of translating knowledge gained from animal models to humans. To the best of our knowledge, this study is the first systematic attempt at comparing a high-level visual behavior of humans and macaque monkeys. PMID:26338324

Word-to-picture recognition is a function of motor components mappings at the stage of retrieval.

PubMed

Brouillet, Denis; Brouillet, Thibaut; Milhau, Audrey; Heurley, Loïc; Vagnot, Caroline; Brunel, Lionel

2016-10-01

Embodied approaches of cognition argue that retrieval involves the re-enactment of both sensory and motor components of the desired remembering. In this study, we investigated the effect of motor action performed to produce the response in a recognition task when this action is compatible with the affordance of the objects that have to be recognised. In our experiment, participants were first asked to learn a list of words referring to graspable objects, and then told to make recognition judgements on pictures. The pictures represented objects where the graspable part was either pointing to the same or to the opposite side of the "Yes" response key. Results show a robust effect of compatibility between objects affordance and response hand. Moreover, this compatibility improves participants' ability of discrimination, suggesting that motor components are relevant cue for memory judgement at the stage of retrieval in a recognition task. More broadly, our data highlight that memory judgements are a function of motor components mappings at the stage of retrieval. © 2015 International Union of Psychological Science.

Towards a Computational Comparative Neuroprimatology: Framing the language-ready brain.

PubMed

Arbib, Michael A

2016-03-01

We make the case for developing a Computational Comparative Neuroprimatology to inform the analysis of the function and evolution of the human brain. First, we update the mirror system hypothesis on the evolution of the language-ready brain by (i) modeling action and action recognition and opportunistic scheduling of macaque brains to hypothesize the nature of the last common ancestor of macaque and human (LCA-m); and then we (ii) introduce dynamic brain modeling to show how apes could acquire gesture through ontogenetic ritualization, hypothesizing the nature of evolution from LCA-m to the last common ancestor of chimpanzee and human (LCA-c). We then (iii) hypothesize the role of imitation, pantomime, protosign and protospeech in biological and cultural evolution from LCA-c to Homo sapiens with a language-ready brain. Second, we suggest how cultural evolution in Homo sapiens led from protolanguages to full languages with grammar and compositional semantics. Third, we assess the similarities and differences between the dorsal and ventral streams in audition and vision as the basis for presenting and comparing two models of language processing in the human brain: A model of (i) the auditory dorsal and ventral streams in sentence comprehension; and (ii) the visual dorsal and ventral streams in defining "what language is about" in both production and perception of utterances related to visual scenes provide the basis for (iii) a first step towards a synthesis and a look at challenges for further research. Copyright © 2015 Elsevier B.V. All rights reserved.

Motor-auditory-visual integration: The role of the human mirror neuron system in communication and communication disorders.

PubMed

Le Bel, Ronald M; Pineda, Jaime A; Sharma, Anu

2009-01-01

The mirror neuron system (MNS) is a trimodal system composed of neuronal populations that respond to motor, visual, and auditory stimulation, such as when an action is performed, observed, heard or read about. In humans, the MNS has been identified using neuroimaging techniques (such as fMRI and mu suppression in the EEG). It reflects an integration of motor-auditory-visual information processing related to aspects of language learning including action understanding and recognition. Such integration may also form the basis for language-related constructs such as theory of mind. In this article, we review the MNS system as it relates to the cognitive development of language in typically developing children and in children at-risk for communication disorders, such as children with autism spectrum disorder (ASD) or hearing impairment. Studying MNS development in these children may help illuminate an important role of the MNS in children with communication disorders. Studies with deaf children are especially important because they offer potential insights into how the MNS is reorganized when one modality, such as audition, is deprived during early cognitive development, and this may have long-term consequences on language maturation and theory of mind abilities. Readers will be able to (1) understand the concept of mirror neurons, (2) identify cortical areas associated with the MNS in animal and human studies, (3) discuss the use of mu suppression in the EEG for measuring the MNS in humans, and (4) discuss MNS dysfunction in children with (ASD).

Towards a Computational Comparative Neuroprimatology: Framing the language-ready brain

NASA Astrophysics Data System (ADS)

Arbib, Michael A.

2016-03-01

We make the case for developing a Computational Comparative Neuroprimatology to inform the analysis of the function and evolution of the human brain. First, we update the mirror system hypothesis on the evolution of the language-ready brain by (i) modeling action and action recognition and opportunistic scheduling of macaque brains to hypothesize the nature of the last common ancestor of macaque and human (LCA-m); and then we (ii) introduce dynamic brain modeling to show how apes could acquire gesture through ontogenetic ritualization, hypothesizing the nature of evolution from LCA-m to the last common ancestor of chimpanzee and human (LCA-c). We then (iii) hypothesize the role of imitation, pantomime, protosign and protospeech in biological and cultural evolution from LCA-c to Homo sapiens with a language-ready brain. Second, we suggest how cultural evolution in Homo sapiens led from protolanguages to full languages with grammar and compositional semantics. Third, we assess the similarities and differences between the dorsal and ventral streams in audition and vision as the basis for presenting and comparing two models of language processing in the human brain: A model of (i) the auditory dorsal and ventral streams in sentence comprehension; and (ii) the visual dorsal and ventral streams in defining ;what language is about; in both production and perception of utterances related to visual scenes provide the basis for (iii) a first step towards a synthesis and a look at challenges for further research.

Action and Emotion Recognition from Point Light Displays: An Investigation of Gender Differences

PubMed Central

Alaerts, Kaat; Nackaerts, Evelien; Meyns, Pieter; Swinnen, Stephan P.; Wenderoth, Nicole

2011-01-01

Folk psychology advocates the existence of gender differences in socio-cognitive functions such as ‘reading’ the mental states of others or discerning subtle differences in body-language. A female advantage has been demonstrated for emotion recognition from facial expressions, but virtually nothing is known about gender differences in recognizing bodily stimuli or body language. The aim of the present study was to investigate potential gender differences in a series of tasks, involving the recognition of distinct features from point light displays (PLDs) depicting bodily movements of a male and female actor. Although recognition scores were considerably high at the overall group level, female participants were more accurate than males in recognizing the depicted actions from PLDs. Response times were significantly higher for males compared to females on PLD recognition tasks involving (i) the general recognition of ‘biological motion’ versus ‘non-biological’ (or ‘scrambled’ motion); or (ii) the recognition of the ‘emotional state’ of the PLD-figures. No gender differences were revealed for a control test (involving the identification of a color change in one of the dots) and for recognizing the gender of the PLD-figure. In addition, previous findings of a female advantage on a facial emotion recognition test (the ‘Reading the Mind in the Eyes Test’ (Baron-Cohen, 2001)) were replicated in this study. Interestingly, a strong correlation was revealed between emotion recognition from bodily PLDs versus facial cues. This relationship indicates that inter-individual or gender-dependent differences in recognizing emotions are relatively generalized across facial and bodily emotion perception. Moreover, the tight correlation between a subject's ability to discern subtle emotional cues from PLDs and the subject's ability to basically discriminate biological from non-biological motion provides indications that differences in emotion recognition may - at least to some degree – be related to more basic differences in processing biological motion per se. PMID:21695266

Action and emotion recognition from point light displays: an investigation of gender differences.

PubMed

Alaerts, Kaat; Nackaerts, Evelien; Meyns, Pieter; Swinnen, Stephan P; Wenderoth, Nicole

2011-01-01

Folk psychology advocates the existence of gender differences in socio-cognitive functions such as 'reading' the mental states of others or discerning subtle differences in body-language. A female advantage has been demonstrated for emotion recognition from facial expressions, but virtually nothing is known about gender differences in recognizing bodily stimuli or body language. The aim of the present study was to investigate potential gender differences in a series of tasks, involving the recognition of distinct features from point light displays (PLDs) depicting bodily movements of a male and female actor. Although recognition scores were considerably high at the overall group level, female participants were more accurate than males in recognizing the depicted actions from PLDs. Response times were significantly higher for males compared to females on PLD recognition tasks involving (i) the general recognition of 'biological motion' versus 'non-biological' (or 'scrambled' motion); or (ii) the recognition of the 'emotional state' of the PLD-figures. No gender differences were revealed for a control test (involving the identification of a color change in one of the dots) and for recognizing the gender of the PLD-figure. In addition, previous findings of a female advantage on a facial emotion recognition test (the 'Reading the Mind in the Eyes Test' (Baron-Cohen, 2001)) were replicated in this study. Interestingly, a strong correlation was revealed between emotion recognition from bodily PLDs versus facial cues. This relationship indicates that inter-individual or gender-dependent differences in recognizing emotions are relatively generalized across facial and bodily emotion perception. Moreover, the tight correlation between a subject's ability to discern subtle emotional cues from PLDs and the subject's ability to basically discriminate biological from non-biological motion provides indications that differences in emotion recognition may - at least to some degree - be related to more basic differences in processing biological motion per se.

Structural insights into 5‧ flap DNA unwinding and incision by the human FAN1 dimer

NASA Astrophysics Data System (ADS)

Zhao, Qi; Xue, Xiaoyu; Longerich, Simonne; Sung, Patrick; Xiong, Yong

2014-12-01

Human FANCD2-associated nuclease 1 (FAN1) is a DNA structure-specific nuclease involved in the processing of DNA interstrand crosslinks (ICLs). FAN1 maintains genomic stability and prevents tissue decline in multiple organs, yet it confers ICL-induced anti-cancer drug resistance in several cancer subtypes. Here we report three crystal structures of human FAN1 in complex with a 5‧ flap DNA substrate, showing that two FAN1 molecules form a head-to-tail dimer to locate the lesion, orient the DNA and unwind a 5‧ flap for subsequent incision. Biochemical experiments further validate our model for FAN1 action, as structure-informed mutations that disrupt protein dimerization, substrate orientation or flap unwinding impair the structure-specific nuclease activity. Our work elucidates essential aspects of FAN1-DNA lesion recognition and a unique mechanism of incision. These structural insights shed light on the cellular mechanisms underlying organ degeneration protection and cancer drug resistance mediated by FAN1.

Muscle-specific Deletion of Carnitine Acetyltransferase Compromises Glucose Tolerance and Metabolic Flexibility

PubMed Central

Muoio, Deborah M.; Noland, Robert C.; Kovalik, Jean-Paul; Seiler, Sarah E.; Davies, Michael N.; DeBalsi, Karen L.; Ilkayeva, Olga R.; Stevens, Robert D.; Kheterpal, Indu; Zhang, Jingying; Covington, Jeffrey D.; Bajpeyi, Sudip; Ravussin, Eric; Kraus, William; Koves, Timothy R.; Mynatt, Randall L.

2012-01-01

Summary The concept of “metabolic inflexibility” was first introduced to describe the failure of insulin resistant human subjects to appropriately adjust mitochondrial fuel selection in response to nutritional cues. This phenomenon has since gained increasing recognition as a core component of the metabolic syndrome, but the underlying mechanisms have remained elusive. Here, we identify an essential role for the mitochondrial matrix enzyme, carnitine acetyltransferase (CrAT), in regulating substrate switching and glucose tolerance. By converting acetyl-CoA to its membrane permeant acetylcarnitine ester, CrAT regulates mitochondrial and intracellular carbon trafficking. Studies in muscle-specific Crat knockout mice, primary human skeletal myocytes and human subjects undergoing L-carnitine supplementation support a model wherein CrAT combats nutrient stress, promotes metabolic flexibility and enhances insulin action by permitting mitochondrial efflux of excess acetyl moieties that otherwise inhibit key regulatory enzymes such as pyruvate dehydrogenase. These findings offer therapeutically relevant insights into the molecular basis of metabolic inflexibility. PMID:22560225

Muscle-specific deletion of carnitine acetyltransferase compromises glucose tolerance and metabolic flexibility.

PubMed

Muoio, Deborah M; Noland, Robert C; Kovalik, Jean-Paul; Seiler, Sarah E; Davies, Michael N; DeBalsi, Karen L; Ilkayeva, Olga R; Stevens, Robert D; Kheterpal, Indu; Zhang, Jingying; Covington, Jeffrey D; Bajpeyi, Sudip; Ravussin, Eric; Kraus, William; Koves, Timothy R; Mynatt, Randall L

2012-05-02

The concept of "metabolic inflexibility" was first introduced to describe the failure of insulin-resistant human subjects to appropriately adjust mitochondrial fuel selection in response to nutritional cues. This phenomenon has since gained increasing recognition as a core component of the metabolic syndrome, but the underlying mechanisms have remained elusive. Here, we identify an essential role for the mitochondrial matrix enzyme, carnitine acetyltransferase (CrAT), in regulating substrate switching and glucose tolerance. By converting acetyl-CoA to its membrane permeant acetylcarnitine ester, CrAT regulates mitochondrial and intracellular carbon trafficking. Studies in muscle-specific Crat knockout mice, primary human skeletal myocytes, and human subjects undergoing L-carnitine supplementation support a model wherein CrAT combats nutrient stress, promotes metabolic flexibility, and enhances insulin action by permitting mitochondrial efflux of excess acetyl moieties that otherwise inhibit key regulatory enzymes such as pyruvate dehydrogenase. These findings offer therapeutically relevant insights into the molecular basis of metabolic inflexibility. Copyright © 2012 Elsevier Inc. All rights reserved.

Implications of Animal Object Memory Research for Human Amnesia

ERIC Educational Resources Information Center

Winters, Boyer D.; Saksida, Lisa M.; Bussey, Timothy J.

2010-01-01

Damage to structures in the human medial temporal lobe causes severe memory impairment. Animal object recognition tests gained prominence from attempts to model "global" human medial temporal lobe amnesia, such as that observed in patient HM. These tasks, such as delayed nonmatching-to-sample and spontaneous object recognition, for assessing…

Arginine Vasopressin selectively enhances recognition of sexual cues in male humans.

PubMed

Guastella, Adam J; Kenyon, Amanda R; Unkelbach, Christian; Alvares, Gail A; Hickie, Ian B

2011-02-01

Arginine Vasopressin modulates complex social and sexual behavior by enhancing social recognition, pair bonding, and aggression in non-human mammals. The influence of Arginine Vasopressin in human social and sexual behavior is, however, yet to be fully understood. We evaluated whether Arginine Vasopressin nasal spray facilitated recognition of positive and negative social and sexual stimuli over non-social stimuli. We used a recognition task that has already been shown to be sensitive to the influence of Oxytocin nasal spray (Unkelbach et al., 2008). In a double-blind, randomized, placebo-controlled, between-subjects design, 41 healthy male volunteers were administered Arginine Vasopressin (20 IU) or a placebo nasal spray after a 45 min wait period and then completed the recognition task. Results showed that the participants administered Arginine Vasopressin nasal spray were faster to detect sexual words over other types of words. This effect appeared for both positively and negatively valenced words. Results demonstrate for the first time that Arginine Vasopressin selectively enhances human cognition for sexual stimuli, regardless of valence. They further extend animal and human genetic studies linking Arginine Vasopressin to sexual behavior in males. Findings suggest an important cognitive mechanism that could enhance sexual behaviors in humans. Crown Copyright © 2010. Published by Elsevier Ltd. All rights reserved.

Serious and actionable risks, plus disclosure: Investigating an alternative approach for presenting risk information in prescription drug television advertisements.

PubMed

Betts, Kevin R; Boudewyns, Vanessa; Aikin, Kathryn J; Squire, Claudia; Dolina, Suzanne; Hayes, Jennifer J; Southwell, Brian G

2017-08-02

Broadcast direct-to-consumer (DTC) prescription drug ads that present product claims are required to also present the product's major risks. Debate exists regarding how much information should be included in these major risk statements. Some argue that such statements expose people to unnecessary amounts of information, while others argue that they leave out important information. Examine the impact of type of risk statement (unedited versus serious and actionable risks only) and a disclosure indicating that not all risks are presented on consumers' ability to remember the important risks and benefits of a drug following exposure to a DTC television advertisement (ad). Risk and benefit perceptions, ad-prompted actions, recognition of the disclosure statement, and evaluations of both the disclosure and risk statement were also examined. A web-based experiment was conducted in which US adults who self-reported as having depression (N = 500), insomnia (N = 500), or high cholesterol (N = 500) were randomly assigned to view one of four versions of the television ad, and then complete a questionnaire. The type of risk statement had a significant effect on risk recall and recognition, benefit recognition, perceived risk severity (depression condition only), and perceived benefit magnitude (high cholesterol condition only). Disclosure recognition (using bias-corrected scores) ranged from 63% to 70% across the three illness samples. The revised risk statement improved overall processing of the television ad, as evidenced by improved risk recall and recognition and improved benefit recognition. Further, the presence of the disclosure did not adversely affect consumers' processing of drug risk and benefit information. Therefore, limiting the risks presented in DTC television ads and including a disclosure alerting consumers that not all risks are presented may be an effective strategy for communicating product risks. Published by Elsevier Inc.

15 CFR 310.4 - Action on application.

Code of Federal Regulations, 2014 CFR

2014-01-01

...) INTERNATIONAL TRADE ADMINISTRATION, DEPARTMENT OF COMMERCE MISCELLANEOUS REGULATIONS OFFICIAL U.S. GOVERNMENT RECOGNITION OF AND PARTICIPATION IN INTERNATIONAL EXPOSITIONS HELD IN THE UNITED STATES § 310.4 Action on... analyzing the applications, the Director may hold public hearings with the objective of clarifying issues...

Targeting RNA in mammalian systems with small molecules.

PubMed

Donlic, Anita; Hargrove, Amanda E

2018-05-03

The recognition of RNA functions beyond canonical protein synthesis has challenged the central dogma of molecular biology. Indeed, RNA is now known to directly regulate many important cellular processes, including transcription, splicing, translation, and epigenetic modifications. The misregulation of these processes in disease has led to an appreciation of RNA as a therapeutic target. This potential was first recognized in bacteria and viruses, but discoveries of new RNA classes following the sequencing of the human genome have invigorated exploration of its disease-related functions in mammals. As stable structure formation is evolving as a hallmark of mammalian RNAs, the prospect of utilizing small molecules to specifically probe the function of RNA structural domains and their interactions is gaining increased recognition. To date, researchers have discovered bioactive small molecules that modulate phenotypes by binding to expanded repeats, microRNAs, G-quadruplex structures, and RNA splice sites in neurological disorders, cancers, and other diseases. The lessons learned from achieving these successes both call for additional studies and encourage exploration of the plethora of mammalian RNAs whose precise mechanisms of action remain to be elucidated. Efforts toward understanding fundamental principles of small molecule-RNA recognition combined with advances in methodology development should pave the way toward targeting emerging RNA classes such as long noncoding RNAs. Together, these endeavors can unlock the full potential of small molecule-based probing of RNA-regulated processes and enable us to discover new biology and underexplored avenues for therapeutic intervention in human disease. This article is categorized under: RNA Methods > RNA Analyses In Vitro and In Silico RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions RNA in Disease and Development > RNA in Disease. © 2018 Wiley Periodicals, Inc.

Neural network face recognition using wavelets

NASA Astrophysics Data System (ADS)

Karunaratne, Passant V.; Jouny, Ismail I.

1997-04-01

The recognition of human faces is a phenomenon that has been mastered by the human visual system and that has been researched extensively in the domain of computer neural networks and image processing. This research is involved in the study of neural networks and wavelet image processing techniques in the application of human face recognition. The objective of the system is to acquire a digitized still image of a human face, carry out pre-processing on the image as required, an then, given a prior database of images of possible individuals, be able to recognize the individual in the image. The pre-processing segment of the system includes several procedures, namely image compression, denoising, and feature extraction. The image processing is carried out using Daubechies wavelets. Once the images have been passed through the wavelet-based image processor they can be efficiently analyzed by means of a neural network. A back- propagation neural network is used for the recognition segment of the system. The main constraints of the system is with regard to the characteristics of the images being processed. The system should be able to carry out effective recognition of the human faces irrespective of the individual's facial-expression, presence of extraneous objects such as head-gear or spectacles, and face/head orientation. A potential application of this face recognition system would be as a secondary verification method in an automated teller machine.

Capturing specific abilities as a window into human individuality: The example of face recognition

PubMed Central

Wilmer, Jeremy B.; Germine, Laura; Chabris, Christopher F.; Chatterjee, Garga; Gerbasi, Margaret; Nakayama, Ken

2013-01-01

Proper characterization of each individual's unique pattern of strengths and weaknesses requires good measures of diverse abilities. Here, we advocate combining our growing understanding of neural and cognitive mechanisms with modern psychometric methods in a renewed effort to capture human individuality through a consideration of specific abilities. We articulate five criteria for the isolation and measurement of specific abilities, then apply these criteria to face recognition. We cleanly dissociate face recognition from more general visual and verbal recognition. This dissociation stretches across ability as well as disability, suggesting that specific developmental face recognition deficits are a special case of a broader specificity that spans the entire spectrum of human face recognition performance. Item-by-item results from 1,471 web-tested participants, included as supplementary information, fuel item analyses, validation, norming, and item response theory (IRT) analyses of our three tests: (a) the widely used Cambridge Face Memory Test (CFMT); (b) an Abstract Art Memory Test (AAMT), and (c) a Verbal Paired-Associates Memory Test (VPMT). The availability of this data set provides a solid foundation for interpreting future scores on these tests. We argue that the allied fields of experimental psychology, cognitive neuroscience, and vision science could fuel the discovery of additional specific abilities to add to face recognition, thereby providing new perspectives on human individuality. PMID:23428079

Neural Mechanism for Mirrored Self-face Recognition.

PubMed

Sugiura, Motoaki; Miyauchi, Carlos Makoto; Kotozaki, Yuka; Akimoto, Yoritaka; Nozawa, Takayuki; Yomogida, Yukihito; Hanawa, Sugiko; Yamamoto, Yuki; Sakuma, Atsushi; Nakagawa, Seishu; Kawashima, Ryuta

2015-09-01

Self-face recognition in the mirror is considered to involve multiple processes that integrate 2 perceptual cues: temporal contingency of the visual feedback on one's action (contingency cue) and matching with self-face representation in long-term memory (figurative cue). The aim of this study was to examine the neural bases of these processes by manipulating 2 perceptual cues using a "virtual mirror" system. This system allowed online dynamic presentations of real-time and delayed self- or other facial actions. Perception-level processes were identified as responses to only a single perceptual cue. The effect of the contingency cue was identified in the cuneus. The regions sensitive to the figurative cue were subdivided by the response to a static self-face, which was identified in the right temporal, parietal, and frontal regions, but not in the bilateral occipitoparietal regions. Semantic- or integration-level processes, including amodal self-representation and belief validation, which allow modality-independent self-recognition and the resolution of potential conflicts between perceptual cues, respectively, were identified in distinct regions in the right frontal and insular cortices. The results are supportive of the multicomponent notion of self-recognition and suggest a critical role for contingency detection in the co-emergence of self-recognition and empathy in infants. © The Author 2014. Published by Oxford University Press.

Neural Mechanism for Mirrored Self-face Recognition

PubMed Central

Sugiura, Motoaki; Miyauchi, Carlos Makoto; Kotozaki, Yuka; Akimoto, Yoritaka; Nozawa, Takayuki; Yomogida, Yukihito; Hanawa, Sugiko; Yamamoto, Yuki; Sakuma, Atsushi; Nakagawa, Seishu; Kawashima, Ryuta

2015-01-01

Self-face recognition in the mirror is considered to involve multiple processes that integrate 2 perceptual cues: temporal contingency of the visual feedback on one's action (contingency cue) and matching with self-face representation in long-term memory (figurative cue). The aim of this study was to examine the neural bases of these processes by manipulating 2 perceptual cues using a “virtual mirror” system. This system allowed online dynamic presentations of real-time and delayed self- or other facial actions. Perception-level processes were identified as responses to only a single perceptual cue. The effect of the contingency cue was identified in the cuneus. The regions sensitive to the figurative cue were subdivided by the response to a static self-face, which was identified in the right temporal, parietal, and frontal regions, but not in the bilateral occipitoparietal regions. Semantic- or integration-level processes, including amodal self-representation and belief validation, which allow modality-independent self-recognition and the resolution of potential conflicts between perceptual cues, respectively, were identified in distinct regions in the right frontal and insular cortices. The results are supportive of the multicomponent notion of self-recognition and suggest a critical role for contingency detection in the co-emergence of self-recognition and empathy in infants. PMID:24770712

Gesture Based Control and EMG Decomposition

NASA Technical Reports Server (NTRS)

Wheeler, Kevin R.; Chang, Mindy H.; Knuth, Kevin H.

2005-01-01

This paper presents two probabilistic developments for use with Electromyograms (EMG). First described is a new-electric interface for virtual device control based on gesture recognition. The second development is a Bayesian method for decomposing EMG into individual motor unit action potentials. This more complex technique will then allow for higher resolution in separating muscle groups for gesture recognition. All examples presented rely upon sampling EMG data from a subject's forearm. The gesture based recognition uses pattern recognition software that has been trained to identify gestures from among a given set of gestures. The pattern recognition software consists of hidden Markov models which are used to recognize the gestures as they are being performed in real-time from moving averages of EMG. Two experiments were conducted to examine the feasibility of this interface technology. The first replicated a virtual joystick interface, and the second replicated a keyboard. Moving averages of EMG do not provide easy distinction between fine muscle groups. To better distinguish between different fine motor skill muscle groups we present a Bayesian algorithm to separate surface EMG into representative motor unit action potentials. The algorithm is based upon differential Variable Component Analysis (dVCA) [l], [2] which was originally developed for Electroencephalograms. The algorithm uses a simple forward model representing a mixture of motor unit action potentials as seen across multiple channels. The parameters of this model are iteratively optimized for each component. Results are presented on both synthetic and experimental EMG data. The synthetic case has additive white noise and is compared with known components. The experimental EMG data was obtained using a custom linear electrode array designed for this study.

Building words on actions: verb enactment and verb recognition in children with specific language impairment.

PubMed

Levi, Gabriel; Colonnello, Valentina; Giacchè, Roberta; Piredda, Maria Letizia; Sogos, Carla

2014-05-01

Recent studies have shown that language processing is grounded in actions. Multiple independent research findings indicate that children with specific language impairment (SLI) show subtle difficulties beyond the language domain. Uncertainties remain on possible association between body-mediated, non-linguistic expression of verbs and early manifestation of SLI during verb acquisition. The present study was conducted to determine whether verb production through non-linguistic modalities is impaired in children with SLI. Children with SLI (mean age 41 months) and typically developing children (mean age 40 months) were asked to recognize target verbs while viewing video clips showing the action associated with the verb (verb-recognition task) and to enact the action corresponding to the verb (verb-enacting task). Children with SLI performed more poorly than control children in both tasks. The present study demonstrates that early language impairment emerges at the bodily level. These findings are consistent with the embodied theories of cognition and underscore the role of action-based representations during language development. Copyright © 2014 Elsevier Ltd. All rights reserved.

Combining heterogenous features for 3D hand-held object recognition

NASA Astrophysics Data System (ADS)

Lv, Xiong; Wang, Shuang; Li, Xiangyang; Jiang, Shuqiang

2014-10-01

Object recognition has wide applications in the area of human-machine interaction and multimedia retrieval. However, due to the problem of visual polysemous and concept polymorphism, it is still a great challenge to obtain reliable recognition result for the 2D images. Recently, with the emergence and easy availability of RGB-D equipment such as Kinect, this challenge could be relieved because the depth channel could bring more information. A very special and important case of object recognition is hand-held object recognition, as hand is a straight and natural way for both human-human interaction and human-machine interaction. In this paper, we study the problem of 3D object recognition by combining heterogenous features with different modalities and extraction techniques. For hand-craft feature, although it reserves the low-level information such as shape and color, it has shown weakness in representing hiconvolutionalgh-level semantic information compared with the automatic learned feature, especially deep feature. Deep feature has shown its great advantages in large scale dataset recognition but is not always robust to rotation or scale variance compared with hand-craft feature. In this paper, we propose a method to combine hand-craft point cloud features and deep learned features in RGB and depth channle. First, hand-held object segmentation is implemented by using depth cues and human skeleton information. Second, we combine the extracted hetegerogenous 3D features in different stages using linear concatenation and multiple kernel learning (MKL). Then a training model is used to recognize 3D handheld objects. Experimental results validate the effectiveness and gerneralization ability of the proposed method.

Track-based event recognition in a realistic crowded environment

NASA Astrophysics Data System (ADS)

van Huis, Jasper R.; Bouma, Henri; Baan, Jan; Burghouts, Gertjan J.; Eendebak, Pieter T.; den Hollander, Richard J. M.; Dijk, Judith; van Rest, Jeroen H.

2014-10-01

Automatic detection of abnormal behavior in CCTV cameras is important to improve the security in crowded environments, such as shopping malls, airports and railway stations. This behavior can be characterized at different time scales, e.g., by small-scale subtle and obvious actions or by large-scale walking patterns and interactions between people. For example, pickpocketing can be recognized by the actual snatch (small scale), when he follows the victim, or when he interacts with an accomplice before and after the incident (longer time scale). This paper focusses on event recognition by detecting large-scale track-based patterns. Our event recognition method consists of several steps: pedestrian detection, object tracking, track-based feature computation and rule-based event classification. In the experiment, we focused on single track actions (walk, run, loiter, stop, turn) and track interactions (pass, meet, merge, split). The experiment includes a controlled setup, where 10 actors perform these actions. The method is also applied to all tracks that are generated in a crowded shopping mall in a selected time frame. The results show that most of the actions can be detected reliably (on average 90%) at a low false positive rate (1.1%), and that the interactions obtain lower detection rates (70% at 0.3% FP). This method may become one of the components that assists operators to find threatening behavior and enrich the selection of videos that are to be observed.

Attentional biases and memory for emotional stimuli in men and male rhesus monkeys.

PubMed

Lacreuse, Agnès; Schatz, Kelly; Strazzullo, Sarah; King, Hanna M; Ready, Rebecca

2013-11-01

We examined attentional biases for social and non-social emotional stimuli in young adult men and compared the results to those of male rhesus monkeys (Macaca mulatta) previously tested in a similar dot-probe task (King et al. in Psychoneuroendocrinology 37(3):396-409, 2012). Recognition memory for these stimuli was also analyzed in each species, using a recognition memory task in humans and a delayed non-matching-to-sample task in monkeys. We found that both humans and monkeys displayed a similar pattern of attentional biases toward threatening facial expressions of conspecifics. The bias was significant in monkeys and of marginal significance in humans. In addition, humans, but not monkeys, exhibited an attentional bias away from negative non-social images. Attentional biases for social and non-social threat differed significantly, with both species showing a pattern of vigilance toward negative social images and avoidance of negative non-social images. Positive stimuli did not elicit significant attentional biases for either species. In humans, emotional content facilitated the recognition of non-social images, but no effect of emotion was found for the recognition of social images. Recognition accuracy was not affected by emotion in monkeys, but response times were faster for negative relative to positive images. Altogether, these results suggest shared mechanisms of social attention in humans and monkeys, with both species showing a pattern of selective attention toward threatening faces of conspecifics. These data are consistent with the view that selective vigilance to social threat is the result of evolutionary constraints. Yet, selective attention to threat was weaker in humans than in monkeys, suggesting that regulatory mechanisms enable non-anxious humans to reduce sensitivity to social threat in this paradigm, likely through enhanced prefrontal control and reduced amygdala activation. In addition, the findings emphasize important differences in attentional biases to social versus non-social threat in both species. Differences in the impact of emotional stimuli on recognition memory between monkeys and humans will require further study, as methodological differences in the recognition tasks may have affected the results.

Action Bank: A High Level Representation of Activity in Video (Author’s Manuscript)

DTIC Science & Technology

2012-07-26

of highly discriminative performance. We have tested action bank on four major activity recognition benchmarks. In all cases, our perfor- mance is...that seek a more semantically rich and discriminative Bank of Action Detectors View 1 View 2 View n Biking Javelin Jump Rope Fencing Input Video...Positive: jumping, throwing , running, ... Negative: biking, fencing, drumming, ... Figure 1. Action bank is a high-level representation for video ac

Computer Recognition of Facial Profiles

DTIC Science & Technology

1974-08-01

facial recognition 20. ABSTRACT (Continue on reverse side It necessary and Identify by block number) A system for the recognition of human faces from...21 2.6 Classification Algorithms ........... ... 32 III FACIAL RECOGNITION AND AUTOMATIC TRAINING . . . 37 3.1 Facial Profile Recognition...provide a fair test of the classification system. The work of Goldstein, Harmon, and Lesk [81 indicates, however, that for facial recognition , a ten class

Intelligent Vision On The SM9O Mini-Computer Basis And Applications

NASA Astrophysics Data System (ADS)

Hawryszkiw, J.

1985-02-01

Distinction has to be made between image processing and vision Image processing finds its roots in the strong tradition of linear signal processing and promotes geometrical transform techniques, such as fi I tering , compression, and restoration. Its purpose is to transform an image for a human observer to easily extract from that image information significant for him. For example edges after a gradient operator, or a specific direction after a directional filtering operation. Image processing consists in fact in a set of local or global space-time transforms. The interpretation of the final image is done by the human observer. The purpose of vision is to extract the semantic content of the image. The machine can then understand that content, and run a process of decision, which turns into an action. Thus, intel I i gent vision depends on - Image processing - Pattern recognition - Artificial intel I igence

The Human Splicing Factor ASF/SF2 can Specifically Recognize Pre-mRNA 5' Splice Sites

NASA Astrophysics Data System (ADS)

Zuo, Ping; Manley, James L.

1994-04-01

ASF/SF2 is a human protein previously shown to function in in vitro pre-mRNA splicing as an essential factor necessary for all splices and also as an alternative splicing factor, capable of switching selection of 5' splice sites. To begin to study the protein's mechanism of action, we have investigated the RNA binding properties of purified recombinant ASF/SF2. Using UV crosslinking and gel shift assays, we demonstrate that the RNA binding region of ASF/SF2 can interact with RNA in a sequence-specific manner, recognizing the 5' splice site in each of two different pre-mRNAs. Point mutations in the 5' splice site consensus can reduce binding by as much as a factor of 100, with the largest effects observed in competition assays. These findings support a model in which ASF/SF2 aids in the recognition of pre-mRNA 5' splice sites.

Transfer Learning for Activity Recognition: A Survey

PubMed Central

Cook, Diane; Feuz, Kyle D.; Krishnan, Narayanan C.

2013-01-01

Many intelligent systems that focus on the needs of a human require information about the activities being performed by the human. At the core of this capability is activity recognition, which is a challenging and well-researched problem. Activity recognition algorithms require substantial amounts of labeled training data yet need to perform well under very diverse circumstances. As a result, researchers have been designing methods to identify and utilize subtle connections between activity recognition datasets, or to perform transfer-based activity recognition. In this paper we survey the literature to highlight recent advances in transfer learning for activity recognition. We characterize existing approaches to transfer-based activity recognition by sensor modality, by differences between source and target environments, by data availability, and by type of information that is transferred. Finally, we present some grand challenges for the community to consider as this field is further developed. PMID:24039326

Valuing autonomy, struggling for an identity and a collective voice, and seeking role recognition: community mental health nurses' perceptions of their roles.

PubMed

White, Jane H; Kudless, Mary

2008-10-01

Leaders in this community mental health system approached the problem of job frustration, morale issues, and turnover concerns of their Community Mental Health Nurses (CMHNs) by designing a qualitative study using Participant Action Research (PAR) methodology based on the philosophy of Habermas. Six focus groups were conducted to address the nurses' concerns. The themes of Valuing Autonomy, Struggling for an Identity and Collective Voice, and Seeking Role Recognition best explained the participants' concerns. The study concluded with an action plan, the implementation of the plan, and a discussion of the plan's final outcomes.

Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

PubMed Central

Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

2016-01-01

The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain. PMID:27282108

Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence.

PubMed

Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

2016-06-10

The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain.

Toward a science of morality: response to Christian Miller's critique.

PubMed

Shermer, Michael

2016-11-01

In developing a science of morality, many examples are provided not only to document the moral progress that has been made over the centuries, but also the reasons why and how this progress has been made. Instead of moralizing about human action and social problems that we find deplorable or undesirable, ever since the Scientific Revolution and the Enlightenment we started seeing them as problems to be solved. A science of morality begins with the discovery of rights and other moral values and emotions, starting with the recognition of the individual as an autonomous moral agent with an evolved natural desire to survive and flourish. © 2016 New York Academy of Sciences.

Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance.

PubMed

Majaj, Najib J; Hong, Ha; Solomon, Ethan A; DiCarlo, James J

2015-09-30

To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT ("face patches") did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. Significance statement: We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. Copyright © 2015 the authors 0270-6474/15/3513402-17$15.00/0.

Course of Relational and Non-Relational Recognition Memory across the Adult Lifespan

ERIC Educational Resources Information Center

Soei, Eleonore; Daum, Irene

2008-01-01

Human recognition memory shows a decline during normal ageing, which is thought to be related to age-associated dysfunctions of mediotemporal lobe structures. Whether the hippocampus is critical for human general relational memory or for spatial relational memory only is still disputed. The human perirhinal cortex is thought to be critically…

Face Recognition Is Shaped by the Use of Sign Language

ERIC Educational Resources Information Center

Stoll, Chloé; Palluel-Germain, Richard; Caldara, Roberto; Lao, Junpeng; Dye, Matthew W. G.; Aptel, Florent; Pascalis, Olivier

2018-01-01

Previous research has suggested that early deaf signers differ in face processing. Which aspects of face processing are changed and the role that sign language may have played in that change are however unclear. Here, we compared face categorization (human/non-human) and human face recognition performance in early profoundly deaf signers, hearing…

Multimodal approaches for emotion recognition: a survey

NASA Astrophysics Data System (ADS)

Sebe, Nicu; Cohen, Ira; Gevers, Theo; Huang, Thomas S.

2004-12-01

Recent technological advances have enabled human users to interact with computers in ways previously unimaginable. Beyond the confines of the keyboard and mouse, new modalities for human-computer interaction such as voice, gesture, and force-feedback are emerging. Despite important advances, one necessary ingredient for natural interaction is still missing-emotions. Emotions play an important role in human-to-human communication and interaction, allowing people to express themselves beyond the verbal domain. The ability to understand human emotions is desirable for the computer in several applications. This paper explores new ways of human-computer interaction that enable the computer to be more aware of the user's emotional and attentional expressions. We present the basic research in the field and the recent advances into the emotion recognition from facial, voice, and physiological signals, where the different modalities are treated independently. We then describe the challenging problem of multimodal emotion recognition and we advocate the use of probabilistic graphical models when fusing the different modalities. We also discuss the difficult issues of obtaining reliable affective data, obtaining ground truth for emotion recognition, and the use of unlabeled data.

Multimodal approaches for emotion recognition: a survey

NASA Astrophysics Data System (ADS)

Sebe, Nicu; Cohen, Ira; Gevers, Theo; Huang, Thomas S.

2005-01-01

Recent technological advances have enabled human users to interact with computers in ways previously unimaginable. Beyond the confines of the keyboard and mouse, new modalities for human-computer interaction such as voice, gesture, and force-feedback are emerging. Despite important advances, one necessary ingredient for natural interaction is still missing-emotions. Emotions play an important role in human-to-human communication and interaction, allowing people to express themselves beyond the verbal domain. The ability to understand human emotions is desirable for the computer in several applications. This paper explores new ways of human-computer interaction that enable the computer to be more aware of the user's emotional and attentional expressions. We present the basic research in the field and the recent advances into the emotion recognition from facial, voice, and physiological signals, where the different modalities are treated independently. We then describe the challenging problem of multimodal emotion recognition and we advocate the use of probabilistic graphical models when fusing the different modalities. We also discuss the difficult issues of obtaining reliable affective data, obtaining ground truth for emotion recognition, and the use of unlabeled data.

Recognition and Mental Manipulation of Body Parts Dissociate in Locked-In Syndrome

ERIC Educational Resources Information Center

Conson, Massimiliano; Pistoia, Francesca; Sara, Marco; Grossi, Dario; Trojano, Luigi

2010-01-01

Several lines of evidence demonstrate that the motor system is involved in motor simulation of actions, but some uncertainty exists about the consequences of lesions of descending motor pathways on mental imagery tasks. Moreover, recent findings suggest that the motor system could also have a role in recognition of body parts. To address these…

Children's Recognition of Pride and Guilt as Consequences of Helping and Not Helping.

ERIC Educational Resources Information Center

Shorr, David N.; McClelland, Stephen E.

1998-01-01

Investigated the relationship between young children's age and their recognition that helping or choosing not to help can cause feelings of pride or guilt. Found age differences in identifying helping-action or inaction as causes, but little support for the hypothesis that identification of guilt as a consequence of not helping would…

Effectiveness of a Phonological Awareness Training Intervention on Word Recognition Ability of Children with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Mohammed, Adel Abdulla; Mostafa, Amaal Ahmed

2012-01-01

This study describes an action research project designed to improve word recognition ability of children with Autism Spectrum Disorder. A total of 47 children diagnosed as having Autism Spectrum Disorder using Autism Spectrum Disorder Evaluation Inventory (Mohammed, 2006), participated in this study. The sample was randomly divided into two…

Freedom of Movement: From Right to Possibility--Recognition of Qualifications through Legislation or Information

ERIC Educational Resources Information Center

Paulsen, Mette Beyer

2008-01-01

This article seeks to describe briefly various initiatives taken to ease recognition and comparison of formal qualifications across borders in the EU. It takes a political angle, from binding legal instruments such as directives and decisions to policy instruments such as recommendations and voluntary action and covers 27 Member States with very…

Word Recognition in Auditory Cortex

ERIC Educational Resources Information Center

DeWitt, Iain D. J.

2013-01-01

Although spoken word recognition is more fundamental to human communication than text recognition, knowledge of word-processing in auditory cortex is comparatively impoverished. This dissertation synthesizes current models of auditory cortex, models of cortical pattern recognition, models of single-word reading, results in phonetics and results in…

Two processes support visual recognition memory in rhesus monkeys.

PubMed

Guderian, Sebastian; Brigham, Danielle; Mishkin, Mortimer

2011-11-29

A large body of evidence in humans suggests that recognition memory can be supported by both recollection and familiarity. Recollection-based recognition is characterized by the retrieval of contextual information about the episode in which an item was previously encountered, whereas familiarity-based recognition is characterized instead by knowledge only that the item had been encountered previously in the absence of any context. To date, it is unknown whether monkeys rely on similar mnemonic processes to perform recognition memory tasks. Here, we present evidence from the analysis of receiver operating characteristics, suggesting that visual recognition memory in rhesus monkeys also can be supported by two separate processes and that these processes have features considered to be characteristic of recollection and familiarity. Thus, the present study provides converging evidence across species for a dual process model of recognition memory and opens up the possibility of studying the neural mechanisms of recognition memory in nonhuman primates on tasks that are highly similar to the ones used in humans.

Two processes support visual recognition memory in rhesus monkeys

PubMed Central

Guderian, Sebastian; Brigham, Danielle; Mishkin, Mortimer

2011-01-01

A large body of evidence in humans suggests that recognition memory can be supported by both recollection and familiarity. Recollection-based recognition is characterized by the retrieval of contextual information about the episode in which an item was previously encountered, whereas familiarity-based recognition is characterized instead by knowledge only that the item had been encountered previously in the absence of any context. To date, it is unknown whether monkeys rely on similar mnemonic processes to perform recognition memory tasks. Here, we present evidence from the analysis of receiver operating characteristics, suggesting that visual recognition memory in rhesus monkeys also can be supported by two separate processes and that these processes have features considered to be characteristic of recollection and familiarity. Thus, the present study provides converging evidence across species for a dual process model of recognition memory and opens up the possibility of studying the neural mechanisms of recognition memory in nonhuman primates on tasks that are highly similar to the ones used in humans. PMID:22084079

Testing the antidepressant properties of the peptide ARA290 in a human neuropsychological model of drug action.

PubMed

Cerit, Hilâl; Veer, Ilya M; Dahan, Albert; Niesters, Marieke; Harmer, Catherine J; Miskowiak, Kamilla W; Rombouts, Serge A R B; Van der Does, Willem

2015-12-01

Studies on the neural effects of Erythropoietin (EPO) indicate that EPO may have antidepressant effects. Due to its hematopoietic effects, EPO may cause serious side-effects with repeated administration if patients are not monitored extensively. ARA290 is an EPO-analog peptide without such hematopoietic side-effects but may have neurotrophic and antidepressant effects. The aim of this study was to investigate the possible antidepressant effects of ARA290 in a neuropsychological model of drug action. Healthy participants (N=36) received ARA290 (2mg) or placebo in a double-blind, randomized, parallel-group design. Neural and cognitive effects were assessed one week after administration. Primary outcome measures were the neural processing of fearful vs happy faces and the behavioral recognition of emotional facial expressions. ARA290-treated individuals displayed lower neural responses to happy faces in the fusiform gyrus. ARA290 tended to lower the recognition of happy and disgust facial expressions. Although ARA290 was not associated with a better memory for positive words, it was associated with faster categorization of positive vs negative words. Finally, ARA290 increased attention towards positive emotional pictures. No effects were observed on mood and affective symptoms. ARA290 may modulate some aspects of emotional processing, however, the direction and the strength of its effects do not unequivocally support an antidepressant-like profile for ARA290. Future studies may investigate the effects of different timing and dose. Copyright © 2015 Elsevier B.V. and ECNP. All rights reserved.

Objects tell us what action we can expect: dissociating brain areas for retrieval and exploitation of action knowledge during action observation in fMRI

PubMed Central

Schubotz, Ricarda I.; Wurm, Moritz F.; Wittmann, Marco K.; von Cramon, D. Yves

2014-01-01

Objects are reminiscent of actions often performed with them: knife and apple remind us on peeling the apple or cutting it. Mnemonic representations of object-related actions (action codes) evoked by the sight of an object may constrain and hence facilitate recognition of unrolling actions. The present fMRI study investigated if and how action codes influence brain activation during action observation. The average number of action codes (NAC) of 51 sets of objects was rated by a group of n = 24 participants. In an fMRI study, different volunteers were asked to recognize actions performed with the same objects presented in short videos. To disentangle areas reflecting the storage of action codes from those exploiting them, we showed object-compatible and object-incompatible (pantomime) actions. Areas storing action codes were considered to positively co-vary with NAC in both object-compatible and object-incompatible action; due to its role in tool-related tasks, we here hypothesized left anterior inferior parietal cortex (aIPL). In contrast, areas exploiting action codes were expected to show this correlation only in object-compatible but not incompatible action, as only object-compatible actions match one of the active action codes. For this interaction, we hypothesized ventrolateral premotor cortex (PMv) to join aIPL due to its role in biasing competition in IPL. We found left anterior intraparietal sulcus (IPS) and left posterior middle temporal gyrus (pMTG) to co-vary with NAC. In addition to these areas, action codes increased activity in object-compatible action in bilateral PMv, right IPS, and lateral occipital cortex (LO). Findings suggest that during action observation, the brain derives possible actions from perceived objects, and uses this information to shape action recognition. In particular, the number of expectable actions quantifies the activity level at PMv, IPL, and pMTG, but only PMv reflects their biased competition while observed action unfolds. PMID:25009519

Songbirds use spectral shape, not pitch, for sound pattern recognition

PubMed Central

Bregman, Micah R.; Patel, Aniruddh D.; Gentner, Timothy Q.

2016-01-01

Humans easily recognize “transposed” musical melodies shifted up or down in log frequency. Surprisingly, songbirds seem to lack this capacity, although they can learn to recognize human melodies and use complex acoustic sequences for communication. Decades of research have led to the widespread belief that songbirds, unlike humans, are strongly biased to use absolute pitch (AP) in melody recognition. This work relies almost exclusively on acoustically simple stimuli that may belie sensitivities to more complex spectral features. Here, we investigate melody recognition in a species of songbird, the European Starling (Sturnus vulgaris), using tone sequences that vary in both pitch and timbre. We find that small manipulations altering either pitch or timbre independently can drive melody recognition to chance, suggesting that both percepts are poor descriptors of the perceptual cues used by birds for this task. Instead we show that melody recognition can generalize even in the absence of pitch, as long as the spectral shapes of the constituent tones are preserved. These results challenge conventional views regarding the use of pitch cues in nonhuman auditory sequence recognition. PMID:26811447

Surface imprinted beads for the recognition of human serum albumin.

PubMed

Bonini, Francesca; Piletsky, Sergey; Turner, Anthony P F; Speghini, Adolfo; Bossi, Alessandra

2007-04-15

The synthesis of poly-aminophenylboronic acid (ABPA) imprinted beads for the recognition of the protein human serum albumin (HSA) is reported. In order to create homogeneous recognition sites, covalent immobilisation of the template HSA was exploited. The resulting imprinted beads were selective for HSA. The indirect imprinting factor (IF) calculated from supernatant was 1.6 and the direct IF, evaluated from the protein recovered from the beads, was 1.9. The binding capacity was 1.4 mg/g, which is comparable to commercially available affinity materials. The specificity of the HSA recognition was evaluated with competitive experiments, indicating a molar ratio 4.5/1 of competitor was necessary to displace half of the bound HSA. The recognition and binding of the imprinted beads was also tested with a complex sample, human serum and targeted removal of HSA without a loss of the other protein components was demonstrated. The easy preparation protocol of derivatised beads and a good protein recognition properties make the approach an attractive solution to analytical and bio-analytical problems in the field of biotechnology.

Can human eyes prevent perceptual narrowing for monkey faces in human infants?

PubMed

Damon, Fabrice; Bayet, Laurie; Quinn, Paul C; Hillairet de Boisferon, Anne; Méary, David; Dupierrix, Eve; Lee, Kang; Pascalis, Olivier

2015-07-01

Perceptual narrowing has been observed in human infants for monkey faces: 6-month-olds can discriminate between them, whereas older infants from 9 months of age display difficulty discriminating between them. The difficulty infants from 9 months have processing monkey faces has not been clearly identified. It could be due to the structural characteristics of monkey faces, particularly the key facial features that differ from human faces. The current study aimed to investigate whether the information conveyed by the eyes is of importance. We examined whether the presence of Caucasian human eyes in monkey faces allows recognition to be maintained in 6-month-olds and facilitates recognition in 9- and 12-month-olds. Our results revealed that the presence of human eyes in monkey faces maintains recognition for those faces at 6 months of age and partially facilitates recognition of those faces at 9 months of age, but not at 12 months of age. The findings are interpreted in the context of perceptual narrowing and suggest that the attenuation of processing of other-species faces is not reversed by the presence of human eyes. © 2015 Wiley Periodicals, Inc.

How could you use the ergonomics 'knowhow' transfer management to enhance human working for sustainable improvements in industrially developing countries?

PubMed

Helali, Faramarz

2012-01-01

This paper describes the different strategic understanding from getting ergonomics intervention programmes' conversations to 'Tip', including minimizing strategies; tipping point strategies; and maximizing strategies from building ergonomics intervention techniques. Those have indicated to different recognitions: 1) when amplification of the 'problem' is necessary; 2) when amplification of the 'tipping point' is necessary, and 3) when amplification of the 'success' is necessary. The practical applications and implications of the ergonomics intervention techniques are drawn from the findings of framing positive questions: 1) what is successful ergonomics intervention technique right now (Appreciative)? 2) What do we need to change for a better future (Imagine)? 3) How do we do this (Design)? 4) Who takes action and with what consequences (Act)? This requires re-framing of the ergonomics intervention techniques in an appreciative way, because of, the future action needs to be inspired by those things that participants feel are worth valuing, worth celebrating and sustaining.

Power and empowerment in nursing: three theoretical approaches.

PubMed

Kuokkanen, L; Leino-Kilpi, H

2000-01-01

Definitions and uses of the concept of empowerment are wide-ranging: the term has been used to describe the essence of human existence and development, but also aspects of organizational effectiveness and quality. The empowerment ideology is rooted in social action where empowerment was associated with community interests and with attempts to increase the power and influence of oppressed groups (such as workers, women and ethnic minorities). Later, there was also growing recognition of the importance of the individual's characteristics and actions. Based on a review of the literature, this paper explores the uses of the empowerment concept as a framework for nurses' professional growth and development. Given the complexity of the concept, it is vital to understand the underlying philosophy before moving on to define its substance. The articles reviewed were classified into three groups on the basis of their theoretical orientation: critical social theory, organization theory and social psychological theory. Empowerment seems likely to provide for an umbrella concept of professional development in nursing.

20 CFR 408.1003 - Which administrative actions are initial determinations?

Code of Federal Regulations, 2010 CFR

2010-04-01

... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false Which administrative actions are initial determinations? 408.1003 Section 408.1003 Employees' Benefits SOCIAL SECURITY ADMINISTRATION SPECIAL BENEFITS FOR...) Our calculation of the amount of change in your federally administered State recognition payment...

Infrared sensing of non-observable human biometrics

NASA Astrophysics Data System (ADS)

Willmore, Michael R.

2005-05-01

Interest and growth of biometric recognition technologies surged after 9/11. Once a technology mainly used for identity verification in law enforcement, biometrics are now being considered as a secure means of providing identity assurance in security related applications. Biometric recognition in law enforcement must, by necessity, use attributes of human uniqueness that are both observable and vulnerable to compromise. Privacy and protection of an individual's identity is not assured during criminal activity. However, a security system must rely on identity assurance for access control to physical or logical spaces while not being vulnerable to compromise and protecting the privacy of an individual. The solution resides in the use of non-observable attributes of human uniqueness to perform the biometric recognition process. This discussion will begin by presenting some key perspectives about biometric recognition and the characteristic differences between observable and non-observable biometric attributes. An introduction to the design, development, and testing of the Thermo-ID system will follow. The Thermo-ID system is an emerging biometric recognition technology that uses non-observable patterns of infrared energy naturally emanating from within the human body. As with all biometric systems, the infrared patterns recorded and compared within the Thermo-ID system are unique and individually distinguishable permitting a link to be confirmed between an individual and a claimed or previously established identity. The non-observable characteristics of infrared patterns of human uniqueness insure both the privacy and protection of an individual using this type of biometric recognition system.

Gesture Recognition Based on the Probability Distribution of Arm Trajectories

NASA Astrophysics Data System (ADS)

Wan, Khairunizam; Sawada, Hideyuki

The use of human motions for the interaction between humans and computers is becoming an attractive alternative to verbal media, especially through the visual interpretation of the human body motion. In particular, hand gestures are used as non-verbal media for the humans to communicate with machines that pertain to the use of the human gestures to interact with them. This paper introduces a 3D motion measurement of the human upper body for the purpose of the gesture recognition, which is based on the probability distribution of arm trajectories. In this study, by examining the characteristics of the arm trajectories given by a signer, motion features are selected and classified by using a fuzzy technique. Experimental results show that the use of the features extracted from arm trajectories effectively works on the recognition of dynamic gestures of a human, and gives a good performance to classify various gesture patterns.

Case-Based Plan Recognition Using Action Sequence Graphs

DTIC Science & Technology

2014-10-01

resized as necessary. Similarly, trace- based reasoning (Zarka et al., 2013) and episode -based reasoning (Sánchez-Marré, 2005) store fixed-length...is a goal state of Π, where satisfies has the same semantics as originally laid out in Ghallab, Nau & Traverso (2004). Action 0 is ...Although there are syntactic similarities between planning encoding graphs and action sequence graphs, important semantic differences exist because the

ONR (Office of Naval Research) Research in Distributed Reasoning and Planning.

DTIC Science & Technology

1987-05-01

Reasoning about Actions and Plans, Timberline Lodge , Timberline , Oregon (1987). [62] Lansky, A.L. "GEMPLAN: Event-based Planning Through Temporal...Actions and Plans, Timberline Lodge , Timberline , Oregon (1987). [71] Litman, D. Plan Recognition and Discourse Analysis. PhD thesis, University of...Workshop on Reasoning about Actions and Plans, Timberline Lodge , Timberlinie. Oregon (1987). [74] Manna, Z. and P.Wolper, "Synthesis of Communicating

Body-Based Gender Recognition Using Images from Visible and Thermal Cameras

PubMed Central

Nguyen, Dat Tien; Park, Kang Ryoung

2016-01-01

Gender information has many useful applications in computer vision systems, such as surveillance systems, counting the number of males and females in a shopping mall, accessing control systems in restricted areas, or any human-computer interaction system. In most previous studies, researchers attempted to recognize gender by using visible light images of the human face or body. However, shadow, illumination, and time of day greatly affect the performance of these methods. To overcome this problem, we propose a new gender recognition method based on the combination of visible light and thermal camera images of the human body. Experimental results, through various kinds of feature extraction and fusion methods, show that our approach is efficient for gender recognition through a comparison of recognition rates with conventional systems. PMID:26828487

Body-Based Gender Recognition Using Images from Visible and Thermal Cameras.

PubMed

Nguyen, Dat Tien; Park, Kang Ryoung

2016-01-27

Gender information has many useful applications in computer vision systems, such as surveillance systems, counting the number of males and females in a shopping mall, accessing control systems in restricted areas, or any human-computer interaction system. In most previous studies, researchers attempted to recognize gender by using visible light images of the human face or body. However, shadow, illumination, and time of day greatly affect the performance of these methods. To overcome this problem, we propose a new gender recognition method based on the combination of visible light and thermal camera images of the human body. Experimental results, through various kinds of feature extraction and fusion methods, show that our approach is efficient for gender recognition through a comparison of recognition rates with conventional systems.

Learning and Recognition of a Non-conscious Sequence of Events in Human Primary Visual Cortex.

PubMed

Rosenthal, Clive R; Andrews, Samantha K; Antoniades, Chrystalina A; Kennard, Christopher; Soto, David

2016-03-21

Human primary visual cortex (V1) has long been associated with learning simple low-level visual discriminations [1] and is classically considered outside of neural systems that support high-level cognitive behavior in contexts that differ from the original conditions of learning, such as recognition memory [2, 3]. Here, we used a novel fMRI-based dichoptic masking protocol-designed to induce activity in V1, without modulation from visual awareness-to test whether human V1 is implicated in human observers rapidly learning and then later (15-20 min) recognizing a non-conscious and complex (second-order) visuospatial sequence. Learning was associated with a change in V1 activity, as part of a temporo-occipital and basal ganglia network, which is at variance with the cortico-cerebellar network identified in prior studies of "implicit" sequence learning that involved motor responses and visible stimuli (e.g., [4]). Recognition memory was associated with V1 activity, as part of a temporo-occipital network involving the hippocampus, under conditions that were not imputable to mechanisms associated with conscious retrieval. Notably, the V1 responses during learning and recognition separately predicted non-conscious recognition memory, and functional coupling between V1 and the hippocampus was enhanced for old retrieval cues. The results provide a basis for novel hypotheses about the signals that can drive recognition memory, because these data (1) identify human V1 with a memory network that can code complex associative serial visuospatial information and support later non-conscious recognition memory-guided behavior (cf. [5]) and (2) align with mouse models of experience-dependent V1 plasticity in learning and memory [6]. Copyright © 2016 Elsevier Ltd. All rights reserved.

Conformal Predictions in Multimedia Pattern Recognition

ERIC Educational Resources Information Center

Nallure Balasubramanian, Vineeth

2010-01-01

The fields of pattern recognition and machine learning are on a fundamental quest to design systems that can learn the way humans do. One important aspect of human intelligence that has so far not been given sufficient attention is the capability of humans to express when they are certain about a decision, or when they are not. Machine learning…

Presentations of Shape in Object Recognition and Long-Term Visual Memory

DTIC Science & Technology

1994-04-05

theory of human image understanding . Psychological Review, 94, 115-147. Biederman, I., & Gerhardstein, P. C. (1993). Recognizing depth-rotated...Kybemetik. Submitted to Journal of Experimental Psychology: Human Perception and Performance. REFERENCES Biederman, I. (1987). Recognition-by-components: A

Human rights, repression and health meet takes stand on gender-based violence.

PubMed

Copelon, R

1992-01-01

The Third International Conference on Health, Political Repression and Human Rights held two workshops on the topic of women and organized violence. Several narratives from the different participants in the conference are cited concerning women's experiences resulting from domestic violence. The conference adopted by acclamation a resolution identifying the close relationship between organized dictatorial violence and the structural, systemic, daily violence inflicted upon women and children in the family. This conference is the first human rights organization to go on record in support of the current efforts in the Organization of American States (OAS) and the UN. There are three actions presently ongoing to push the recognition of gender-based violence as a violation of human rights. First, the OAS seeks to enact a treaty, entitled the ¿Draft Convention to Prevent, Punish, and Eradicate Violence Against Women.¿ Second, a working group in the UN recommends deferring the treaty route in favor of requiring improved reporting and the appointment of special rapporteurs on gender-based violence. And third, a worldwide petition seeks to have the issues of gender-based violence in all forms placed on the agenda on the UN 1993 World Conference on Human Rights.

Slow [Na+]i dynamics impacts arrhythmogenesis and spiral wave reentry in cardiac myocyte ionic model.

PubMed

Krogh-Madsen, Trine; Christini, David J

2017-09-01

Accumulation of intracellular Na + is gaining recognition as an important regulator of cardiac myocyte electrophysiology. The intracellular Na + concentration can be an important determinant of the cardiac action potential duration, can modulate the tissue-level conduction of excitation waves, and can alter vulnerability to arrhythmias. Mathematical models of cardiac electrophysiology often incorporate a dynamic intracellular Na + concentration, which changes much more slowly than the remaining variables. We investigated the dependence of several arrhythmogenesis-related factors on [Na + ] i in a mathematical model of the human atrial action potential. In cell simulations, we found that [Na + ] i accumulation stabilizes the action potential duration to variations in several conductances and that the slow dynamics of [Na + ] i impacts bifurcations to pro-arrhythmic afterdepolarizations, causing intermittency between different rhythms. In long-lasting tissue simulations of spiral wave reentry, [Na + ] i becomes spatially heterogeneous with a decreased area around the spiral wave rotation center. This heterogeneous region forms a functional anchor, resulting in diminished meandering of the spiral wave. Our findings suggest that slow, physiological, rate-dependent variations in [Na + ] i may play complex roles in cellular and tissue-level cardiac dynamics.

Slow [Na+]i dynamics impacts arrhythmogenesis and spiral wave reentry in cardiac myocyte ionic model

NASA Astrophysics Data System (ADS)

Krogh-Madsen, Trine; Christini, David J.

2017-09-01

Accumulation of intracellular Na+ is gaining recognition as an important regulator of cardiac myocyte electrophysiology. The intracellular Na+ concentration can be an important determinant of the cardiac action potential duration, can modulate the tissue-level conduction of excitation waves, and can alter vulnerability to arrhythmias. Mathematical models of cardiac electrophysiology often incorporate a dynamic intracellular Na+ concentration, which changes much more slowly than the remaining variables. We investigated the dependence of several arrhythmogenesis-related factors on [Na+]i in a mathematical model of the human atrial action potential. In cell simulations, we found that [Na+]i accumulation stabilizes the action potential duration to variations in several conductances and that the slow dynamics of [Na+]i impacts bifurcations to pro-arrhythmic afterdepolarizations, causing intermittency between different rhythms. In long-lasting tissue simulations of spiral wave reentry, [Na+]i becomes spatially heterogeneous with a decreased area around the spiral wave rotation center. This heterogeneous region forms a functional anchor, resulting in diminished meandering of the spiral wave. Our findings suggest that slow, physiological, rate-dependent variations in [Na+]i may play complex roles in cellular and tissue-level cardiac dynamics.

Discovery of Novel Irreversible Inhibitors of Interleukin (IL)-2-inducible Tyrosine Kinase (Itk) by Targeting Cysteine 442 in the ATP Pocket

PubMed Central

Harling, John D.; Deakin, Angela M.; Campos, Sébastien; Grimley, Rachel; Chaudry, Laiq; Nye, Catherine; Polyakova, Oxana; Bessant, Christina M.; Barton, Nick; Somers, Don; Barrett, John; Graves, Rebecca H.; Hanns, Laura; Kerr, William J.; Solari, Roberto

2013-01-01

IL-2-inducible tyrosine kinase (Itk) plays a key role in antigen receptor signaling in T cells and is considered an important target for anti-inflammatory drug discovery. In order to generate inhibitors with the necessary potency and selectivity, a compound that targeted cysteine 442 in the ATP binding pocket and with an envisaged irreversible mode of action was designed. We incorporated a high degree of molecular recognition and specific design features making the compound suitable for inhaled delivery. This study confirms the irreversible covalent binding of the inhibitor to the kinase by x-ray crystallography and enzymology while demonstrating potency, selectivity, and prolonged duration of action in in vitro biological assays. The biosynthetic turnover of the kinase was also examined as a critical factor when designing irreversible inhibitors for extended duration of action. The exemplified Itk inhibitor demonstrated inhibition of both TH1 and TH2 cytokines, was additive with fluticasone propionate, and inhibited cytokine release from human lung fragments. Finally, we describe an in vivo pharmacodynamic assay that allows rapid preclinical development without animal efficacy models. PMID:23935099

From Humans to Rats and Back Again: Bridging the Divide between Human and Animal Studies of Recognition Memory with Receiver Operating Characteristics

ERIC Educational Resources Information Center

Koen, Joshua D.; Yonelinas, Andrew P.

2011-01-01

Receiver operating characteristics (ROCs) have been used extensively to study the processes underlying human recognition memory, and this method has recently been applied in studies of rats. However, the extent to which the results from human and animal studies converge is neither entirely clear, nor is it known how the different methods used to…

Human body contour data based activity recognition.

PubMed

Myagmarbayar, Nergui; Yuki, Yoshida; Imamoglu, Nevrez; Gonzalez, Jose; Otake, Mihoko; Yu, Wenwei

2013-01-01

This research work is aimed to develop autonomous bio-monitoring mobile robots, which are capable of tracking and measuring patients' motions, recognizing the patients' behavior based on observation data, and providing calling for medical personnel in emergency situations in home environment. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs). In our previous research, a full framework was established towards this research goal. In this research, we aimed at improving the human activity recognition by using contour data of the tracked human subject extracted from the depth images as the signal source, instead of the lower limb joint angle data used in the previous research, which are more likely to be affected by the motion of the robot and human subjects. Several geometric parameters, such as, the ratio of height to weight of the tracked human subject, and distance (pixels) between centroid points of upper and lower parts of human body, were calculated from the contour data, and used as the features for the activity recognition. A Hidden Markov Model (HMM) is employed to classify different human activities from the features. Experimental results showed that the human activity recognition could be achieved with a high correct rate.

Enhancing Perception with Tactile Object Recognition in Adaptive Grippers for Human-Robot Interaction.

PubMed

Gandarias, Juan M; Gómez-de-Gabriel, Jesús M; García-Cerezo, Alfonso J

2018-02-26

The use of tactile perception can help first response robotic teams in disaster scenarios, where visibility conditions are often reduced due to the presence of dust, mud, or smoke, distinguishing human limbs from other objects with similar shapes. Here, the integration of the tactile sensor in adaptive grippers is evaluated, measuring the performance of an object recognition task based on deep convolutional neural networks (DCNNs) using a flexible sensor mounted in adaptive grippers. A total of 15 classes with 50 tactile images each were trained, including human body parts and common environment objects, in semi-rigid and flexible adaptive grippers based on the fin ray effect. The classifier was compared against the rigid configuration and a support vector machine classifier (SVM). Finally, a two-level output network has been proposed to provide both object-type recognition and human/non-human classification. Sensors in adaptive grippers have a higher number of non-null tactels (up to 37% more), with a lower mean of pressure values (up to 72% less) than when using a rigid sensor, with a softer grip, which is needed in physical human-robot interaction (pHRI). A semi-rigid implementation with 95.13% object recognition rate was chosen, even though the human/non-human classification had better results (98.78%) with a rigid sensor.

Automated facial attendance logger for students

NASA Astrophysics Data System (ADS)

Krithika, L. B.; Kshitish, S.; Kishore, M. R.

2017-11-01

From the past two decades, various spheres of activity are in the aspect of ‘Face recognition’ as an essential tool. The complete series of actions of face recognition is composed of 3 stages: Face Detection, Feature Extraction and Recognition. In this paper, we make an effort to put forth a new application of face recognition and detection in education. The proposed system scans the classroom and detects the face of the students in class and matches the scanned face with the templates that is available in the database and updates the attendance of the respective students.

Oceans and human health: Emerging public health risks n the marine environment

PubMed Central

Fleming, L.E.; Broad, K.; Clement, A.; Dewailly, E.; Elmir, S.; Knap, A.; Pomponi, S.A.; Smith, S.; Gabriele, H. Solo; Walsh, P.

2008-01-01

There has been an increasing recognition of the inter-relationship between human health and the oceans. Traditionally, the focus of research and concern has been on the impact of human activities on the oceans, particularly through anthropogenic pollution and the exploitation of marine resources. More recently, there has been recognition of the potential direct impact of the oceans on human health, both detrimental and beneficial. Areas identified include: global change, harmful algal blooms (HABs), microbial and chemical contamination of marine waters and seafood, and marine models and natural products from the seas. It is hoped that through the recognition of the inter-dependence of the health of both humans and the oceans, efforts will be made to restore and preserve the oceans. PMID:16996542

Evaluating Effects of Language Recognition on Language Rights and the Vitality of New Zealand Sign Language

ERIC Educational Resources Information Center

McKee, Rachel Locker; Manning, Victoria

2015-01-01

Status planning through legislation made New Zealand Sign Language (NZSL) an official language in 2006. But this strong symbolic action did not create resources or mechanisms to further the aims of the act. In this article we discuss the extent to which legal recognition and ensuing language-planning activities by state and community have affected…

Motor-visual neurons and action recognition in social interactions.

PubMed

de la Rosa, Stephan; Bülthoff, Heinrich H

2014-04-01

Cook et al. suggest that motor-visual neurons originate from associative learning. This suggestion has interesting implications for the processing of socially relevant visual information in social interactions. Here, we discuss two aspects of the associative learning account that seem to have particular relevance for visual recognition of social information in social interactions - namely, context-specific and contingency based learning.

Agonistic Recognition in Education: On Arendt's Qualification of Political and Moral Meaning

ERIC Educational Resources Information Center

Ljunggren, Carsten

2010-01-01

Agonistic recognition in education has three interlinked modes of aesthetic experience and self-presentation where one is related to actions in the public realm; one is related to plurality in the way in which it comes into existence in confrontation with others; and one is related to the subject-self, disclosed by "thinking. Arendt"s conception…

Monuments Men Recognition Act of 2013

THOMAS, 113th Congress

Sen. Blunt, Roy [R-MO

2013-12-19

Senate - 12/19/2013 Read twice and referred to the Committee on Banking, Housing, and Urban Affairs. (All Actions) Notes: For further action, see H.R.3658, which became Public Law 113-116 on 6/9/2014. Tracker: This bill has the status IntroducedHere are the steps for Status of Legislation:

Civilian Service Recognition Act of 2011

THOMAS, 112th Congress

Sen. Akaka, Daniel K. [D-HI

2011-07-28

Senate - 07/28/2011 Read twice and referred to the Committee on Homeland Security and Governmental Affairs. (All Actions) Notes: For further action, see H.R.2061, which became Public Law 112-73 on 12/20/2011. Tracker: This bill has the status IntroducedHere are the steps for Status of Legislation:

20 CFR 408.1004 - Which administrative actions are not initial determinations?

Code of Federal Regulations, 2010 CFR

2010-04-01

... 20 Employees' Benefits 2 2010-04-01 2010-04-01 false Which administrative actions are not initial determinations? 408.1004 Section 408.1004 Employees' Benefits SOCIAL SECURITY ADMINISTRATION SPECIAL BENEFITS FOR... federally administered State recognition payments due to a State-initiated mass change, as defined in § 408...

Assessing changes in HIV-related legal and policy environments: Lessons learned from a multi-country evaluation.

PubMed

Ferguson, Laura; Nicholson, Alexandra; Henry, Ian; Saha, Amitrajit; Sellers, Tilly; Gruskin, Sofia

2018-01-01

There is growing recognition in the health community that the legal environment-including laws, policies, and related procedures-impacts vulnerability to HIV and access to HIV-related services both positively and negatively. Assessing changes in the legal environment and how these affect HIV-related outcomes, however, is challenging, and understanding of appropriate methodologies nascent. We conducted an evaluation of a UNDP project designed to strengthen legal environments to support the human rights of key populations, in particular LGBT populations, women and girls, affected by HIV in sub-Saharan Africa. We analyzed data on activities designed to improve legal environments through a systematic document review and 53 qualitative interviews. The project made substantial strides towards legal change in many places, and examples provide broader lessons for work in this area. Two core pillars appear fundamental: a government-led participatory assessment of the legal environment, and building the capacity of those impacted by and engaged in this work. Systematic attention to human rights is vital: it can help open new spaces for dialogue among diverse stakeholders, foster new collaborations, and ensure local ownership, nuanced understanding of the political landscape, attention to marginalized populations, and accountability for (in)action. Entry points for effecting legal change go beyond "HIV laws" to also include other laws, national policies and strategies. Conducting legal environment assessments, multi-stakeholder dialogues, action planning and related activities, alongside capacity building, can contribute to changes in knowledge and attitudes directly relevant to reforming laws that are found to be harmful. Shorter-term goals along the causal pathway to legal change (e.g. changes in policy) can constitute interim markers of success, and recognition of these can maintain momentum. Increasing understanding of progress towards changes in the legal environment that can positively affect HIV-related outcomes is important in working to improve the health and lives of people living with HIV.

Assessing changes in HIV-related legal and policy environments: Lessons learned from a multi-country evaluation

PubMed Central

Nicholson, Alexandra; Henry, Ian; Saha, Amitrajit; Sellers, Tilly; Gruskin, Sofia

2018-01-01

Introduction There is growing recognition in the health community that the legal environment—including laws, policies, and related procedures—impacts vulnerability to HIV and access to HIV-related services both positively and negatively. Assessing changes in the legal environment and how these affect HIV-related outcomes, however, is challenging, and understanding of appropriate methodologies nascent. Methods We conducted an evaluation of a UNDP project designed to strengthen legal environments to support the human rights of key populations, in particular LGBT populations, women and girls, affected by HIV in sub-Saharan Africa. We analyzed data on activities designed to improve legal environments through a systematic document review and 53 qualitative interviews. Results The project made substantial strides towards legal change in many places, and examples provide broader lessons for work in this area. Two core pillars appear fundamental: a government-led participatory assessment of the legal environment, and building the capacity of those impacted by and engaged in this work. Systematic attention to human rights is vital: it can help open new spaces for dialogue among diverse stakeholders, foster new collaborations, and ensure local ownership, nuanced understanding of the political landscape, attention to marginalized populations, and accountability for (in)action. Entry points for effecting legal change go beyond “HIV laws” to also include other laws, national policies and strategies. Conclusion Conducting legal environment assessments, multi-stakeholder dialogues, action planning and related activities, alongside capacity building, can contribute to changes in knowledge and attitudes directly relevant to reforming laws that are found to be harmful. Shorter-term goals along the causal pathway to legal change (e.g. changes in policy) can constitute interim markers of success, and recognition of these can maintain momentum. Increasing understanding of progress towards changes in the legal environment that can positively affect HIV-related outcomes is important in working to improve the health and lives of people living with HIV. PMID:29474486

The interaction between embodiment and empathy in facial expression recognition

PubMed Central

Jospe, Karine; Flöel, Agnes; Lavidor, Michal

2018-01-01

Abstract Previous research has demonstrated that the Action-Observation Network (AON) is involved in both emotional-embodiment (empathy) and action-embodiment mechanisms. In this study, we hypothesized that interfering with the AON will impair action recognition and that this impairment will be modulated by empathy levels. In Experiment 1 (n = 90), participants were asked to recognize facial expressions while their facial motion was restricted. In Experiment 2 (n = 50), we interfered with the AON by applying transcranial Direct Current Stimulation to the motor cortex. In both experiments, we found that interfering with the AON impaired the performance of participants with high empathy levels; however, for the first time, we demonstrated that the interference enhanced the performance of participants with low empathy. This novel finding suggests that the embodiment module may be flexible, and that it can be enhanced in individuals with low empathy by simple manipulation of motor activation. PMID:29378022

Modeling Interval Temporal Dependencies for Complex Activities Understanding

DTIC Science & Technology

2013-10-11

ORGANIZATION NAMES AND ADDRESSES U.S. Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211 15. SUBJECT TERMS Human activity modeling...computer vision applications: human activity recognition and facial activity recognition. The results demonstrate the superior performance of the

Science 101: How Does Speech-Recognition Software Work?

ERIC Educational Resources Information Center

Robertson, Bill

2016-01-01

This column provides background science information for elementary teachers. Many innovations with computer software begin with analysis of how humans do a task. This article takes a look at how humans recognize spoken words and explains the origins of speech-recognition software.

Effect of 1.8 GHz radiofrequency electromagnetic radiation on novel object associative recognition memory in mice

PubMed Central

Wang, Kai; Lu, Jun-Mei; Xing, Zhen-He; Zhao, Qian-Ru; Hu, Lin-Qi; Xue, Lei; Zhang, Jie; Mei, Yan-Ai

2017-01-01

Mounting evidence suggests that exposure to radiofrequency electromagnetic radiation (RF-EMR) can influence learning and memory in rodents. In this study, we examined the effects of single exposure to 1.8 GHz RF-EMR for 30 min on subsequent recognition memory in mice, using the novel object recognition task (NORT). RF-EMR exposure at an intensity of >2.2 W/kg specific absorption rate (SAR) power density induced a significant density-dependent increase in NORT index with no corresponding changes in spontaneous locomotor activity. RF-EMR exposure increased dendritic-spine density and length in hippocampal and prefrontal cortical neurons, as shown by Golgi staining. Whole-cell recordings in acute hippocampal and medial prefrontal cortical slices showed that RF-EMR exposure significantly altered the resting membrane potential and action potential frequency, and reduced the action potential half-width, threshold, and onset delay in pyramidal neurons. These results demonstrate that exposure to 1.8 GHz RF-EMR for 30 min can significantly increase recognition memory in mice, and can change dendritic-spine morphology and neuronal excitability in the hippocampus and prefrontal cortex. The SAR in this study (3.3 W/kg) was outside the range encountered in normal daily life, and its relevance as a potential therapeutic approach for disorders associated with recognition memory deficits remains to be clarified. PMID:28303965

Areas Recruited during Action Understanding Are Not Modulated by Auditory or Sign Language Experience.

PubMed

Fang, Yuxing; Chen, Quanjing; Lingnau, Angelika; Han, Zaizhu; Bi, Yanchao

2016-01-01

The observation of other people's actions recruits a network of areas including the inferior frontal gyrus (IFG), the inferior parietal lobule (IPL), and posterior middle temporal gyrus (pMTG). These regions have been shown to be activated through both visual and auditory inputs. Intriguingly, previous studies found no engagement of IFG and IPL for deaf participants during non-linguistic action observation, leading to the proposal that auditory experience or sign language usage might shape the functionality of these areas. To understand which variables induce plastic changes in areas recruited during the processing of other people's actions, we examined the effects of tasks (action understanding and passive viewing) and effectors (arm actions vs. leg actions), as well as sign language experience in a group of 12 congenitally deaf signers and 13 hearing participants. In Experiment 1, we found a stronger activation during an action recognition task in comparison to a low-level visual control task in IFG, IPL and pMTG in both deaf signers and hearing individuals, but no effect of auditory or sign language experience. In Experiment 2, we replicated the results of the first experiment using a passive viewing task. Together, our results provide robust evidence demonstrating that the response obtained in IFG, IPL, and pMTG during action recognition and passive viewing is not affected by auditory or sign language experience, adding further support for the supra-modal nature of these regions.

Prediction of the thermal imaging minimum resolvable (circle) temperature difference with neural network application.

PubMed

Fang, Yi-Chin; Wu, Bo-Wen

2008-12-01

Thermal imaging is an important technology in both national defense and the private sector. An advantage of thermal imaging is its ability to be deployed while fully engaged in duties, not limited by weather or the brightness of indoor or outdoor conditions. However, in an outdoor environment, many factors, including atmospheric decay, target shape, great distance, fog, temperature out of range and diffraction limits can lead to bad image formation, which directly affects the accuracy of object recognition. The visual characteristics of the human eye mean that it has a much better capacity for picture recognition under normal conditions than artificial intelligence does. However, conditions of interference significantly reduce this capacity for picture recognition for instance, fatigue impairs human eyesight. Hence, psychological and physiological factors can affect the result when the human eye is adopted to measure MRTD (minimum resolvable temperature difference) and MRCTD (minimum resolvable circle temperature difference). This study explores thermal imaging recognition, and presents a method for effectively choosing the characteristic values and processing the images fully. Neural network technology is successfully applied to recognize thermal imaging and predict MRTD and MRCTD (Appendix A), exceeding thermal imaging recognition under fatigue and the limits of the human eye.

The Medial Dorsal Thalamic Nucleus and the Medial Prefrontal Cortex of the Rat Function Together to Support Associative Recognition and Recency but Not Item Recognition

ERIC Educational Resources Information Center

Cross, Laura; Brown, Malcolm W.; Aggleton, John P.; Warburton, E. Clea

2013-01-01

In humans recognition memory deficits, a typical feature of diencephalic amnesia, have been tentatively linked to mediodorsal thalamic nucleus (MD) damage. Animal studies have occasionally investigated the role of the MD in single-item recognition, but have not systematically analyzed its involvement in other recognition memory processes. In…

Emotion Recognition in Animated Compared to Human Stimuli in Adolescents with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Brosnan, Mark; Johnson, Hilary; Grawmeyer, Beate; Chapman, Emma; Benton, Laura

2015-01-01

There is equivocal evidence as to whether there is a deficit in recognising emotional expressions in Autism spectrum disorder (ASD). This study compared emotion recognition in ASD in three types of emotion expression media (still image, dynamic image, auditory) across human stimuli (e.g. photo of a human face) and animated stimuli (e.g. cartoon…

Neural network application for thermal image recognition of low-resolution objects

NASA Astrophysics Data System (ADS)

Fang, Yi-Chin; Wu, Bo-Wen

2007-02-01

In the ever-changing situation on a battle field, accurate recognition of a distant object is critical to a commander's decision-making and the general public's safety. Efficiently distinguishing between an enemy's armoured vehicles and ordinary civilian houses under all weather conditions has become an important research topic. This study presents a system for recognizing an armoured vehicle by distinguishing marks and contours. The characteristics of 12 different shapes and 12 characters are used to explore thermal image recognition under the circumstance of long distance and low resolution. Although the recognition capability of human eyes is superior to that of artificial intelligence under normal conditions, it tends to deteriorate substantially under long-distance and low-resolution scenarios. This study presents an effective method for choosing features and processing images. The artificial neural network technique is applied to further improve the probability of accurate recognition well beyond the limit of the recognition capability of human eyes.

Autonomous facial recognition system inspired by human visual system based logarithmical image visualization technique

NASA Astrophysics Data System (ADS)

Wan, Qianwen; Panetta, Karen; Agaian, Sos

2017-05-01

Autonomous facial recognition system is widely used in real-life applications, such as homeland border security, law enforcement identification and authentication, and video-based surveillance analysis. Issues like low image quality, non-uniform illumination as well as variations in poses and facial expressions can impair the performance of recognition systems. To address the non-uniform illumination challenge, we present a novel robust autonomous facial recognition system inspired by the human visual system based, so called, logarithmical image visualization technique. In this paper, the proposed method, for the first time, utilizes the logarithmical image visualization technique coupled with the local binary pattern to perform discriminative feature extraction for facial recognition system. The Yale database, the Yale-B database and the ATT database are used for computer simulation accuracy and efficiency testing. The extensive computer simulation demonstrates the method's efficiency, accuracy, and robustness of illumination invariance for facial recognition.

Motor-auditory-visual integration: The role of the human mirror neuron system in communication and communication disorders

PubMed Central

Le Bel, Ronald M.; Pineda, Jaime A.; Sharma, Anu

2009-01-01

The mirror neuron system (MNS) is a trimodal system composed of neuronal populations that respond to motor, visual, and auditory stimulation, such as when an action is performed, observed, heard or read about. In humans, the MNS has been identified using neuro-imaging techniques (such as fMRI and mu suppression in the EEG). It reflects an integration of motor-auditory-visual information processing related to aspects of language learning including action understanding and recognition. Such integration may also form the basis for language-related constructs such as theory of mind. In this article, we review the MNS system as it relates to the cognitive development of language in typically developing children and in children at-risk for communication disorders, such as children with autism spectrum disorder (ASD) or hearing impairment. Studying MNS development in these children may help illuminate an important role of the MNS in children with communication disorders. Studies with deaf children are especially important because they offer potential insights into how the MNS is reorganized when one modality, such as audition, is deprived during early cognitive development, and this may have long-term consequences on language maturation and theory of mind abilities. Learning outcomes Readers will be able to (1) understand the concept of mirror neurons, (2) identify cortical areas associated with the MNS in animal and human studies, (3) discuss the use of mu suppression in the EEG for measuring the MNS in humans, and (4) discuss MNS dysfunction in children with (ASD). PMID:19419735

The MIT Summit Speech Recognition System: A Progress Report

DTIC Science & Technology

1989-01-01

understanding of the human communication process. Despite recent development of some speech recognition systems with high accuracy, the performance of such...over the past four decades on human communication , in the hope that such systems will one day have a performance approaching that of humans. We are...optimize its use. Third, the system must have a stochastic component to deal with the present state of ignorance in our understanding of the human

Video-based convolutional neural networks for activity recognition from robot-centric videos

NASA Astrophysics Data System (ADS)

Ryoo, M. S.; Matthies, Larry

2016-05-01

In this evaluation paper, we discuss convolutional neural network (CNN)-based approaches for human activity recognition. In particular, we investigate CNN architectures designed to capture temporal information in videos and their applications to the human activity recognition problem. There have been multiple previous works to use CNN-features for videos. These include CNNs using 3-D XYT convolutional filters, CNNs using pooling operations on top of per-frame image-based CNN descriptors, and recurrent neural networks to learn temporal changes in per-frame CNN descriptors. We experimentally compare some of these different representatives CNNs while using first-person human activity videos. We especially focus on videos from a robots viewpoint, captured during its operations and human-robot interactions.

A model of EcoRII restriction endonuclease action: the active complex is most likely formed by one protein subunit and one DNA recognition site

NASA Technical Reports Server (NTRS)

Karpova, E. A.; Kubareva, E. A.; Shabarova, Z. A.

1999-01-01

To elucidate the mechanism of interaction of restriction endonuclease EcoRII with DNA, we studied by native gel electrophoresis the binding of this endonuclease to a set of synthetic DNA-duplexes containing the modified or canonical recognition sequence 5'-d(CCA/TGG)-3'. All binding substrate or substrate analogues tested could be divided into two major groups: (i) duplexes that, at the interaction with endonuclease EcoRII, form two types of stable complexes on native gel in the absence of Mg2+ cofactor; (ii) duplexes that form only one type of complex, observed both in the presence and absence of Mg2+. Unlike the latter, duplexes under the first group can be hydrolyzed by endonuclease. Data obtained suggest that the active complex is most likely formed by one protein subunit and one DNA recognition sequence. A model of EcoRII endonuclease action is presented.

Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality.

PubMed

Mehta, Dhwani; Siddiqui, Mohammad Faridul Haque; Javaid, Ahmad Y

2018-02-01

Extensive possibilities of applications have made emotion recognition ineluctable and challenging in the field of computer science. The use of non-verbal cues such as gestures, body movement, and facial expressions convey the feeling and the feedback to the user. This discipline of Human-Computer Interaction places reliance on the algorithmic robustness and the sensitivity of the sensor to ameliorate the recognition. Sensors play a significant role in accurate detection by providing a very high-quality input, hence increasing the efficiency and the reliability of the system. Automatic recognition of human emotions would help in teaching social intelligence in the machines. This paper presents a brief study of the various approaches and the techniques of emotion recognition. The survey covers a succinct review of the databases that are considered as data sets for algorithms detecting the emotions by facial expressions. Later, mixed reality device Microsoft HoloLens (MHL) is introduced for observing emotion recognition in Augmented Reality (AR). A brief introduction of its sensors, their application in emotion recognition and some preliminary results of emotion recognition using MHL are presented. The paper then concludes by comparing results of emotion recognition by the MHL and a regular webcam.

Visual body recognition in a prosopagnosic patient.

PubMed

Moro, V; Pernigo, S; Avesani, R; Bulgarelli, C; Urgesi, C; Candidi, M; Aglioti, S M

2012-01-01

Conspicuous deficits in face recognition characterize prosopagnosia. Information on whether agnosic deficits may extend to non-facial body parts is lacking. Here we report the neuropsychological description of FM, a patient affected by a complete deficit in face recognition in the presence of mild clinical signs of visual object agnosia. His deficit involves both overt and covert recognition of faces (i.e. recognition of familiar faces, but also categorization of faces for gender or age) as well as the visual mental imagery of faces. By means of a series of matching-to-sample tasks we investigated: (i) a possible association between prosopagnosia and disorders in visual body perception; (ii) the effect of the emotional content of stimuli on the visual discrimination of faces, bodies and objects; (iii) the existence of a dissociation between identity recognition and the emotional discrimination of faces and bodies. Our results document, for the first time, the co-occurrence of body agnosia, i.e. the visual inability to discriminate body forms and body actions, and prosopagnosia. Moreover, the results show better performance in the discrimination of emotional face and body expressions with respect to body identity and neutral actions. Since FM's lesions involve bilateral fusiform areas, it is unlikely that the amygdala-temporal projections explain the relative sparing of emotion discrimination performance. Indeed, the emotional content of the stimuli did not improve the discrimination of their identity. The results hint at the existence of two segregated brain networks involved in identity and emotional discrimination that are at least partially shared by face and body processing. Copyright © 2011 Elsevier Ltd. All rights reserved.

Atoms of recognition in human and computer vision.

PubMed

Ullman, Shimon; Assif, Liav; Fetaya, Ethan; Harari, Daniel

2016-03-08

Discovering the visual features and representations used by the brain to recognize objects is a central problem in the study of vision. Recently, neural network models of visual object recognition, including biological and deep network models, have shown remarkable progress and have begun to rival human performance in some challenging tasks. These models are trained on image examples and learn to extract features and representations and to use them for categorization. It remains unclear, however, whether the representations and learning processes discovered by current models are similar to those used by the human visual system. Here we show, by introducing and using minimal recognizable images, that the human visual system uses features and processes that are not used by current models and that are critical for recognition. We found by psychophysical studies that at the level of minimal recognizable images a minute change in the image can have a drastic effect on recognition, thus identifying features that are critical for the task. Simulations then showed that current models cannot explain this sensitivity to precise feature configurations and, more generally, do not learn to recognize minimal images at a human level. The role of the features shown here is revealed uniquely at the minimal level, where the contribution of each feature is essential. A full understanding of the learning and use of such features will extend our understanding of visual recognition and its cortical mechanisms and will enhance the capacity of computational models to learn from visual experience and to deal with recognition and detailed image interpretation.

Multifunctional receptor model for dioxin and related compound toxic action: possible thyroid hormone-responsive effector-linked site.

PubMed Central

McKinney, J D

1989-01-01

Molecular/theoretical modeling studies have revealed that thyroid hormones and toxic chlorinated aromatic hydrocarbons of environmental significance (for which dioxin or TCDD is the prototype) have similar structural properties that could be important in molecular recognition in biochemical systems. These molecular properties include a somewhat rigid, sterically accessible and polarizable aromatic ring and size-limited, hydrophobic lateral substituents, usually contained in opposite adjoining rings of a diphenyl compound. These molecular properties define the primary binding groups thought to be important in molecular recognition of both types of structures in biochemical systems. Similar molecular reactivities are supported by the demonstration of effective specific binding of thyroid hormones and chlorinated aromatic hydrocarbons with four different proteins, enzymes, or receptor preparations that are known or suspected to be involved in the expression of thyroid hormone activity. These binding interactions represent both aromatic-aromatic (stacking) and molecular cleft-type recognition processes. A multiple protein or multifunctional receptor-ligand binding mechanism model is proposed as a way of visualizing the details and possible role of both the stacking and cleft type molecular recognition factors in the expression of biological activity. The model suggests a means by which hormone-responsive effector-linked sites (possible protein-protein-DNA complexes) can maintain highly structurally specific control of hormone action. Finally, the model also provides a theoretical basis for the design and conduct of further biological experimentation on the molecular mechanism(s) of action of toxic chlorinated aromatic hydrocarbons and thyroid hormones. Images FIGURE 3. A FIGURE 3. B FIGURE 3. C FIGURE 3. D PMID:2551666

Face Encoding and Recognition in the Human Brain

NASA Astrophysics Data System (ADS)

Haxby, James V.; Ungerleider, Leslie G.; Horwitz, Barry; Maisog, Jose Ma.; Rapoport, Stanley I.; Grady, Cheryl L.

1996-01-01

A dissociation between human neural systems that participate in the encoding and later recognition of new memories for faces was demonstrated by measuring memory task-related changes in regional cerebral blood flow with positron emission tomography. There was almost no overlap between the brain structures associated with these memory functions. A region in the right hippocampus and adjacent cortex was activated during memory encoding but not during recognition. The most striking finding in neocortex was the lateralization of prefrontal participation. Encoding activated left prefrontal cortex, whereas recognition activated right prefrontal cortex. These results indicate that the hippocampus and adjacent cortex participate in memory function primarily at the time of new memory encoding. Moreover, face recognition is not mediated simply by recapitulation of operations performed at the time of encoding but, rather, involves anatomically dissociable operations.

Unfoldomics of human diseases: linking protein intrinsic disorder with diseases

PubMed Central

Uversky, Vladimir N; Oldfield, Christopher J; Midic, Uros; Xie, Hongbo; Xue, Bin; Vucetic, Slobodan; Iakoucheva, Lilia M; Obradovic, Zoran; Dunker, A Keith

2009-01-01

Background Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) lack stable tertiary and/or secondary structure yet fulfills key biological functions. The recent recognition of IDPs and IDRs is leading to an entire field aimed at their systematic structural characterization and at determination of their mechanisms of action. Bioinformatics studies showed that IDPs and IDRs are highly abundant in different proteomes and carry out mostly regulatory functions related to molecular recognition and signal transduction. These activities complement the functions of structured proteins. IDPs and IDRs were shown to participate in both one-to-many and many-to-one signaling. Alternative splicing and posttranslational modifications are frequently used to tune the IDP functionality. Several individual IDPs were shown to be associated with human diseases, such as cancer, cardiovascular disease, amyloidoses, diabetes, neurodegenerative diseases, and others. This raises questions regarding the involvement of IDPs and IDRs in various diseases. Results IDPs and IDRs were shown to be highly abundant in proteins associated with various human maladies. As the number of IDPs related to various diseases was found to be very large, the concepts of the disease-related unfoldome and unfoldomics were introduced. Novel bioinformatics tools were proposed to populate and characterize the disease-associated unfoldome. Structural characterization of the members of the disease-related unfoldome requires specialized experimental approaches. IDPs possess a number of unique structural and functional features that determine their broad involvement into the pathogenesis of various diseases. Conclusion Proteins associated with various human diseases are enriched in intrinsic disorder. These disease-associated IDPs and IDRs are real, abundant, diversified, vital, and dynamic. These proteins and regions comprise the disease-related unfoldome, which covers a significant part of the human proteome. Profound association between intrinsic disorder and various human diseases is determined by a set of unique structural and functional characteristics of IDPs and IDRs. Unfoldomics of human diseases utilizes unrivaled bioinformatics and experimental techniques, paves the road for better understanding of human diseases, their pathogenesis and molecular mechanisms, and helps develop new strategies for the analysis of disease-related proteins. PMID:19594884

Speech Recognition Using Multiple Features and Multiple Recognizers

DTIC Science & Technology

1991-12-03

6 2.1 Introduction ............................................... 6 2.2 Human Speech Communication Process...119 How to Setup ASRT.......................................... 119 How to Use Interactive Menus .................................. 120...recognize a word from an acoustic signal. The human ear and brain perform this type of recognition with incredible speed and precision. Even though

Modeling Human Visual Perception for Target Detection in Military Simulations

DTIC Science & Technology

2009-06-01

incorrectly, is a subject for future research. Possibly, one could exploit the Recognition-by-Components theory of Biederman (1987) and decompose the...Psychophysiscs, 55, 485-496. Biederman , I. (1987). Recognition-by-components: A theory of human image understand- ing. Psychological Review, 94, 115-147

"When Did I Learn and When Shall I Act?": The Developmental Relationship between Episodic Future Thinking and Memory

ERIC Educational Resources Information Center

Naito, Mika; Suzuki, Toshiko

2011-01-01

This study investigated the development of the ability to reflect on one's personal past and future. A total of 64 4- to 6-year-olds received tasks of delayed self-recognition, source memory, delay of gratification, and a newly developed task of future-oriented action timing. Although children's performance on delayed self-recognition, source…

Examining the Conflict and Interconnectedness of Young People's Ideas about Environmental Issues, Responsibility and Action

ERIC Educational Resources Information Center

Wilks, Leigh; Harris, Neil

2016-01-01

Objective: Young people's environmental views are typically conflicted, with little recognition of the links between environmental issues or between environmental responsibility and action. The purpose of this study was to clarify whether young people's understanding of the environment is in conflict or whether they are forming interconnections…

Educational Malpractice: Can the Judiciary Remedy the Growing Problem of Functional Illiteracy?

ERIC Educational Resources Information Center

Klein, Alice J.

1979-01-01

Investigates the viability of a negligence action for inadequate public school education. Explores the problems inherent in proving each element of negligence, the available defense, and the potential consequences for plaintiffs, defendants, and educational policy-making that would flow from judicial recognition of a cause of action. Journal…

Prospective Memory in Context: Moving through a Familiar Space

ERIC Educational Resources Information Center

Smith, Rebekah E.; Hunt, R. Reed; Murray, Amy E.

2017-01-01

Successful completion of delayed intentions is a common but important aspect of daily behavior. Such behavior requires not only memory for the intended action but also recognition of the opportunity to perform that action, known collectively as prospective memory. The fact that prospective memory tasks occur in the midst of other activities is…

Separate neural systems support representations for actions and objects during narrative speech in post-stroke aphasia☆

PubMed Central

Gleichgerrcht, Ezequiel; Fridriksson, Julius; Rorden, Chris; Nesland, Travis; Desai, Rutvik; Bonilha, Leonardo

2015-01-01

Background Representations of objects and actions in everyday speech are usually materialized as nouns and verbs, two grammatical classes that constitute the core elements of language. Given their very distinct roles in singling out objects (nouns) or referring to transformative actions (verbs), they likely rely on distinct brain circuits. Method We tested this hypothesis by conducting network-based lesion-symptom mapping in 38 patients with chronic stroke to the left hemisphere. We reconstructed the individual brain connectomes from probabilistic tractography applied to magnetic resonance imaging and obtained measures of production of words referring to objects and actions from narrative discourse elicited by picture naming tasks. Results Words for actions were associated with a frontal network strongly engaging structures involved in motor control and programming. Words for objects, instead, were related to a posterior network spreading across the occipital, posterior inferior temporal, and parietal regions, likely related with visual processing and imagery, object recognition, and spatial attention/scanning. Thus, each of these networks engaged brain areas typically involved in cognitive and sensorimotor experiences equivalent to the function served by each grammatical class (e.g. motor areas for verbs, perception areas for nouns). Conclusions The finding that the two major grammatical classes in human speech rely on two dissociable networks has both important theoretical implications for the neurobiology of language and clinical implications for the assessment and potential rehabilitation and treatment of patients with chronic aphasia due to stroke. PMID:26759789

Coordinate Transformations in Object Recognition

ERIC Educational Resources Information Center

Graf, Markus

2006-01-01

A basic problem of visual perception is how human beings recognize objects after spatial transformations. Three central classes of findings have to be accounted for: (a) Recognition performance varies systematically with orientation, size, and position; (b) recognition latencies are sequentially additive, suggesting analogue transformation…

Quest Hierarchy for Hyperspectral Face Recognition

DTIC Science & Technology

2011-03-01

numerous face recognition algorithms available, several very good literature surveys are available that include Abate [29], Samal [110], Kong [18], Zou...Perception, Japan (January 1994). [110] Samal , Ashok and P. Iyengar, Automatic Recognition and Analysis of Human Faces and Facial Expressions: A Survey

21 CFR 170.30 - Eligibility for classification as generally recognized as safe (GRAS).

Code of Federal Regulations, 2013 CFR

2013-04-01

... HEALTH AND HUMAN SERVICES (CONTINUED) FOOD FOR HUMAN CONSUMPTION (CONTINUED) FOOD ADDITIVES Food Additive... obtain approval of a food additive regulation for the ingredient. General recognition of safety through... of scientific procedures required for approval of a food additive regulation. General recognition of...

21 CFR 170.30 - Eligibility for classification as generally recognized as safe (GRAS).

Code of Federal Regulations, 2011 CFR

2011-04-01

... HEALTH AND HUMAN SERVICES (CONTINUED) FOOD FOR HUMAN CONSUMPTION (CONTINUED) FOOD ADDITIVES Food Additive... obtain approval of a food additive regulation for the ingredient. General recognition of safety through... of scientific procedures required for approval of a food additive regulation. General recognition of...

21 CFR 170.30 - Eligibility for classification as generally recognized as safe (GRAS).

Code of Federal Regulations, 2012 CFR

2012-04-01

... HEALTH AND HUMAN SERVICES (CONTINUED) FOOD FOR HUMAN CONSUMPTION (CONTINUED) FOOD ADDITIVES Food Additive... obtain approval of a food additive regulation for the ingredient. General recognition of safety through... of scientific procedures required for approval of a food additive regulation. General recognition of...

21 CFR 170.30 - Eligibility for classification as generally recognized as safe (GRAS).

Code of Federal Regulations, 2010 CFR

2010-04-01

... HEALTH AND HUMAN SERVICES (CONTINUED) FOOD FOR HUMAN CONSUMPTION (CONTINUED) FOOD ADDITIVES Food Additive... obtain approval of a food additive regulation for the ingredient. General recognition of safety through... of scientific procedures required for approval of a food additive regulation. General recognition of...

Bee venom processes human skin lipids for presentation by CD1a

PubMed Central

Bourgeois, Elvire A.; Subramaniam, Sumithra; Cheng, Tan-Yun; De Jong, Annemieke; Layre, Emilie; Ly, Dalam; Salimi, Maryam; Legaspi, Annaliza; Modlin, Robert L.; Salio, Mariolina; Cerundolo, Vincenzo

2015-01-01

Venoms frequently co-opt host immune responses, so study of their mode of action can provide insight into novel inflammatory pathways. Using bee and wasp venom responses as a model system, we investigated whether venoms contain CD1-presented antigens. Here, we show that venoms activate human T cells via CD1a proteins. Whereas CD1 proteins typically present lipids, chromatographic separation of venoms unexpectedly showed that stimulatory factors partition into protein-containing fractions. This finding was explained by demonstrating that bee venom–derived phospholipase A2 (PLA2) activates T cells through generation of small neoantigens, such as free fatty acids and lysophospholipids, from common phosphodiacylglycerides. Patient studies showed that injected PLA2 generates lysophospholipids within human skin in vivo, and polyclonal T cell responses are dependent on CD1a protein and PLA2. These findings support a previously unknown skin immune response based on T cell recognition of CD1a proteins and lipid neoantigen generated in vivo by phospholipases. The findings have implications for skin barrier sensing by T cells and mechanisms underlying phospholipase-dependent inflammatory skin disease. PMID:25584012

How should a speech recognizer work?

PubMed

Scharenborg, Odette; Norris, Dennis; Bosch, Louis; McQueen, James M

2005-11-12

Although researchers studying human speech recognition (HSR) and automatic speech recognition (ASR) share a common interest in how information processing systems (human or machine) recognize spoken language, there is little communication between the two disciplines. We suggest that this lack of communication follows largely from the fact that research in these related fields has focused on the mechanics of how speech can be recognized. In Marr's (1982) terms, emphasis has been on the algorithmic and implementational levels rather than on the computational level. In this article, we provide a computational-level analysis of the task of speech recognition, which reveals the close parallels between research concerned with HSR and ASR. We illustrate this relation by presenting a new computational model of human spoken-word recognition, built using techniques from the field of ASR that, in contrast to current existing models of HSR, recognizes words from real speech input. 2005 Lawrence Erlbaum Associates, Inc.

Voice tracking and spoken word recognition in the presence of other voices

NASA Astrophysics Data System (ADS)

Litong-Palima, Marisciel; Violanda, Renante; Saloma, Caesar

2004-12-01

We study the human hearing process by modeling the hair cell as a thresholded Hopf bifurcator and compare our calculations with experimental results involving human subjects in two different multi-source listening tasks of voice tracking and spoken-word recognition. In the model, we observed noise suppression by destructive interference between noise sources which weakens the effective noise strength acting on the hair cell. Different success rate characteristics were observed for the two tasks. Hair cell performance at low threshold levels agree well with results from voice-tracking experiments while those of word-recognition experiments are consistent with a linear model of the hearing process. The ability of humans to track a target voice is robust against cross-talk interference unlike word-recognition performance which deteriorates quickly with the number of uncorrelated noise sources in the environment which is a response behavior that is associated with linear systems.

Simple thermal to thermal face verification method based on local texture descriptors

NASA Astrophysics Data System (ADS)

Grudzien, A.; Palka, Norbert; Kowalski, M.

2017-08-01

Biometrics is a science that studies and analyzes physical structure of a human body and behaviour of people. Biometrics found many applications ranging from border control systems, forensics systems for criminal investigations to systems for access control. Unique identifiers, also referred to as modalities are used to distinguish individuals. One of the most common and natural human identifiers is a face. As a result of decades of investigations, face recognition achieved high level of maturity, however recognition in visible spectrum is still challenging due to illumination aspects or new ways of spoofing. One of the alternatives is recognition of face in different parts of light spectrum, e.g. in infrared spectrum. Thermal infrared offer new possibilities for human recognition due to its specific properties as well as mature equipment. In this paper we present the scheme of subject's verification methodology by using facial images in thermal range. The study is focused on the local feature extraction methods and on the similarity metrics. We present comparison of two local texture-based descriptors for thermal 1-to-1 face recognition.

Recognizing Age-Separated Face Images: Humans and Machines

PubMed Central

Yadav, Daksha; Singh, Richa; Vatsa, Mayank; Noore, Afzel

2014-01-01

Humans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals. It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms. Such a study has two components - facial age estimation and age-separated face recognition. Age estimation involves predicting the age of an individual given his/her facial image. On the other hand, age-separated face recognition consists of recognizing an individual given his/her age-separated images. In this research, we investigate which facial cues are utilized by humans for estimating the age of people belonging to various age groups along with analyzing the effect of one's gender, age, and ethnicity on age estimation skills. We also analyze how various facial regions such as binocular and mouth regions influence age estimation and recognition capabilities. Finally, we propose an age-invariant face recognition algorithm that incorporates the knowledge learned from these observations. Key observations of our research are: (1) the age group of newborns and toddlers is easiest to estimate, (2) gender and ethnicity do not affect the judgment of age group estimation, (3) face as a global feature, is essential to achieve good performance in age-separated face recognition, and (4) the proposed algorithm yields improved recognition performance compared to existing algorithms and also outperforms a commercial system in the young image as probe scenario. PMID:25474200

Recognizing age-separated face images: humans and machines.

PubMed

Yadav, Daksha; Singh, Richa; Vatsa, Mayank; Noore, Afzel

2014-01-01

Humans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals. It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms. Such a study has two components--facial age estimation and age-separated face recognition. Age estimation involves predicting the age of an individual given his/her facial image. On the other hand, age-separated face recognition consists of recognizing an individual given his/her age-separated images. In this research, we investigate which facial cues are utilized by humans for estimating the age of people belonging to various age groups along with analyzing the effect of one's gender, age, and ethnicity on age estimation skills. We also analyze how various facial regions such as binocular and mouth regions influence age estimation and recognition capabilities. Finally, we propose an age-invariant face recognition algorithm that incorporates the knowledge learned from these observations. Key observations of our research are: (1) the age group of newborns and toddlers is easiest to estimate, (2) gender and ethnicity do not affect the judgment of age group estimation, (3) face as a global feature, is essential to achieve good performance in age-separated face recognition, and (4) the proposed algorithm yields improved recognition performance compared to existing algorithms and also outperforms a commercial system in the young image as probe scenario.

Humanized TLR4/MD-2 mice reveal LPS recognition differentially impacts susceptibility to Yersinia pestis and Salmonella enterica.

PubMed

Hajjar, Adeline M; Ernst, Robert K; Fortuno, Edgardo S; Brasfield, Alicia S; Yam, Cathy S; Newlon, Lindsay A; Kollmann, Tobias R; Miller, Samuel I; Wilson, Christopher B

2012-01-01

Although lipopolysaccharide (LPS) stimulation through the Toll-like receptor (TLR)-4/MD-2 receptor complex activates host defense against Gram-negative bacterial pathogens, how species-specific differences in LPS recognition impact host defense remains undefined. Herein, we establish how temperature dependent shifts in the lipid A of Yersinia pestis LPS that differentially impact recognition by mouse versus human TLR4/MD-2 dictate infection susceptibility. When grown at 37°C, Y. pestis LPS is hypo-acylated and less stimulatory to human compared with murine TLR4/MD-2. By contrast, when grown at reduced temperatures, Y. pestis LPS is more acylated, and stimulates cells equally via human and mouse TLR4/MD-2. To investigate how these temperature dependent shifts in LPS impact infection susceptibility, transgenic mice expressing human rather than mouse TLR4/MD-2 were generated. We found the increased susceptibility to Y. pestis for "humanized" TLR4/MD-2 mice directly paralleled blunted inflammatory cytokine production in response to stimulation with purified LPS. By contrast, for other Gram-negative pathogens with highly acylated lipid A including Salmonella enterica or Escherichia coli, infection susceptibility and the response after stimulation with LPS were indistinguishable between mice expressing human or mouse TLR4/MD-2. Thus, Y. pestis exploits temperature-dependent shifts in LPS acylation to selectively evade recognition by human TLR4/MD-2 uncovered with "humanized" TLR4/MD-2 transgenic mice.

Performing speech recognition research with hypercard

NASA Technical Reports Server (NTRS)

Shepherd, Chip

1993-01-01

The purpose of this paper is to describe a HyperCard-based system for performing speech recognition research and to instruct Human Factors professionals on how to use the system to obtain detailed data about the user interface of a prototype speech recognition application.

Pose Invariant Face Recognition Based on Hybrid Dominant Frequency Features

NASA Astrophysics Data System (ADS)

Wijaya, I. Gede Pasek Suta; Uchimura, Keiichi; Hu, Zhencheng

Face recognition is one of the most active research areas in pattern recognition, not only because the face is a human biometric characteristics of human being but also because there are many potential applications of the face recognition which range from human-computer interactions to authentication, security, and surveillance. This paper presents an approach to pose invariant human face image recognition. The proposed scheme is based on the analysis of discrete cosine transforms (DCT) and discrete wavelet transforms (DWT) of face images. From both the DCT and DWT domain coefficients, which describe the facial information, we build compact and meaningful features vector, using simple statistical measures and quantization. This feature vector is called as the hybrid dominant frequency features. Then, we apply a combination of the L2 and Lq metric to classify the hybrid dominant frequency features to a person's class. The aim of the proposed system is to overcome the high memory space requirement, the high computational load, and the retraining problems of previous methods. The proposed system is tested using several face databases and the experimental results are compared to a well-known Eigenface method. The proposed method shows good performance, robustness, stability, and accuracy without requiring geometrical normalization. Furthermore, the purposed method has low computational cost, requires little memory space, and can overcome retraining problem.

Recognition of Frequency Modulated Whistle-Like Sounds by a Bottlenose Dolphin (Tursiops truncatus) and Humans with Transformations in Amplitude, Duration and Frequency.

PubMed

Branstetter, Brian K; DeLong, Caroline M; Dziedzic, Brandon; Black, Amy; Bakhtiari, Kimberly

2016-01-01

Bottlenose dolphins (Tursiops truncatus) use the frequency contour of whistles produced by conspecifics for individual recognition. Here we tested a bottlenose dolphin's (Tursiops truncatus) ability to recognize frequency modulated whistle-like sounds using a three alternative matching-to-sample paradigm. The dolphin was first trained to select a specific object (object A) in response to a specific sound (sound A) for a total of three object-sound associations. The sounds were then transformed by amplitude, duration, or frequency transposition while still preserving the frequency contour of each sound. For comparison purposes, 30 human participants completed an identical task with the same sounds, objects, and training procedure. The dolphin's ability to correctly match objects to sounds was robust to changes in amplitude with only a minor decrement in performance for short durations. The dolphin failed to recognize sounds that were frequency transposed by plus or minus ½ octaves. Human participants demonstrated robust recognition with all acoustic transformations. The results indicate that this dolphin's acoustic recognition of whistle-like sounds was constrained by absolute pitch. Unlike human speech, which varies considerably in average frequency, signature whistles are relatively stable in frequency, which may have selected for a whistle recognition system invariant to frequency transposition.

Recognition of Frequency Modulated Whistle-Like Sounds by a Bottlenose Dolphin (Tursiops truncatus) and Humans with Transformations in Amplitude, Duration and Frequency

PubMed Central

Branstetter, Brian K.; DeLong, Caroline M.; Dziedzic, Brandon; Black, Amy; Bakhtiari, Kimberly

2016-01-01

Bottlenose dolphins (Tursiops truncatus) use the frequency contour of whistles produced by conspecifics for individual recognition. Here we tested a bottlenose dolphin’s (Tursiops truncatus) ability to recognize frequency modulated whistle-like sounds using a three alternative matching-to-sample paradigm. The dolphin was first trained to select a specific object (object A) in response to a specific sound (sound A) for a total of three object-sound associations. The sounds were then transformed by amplitude, duration, or frequency transposition while still preserving the frequency contour of each sound. For comparison purposes, 30 human participants completed an identical task with the same sounds, objects, and training procedure. The dolphin’s ability to correctly match objects to sounds was robust to changes in amplitude with only a minor decrement in performance for short durations. The dolphin failed to recognize sounds that were frequency transposed by plus or minus ½ octaves. Human participants demonstrated robust recognition with all acoustic transformations. The results indicate that this dolphin’s acoustic recognition of whistle-like sounds was constrained by absolute pitch. Unlike human speech, which varies considerably in average frequency, signature whistles are relatively stable in frequency, which may have selected for a whistle recognition system invariant to frequency transposition. PMID:26863519

Loss-of-function mutations in sodium channel Nav1.7 cause anosmia

PubMed Central

Weiss, Jan; Pyrski, Martina; Jacobi, Eric; Bufe, Bernd; Willnecker, Vivienne; Schick, Bernhard; Zizzari, Philippe; Gossage, Samuel J.; Greer, Charles A.; Leinders-Zufall, Trese; Woods, C. Geoffrey; Wood, John N.; Zufall, Frank

2013-01-01

Loss of function of the gene SCN9A, encoding the voltage-gated sodium channel Nav1.7, causes a congenital inability to experience pain in humans. Here we show that Nav1.7 is not only necessary for pain sensation but is also an essential requirement for odour perception in both mice and humans. We examined human patients with loss-of-function mutations in SCN9A and show that they are unable to sense odours. To establish the essential role of Nav1.7 in odour perception, we generated conditional null mice in which Nav1.7 was removed from all olfactory sensory neurons. In the absence of Nav1.7, these neurons still produce odour-evoked action potentials but fail to initiate synaptic signalling from their axon terminals at the first synapse in the olfactory system. The mutant mice no longer display vital, odour-guided behaviours such as innate odour recognition and avoidance, short-term odour learning, and maternal pup retrieval. Our study creates a mouse model of congenital general anosmia and provides new strategies to explore the genetic basis of the human sense of smell. PMID:21441906

Loss-of-function mutations in sodium channel Nav1.7 cause anosmia.

PubMed

Weiss, Jan; Pyrski, Martina; Jacobi, Eric; Bufe, Bernd; Willnecker, Vivienne; Schick, Bernhard; Zizzari, Philippe; Gossage, Samuel J; Greer, Charles A; Leinders-Zufall, Trese; Woods, C Geoffrey; Wood, John N; Zufall, Frank

2011-04-14

Loss of function of the gene SCN9A, encoding the voltage-gated sodium channel Na(v)1.7, causes a congenital inability to experience pain in humans. Here we show that Na(v)1.7 is not only necessary for pain sensation but is also an essential requirement for odour perception in both mice and humans. We examined human patients with loss-of-function mutations in SCN9A and show that they are unable to sense odours. To establish the essential role of Na(v)1.7 in odour perception, we generated conditional null mice in which Na(v)1.7 was removed from all olfactory sensory neurons. In the absence of Na(v)1.7, these neurons still produce odour-evoked action potentials but fail to initiate synaptic signalling from their axon terminals at the first synapse in the olfactory system. The mutant mice no longer display vital, odour-guided behaviours such as innate odour recognition and avoidance, short-term odour learning, and maternal pup retrieval. Our study creates a mouse model of congenital general anosmia and provides new strategies to explore the genetic basis of the human sense of smell.

A Novel Locally Linear KNN Method With Applications to Visual Recognition.

PubMed

Liu, Qingfeng; Liu, Chengjun

2017-09-01

A locally linear K Nearest Neighbor (LLK) method is presented in this paper with applications to robust visual recognition. Specifically, the concept of an ideal representation is first presented, which improves upon the traditional sparse representation in many ways. The objective function based on a host of criteria for sparsity, locality, and reconstruction is then optimized to derive a novel representation, which is an approximation to the ideal representation. The novel representation is further processed by two classifiers, namely, an LLK-based classifier and a locally linear nearest mean-based classifier, for visual recognition. The proposed classifiers are shown to connect to the Bayes decision rule for minimum error. Additional new theoretical analysis is presented, such as the nonnegative constraint, the group regularization, and the computational efficiency of the proposed LLK method. New methods such as a shifted power transformation for improving reliability, a coefficients' truncating method for enhancing generalization, and an improved marginal Fisher analysis method for feature extraction are proposed to further improve visual recognition performance. Extensive experiments are implemented to evaluate the proposed LLK method for robust visual recognition. In particular, eight representative data sets are applied for assessing the performance of the LLK method for various visual recognition applications, such as action recognition, scene recognition, object recognition, and face recognition.

Why the long face? The importance of vertical image structure for biological "barcodes" underlying face recognition.

PubMed

Spence, Morgan L; Storrs, Katherine R; Arnold, Derek H

2014-07-29

Humans are experts at face recognition. The mechanisms underlying this complex capacity are not fully understood. Recently, it has been proposed that face recognition is supported by a coarse-scale analysis of visual information contained in horizontal bands of contrast distributed along the vertical image axis-a biological facial "barcode" (Dakin & Watt, 2009). A critical prediction of the facial barcode hypothesis is that the distribution of image contrast along the vertical axis will be more important for face recognition than image distributions along the horizontal axis. Using a novel paradigm involving dynamic image distortions, a series of experiments are presented examining famous face recognition impairments from selectively disrupting image distributions along the vertical or horizontal image axes. Results show that disrupting the image distribution along the vertical image axis is more disruptive for recognition than matched distortions along the horizontal axis. Consistent with the facial barcode hypothesis, these results suggest that human face recognition relies disproportionately on appropriately scaled distributions of image contrast along the vertical image axis. © 2014 ARVO.

Behavioral model of visual perception and recognition

NASA Astrophysics Data System (ADS)

Rybak, Ilya A.; Golovan, Alexander V.; Gusakova, Valentina I.

1993-09-01

In the processes of visual perception and recognition human eyes actively select essential information by way of successive fixations at the most informative points of the image. A behavioral program defining a scanpath of the image is formed at the stage of learning (object memorizing) and consists of sequential motor actions, which are shifts of attention from one to another point of fixation, and sensory signals expected to arrive in response to each shift of attention. In the modern view of the problem, invariant object recognition is provided by the following: (1) separated processing of `what' (object features) and `where' (spatial features) information at high levels of the visual system; (2) mechanisms of visual attention using `where' information; (3) representation of `what' information in an object-based frame of reference (OFR). However, most recent models of vision based on OFR have demonstrated the ability of invariant recognition of only simple objects like letters or binary objects without background, i.e. objects to which a frame of reference is easily attached. In contrast, we use not OFR, but a feature-based frame of reference (FFR), connected with the basic feature (edge) at the fixation point. This has provided for our model, the ability for invariant representation of complex objects in gray-level images, but demands realization of behavioral aspects of vision described above. The developed model contains a neural network subsystem of low-level vision which extracts a set of primary features (edges) in each fixation, and high- level subsystem consisting of `what' (Sensory Memory) and `where' (Motor Memory) modules. The resolution of primary features extraction decreases with distances from the point of fixation. FFR provides both the invariant representation of object features in Sensor Memory and shifts of attention in Motor Memory. Object recognition consists in successive recall (from Motor Memory) and execution of shifts of attention and successive verification of the expected sets of features (stored in Sensory Memory). The model shows the ability of recognition of complex objects (such as faces) in gray-level images invariant with respect to shift, rotation, and scale.

Inferential false memories of events: negative consequences protect from distortions when the events are free from further elaboration.

PubMed

Mirandola, Chiara; Toffalini, Enrico; Grassano, Massimo; Cornoldi, Cesare; Melinder, Annika

2014-01-01

The present experiment was conducted to investigate whether negative emotionally charged and arousing content of to-be-remembered scripted material would affect propensity towards memory distortions. We further investigated whether elaboration of the studied material through free recall would affect the magnitude of memory errors. In this study participants saw eight scripts. Each of the scripts included an effect of an action, the cause of which was not presented. Effects were either negatively emotional or neutral. Participants were assigned to either a yes/no recognition test group (recognition), or to a recall and yes/no recognition test group (elaboration + recognition). Results showed that participants in the recognition group produced fewer memory errors in the emotional condition. Conversely, elaboration + recognition participants had lower accuracy and produced more emotional memory errors than the other group, suggesting a mediating role of semantic elaboration on the generation of false memories. The role of emotions and semantic elaboration on the generation of false memories is discussed.

A Robust and Device-Free System for the Recognition and Classification of Elderly Activities.

PubMed

Li, Fangmin; Al-Qaness, Mohammed Abdulaziz Aide; Zhang, Yong; Zhao, Bihai; Luan, Xidao

2016-12-01

Human activity recognition, tracking and classification is an essential trend in assisted living systems that can help support elderly people with their daily activities. Traditional activity recognition approaches depend on vision-based or sensor-based techniques. Nowadays, a novel promising technique has obtained more attention, namely device-free human activity recognition that neither requires the target object to wear or carry a device nor install cameras in a perceived area. The device-free technique for activity recognition uses only the signals of common wireless local area network (WLAN) devices available everywhere. In this paper, we present a novel elderly activities recognition system by leveraging the fluctuation of the wireless signals caused by human motion. We present an efficient method to select the correct data from the Channel State Information (CSI) streams that were neglected in previous approaches. We apply a Principle Component Analysis method that exposes the useful information from raw CSI. Thereafter, Forest Decision (FD) is adopted to classify the proposed activities and has gained a high accuracy rate. Extensive experiments have been conducted in an indoor environment to test the feasibility of the proposed system with a total of five volunteer users. The evaluation shows that the proposed system is applicable and robust to electromagnetic noise.

An investigation of potential applications of OP-SAPS: Operational Sampled Analog Processors

NASA Technical Reports Server (NTRS)

Parrish, E. A.; Mcvey, E. S.

1977-01-01

The application of OP-SAP's (operational sampled analog processors) in pattern recognition system is summarized. Areas investigated include: (1) human face recognition; (2) a high-speed programmable transversal filter system; (3) discrete word (speech) recognition; and (4) a resolution enhancement system.

Reading comprehension of health checkup reports and health literacy in Japanese people.

PubMed

Suka, Machi; Odajima, Takeshi; Okamoto, Masako; Sumitani, Masahiko; Nakayama, Takeo; Sugimori, Hiroki

2014-07-01

To determine the reading comprehension of health checkup reports in the context of health literacy (HL) in Japanese people. A web-based survey was conducted among 424 Japanese adults aged 35-59 years. Participants were asked to read specifically designed health checkup reports and then answer a series of questions to examine whether they accomplished the fundamental purposes of health checkup reports (recognition of the problems, recognition of the risk of illness, recognition of the need for preventive action, and motivation for preventive action). HL was simultaneously measured using the 14-item health literacy scale (HLS-14), the 11-item Lipkus scale (Lipkus-J), and the Newest Vital Sign (NVS-J). About 70 % of the study subjects misread the normal/abnormal classification for at least one items. Those with lower HLS-14 scores were significantly less likely to recognize the problems, the risk of illness, and the need for preventive action for the examinee, and also less likely to express their willingness to take preventive action in compliance with the doctor's advice after having received the health checkup report. Compared with the HLS-14 scores, the Lipkus-J and NVS-J scores showed hardly any association with the reading comprehension of health checkup reports. All examinees do not always have an adequate level of HL. HL may be the major determinant of reading comprehension of health checkup reports. For more effective health checkups, health promotion service providers should become aware of the existence of examinees with inadequate HL and address the problem of misreading health checkup results.

A Novel Energy-Efficient Approach for Human Activity Recognition.

PubMed

Zheng, Lingxiang; Wu, Dihong; Ruan, Xiaoyang; Weng, Shaolin; Peng, Ao; Tang, Biyu; Lu, Hai; Shi, Haibin; Zheng, Huiru

2017-09-08

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper.

Current trends in small vocabulary speech recognition for equipment control

NASA Astrophysics Data System (ADS)

Doukas, Nikolaos; Bardis, Nikolaos G.

2017-09-01

Speech recognition systems allow human - machine communication to acquire an intuitive nature that approaches the simplicity of inter - human communication. Small vocabulary speech recognition is a subset of the overall speech recognition problem, where only a small number of words need to be recognized. Speaker independent small vocabulary recognition can find significant applications in field equipment used by military personnel. Such equipment may typically be controlled by a small number of commands that need to be given quickly and accurately, under conditions where delicate manual operations are difficult to achieve. This type of application could hence significantly benefit by the use of robust voice operated control components, as they would facilitate the interaction with their users and render it much more reliable in times of crisis. This paper presents current challenges involved in attaining efficient and robust small vocabulary speech recognition. These challenges concern feature selection, classification techniques, speaker diversity and noise effects. A state machine approach is presented that facilitates the voice guidance of different equipment in a variety of situations.

Optimized Periocular Template Selection for Human Recognition

PubMed Central

Sa, Pankaj K.; Majhi, Banshidhar

2013-01-01

A novel approach for selecting a rectangular template around periocular region optimally potential for human recognition is proposed. A comparatively larger template of periocular image than the optimal one can be slightly more potent for recognition, but the larger template heavily slows down the biometric system by making feature extraction computationally intensive and increasing the database size. A smaller template, on the contrary, cannot yield desirable recognition though the smaller template performs faster due to low computation for feature extraction. These two contradictory objectives (namely, (a) to minimize the size of periocular template and (b) to maximize the recognition through the template) are aimed to be optimized through the proposed research. This paper proposes four different approaches for dynamic optimal template selection from periocular region. The proposed methods are tested on publicly available unconstrained UBIRISv2 and FERET databases and satisfactory results have been achieved. Thus obtained template can be used for recognition of individuals in an organization and can be generalized to recognize every citizen of a nation. PMID:23984370

A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer.

PubMed

Khan, Adil Mehmood; Lee, Young-Koo; Lee, Sungyoung Y; Kim, Tae-Seong

2010-09-01

Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.

To award a Congressional Gold Medal to Dr. Muhammad Yunus, in recognition of his contributions to the fight against global poverty.

THOMAS, 111th Congress

Rep. Holt, Rush [D-NJ-12

2009-04-21

House - 04/21/2009 Referred to the House Committee on Financial Services. (All Actions) Notes: For further action, see S.846, which became Public Law 111-253 on 10/5/2010. Tracker: This bill has the status IntroducedHere are the steps for Status of Legislation:

Indigenous Schooling Grants in Chile: The Impacts of an Integrationist Affirmative Action Policy among Mapuche Pupils

ERIC Educational Resources Information Center

Webb, Andrew

2015-01-01

This article assesses the extent to which indigenous grants administered to school pupils and university students in Chile can be considered affirmative action towards social justice. Drawing on Fraser's framework for parity of participation, I question whether the grants are able to provide both redistribution and recognition for indigenous…

Decoding Actions and Emotions in Deaf Children: Evidence from a Biological Motion Task

ERIC Educational Resources Information Center

Ludlow, Amanda Katherine; Heaton, Pamela; Deruelle, Christine

2013-01-01

This study aimed to explore the recognition of emotional and non-emotional biological movements in children with severe and profound deafness. Twenty-four deaf children, together with 24 control children matched on mental age and 24 control children matched on chronological age, were asked to identify a person's actions, subjective states,…

3 CFR 8867 - Proclamation 8867 of September 20, 2012. National POW/MIA Recognition Day, 2012

Code of Federal Regulations, 2013 CFR

2013-01-01

... black and white banner symbolizing America's Missing in Action and Prisoners of War will be flown over... United States of America A Proclamation For more than two centuries, Americans have bravely served our... tribute to service members who bore war's tragic costs as prisoners of war and those missing in action. We...

Sex steroids: beyond conventional dimorphism.

PubMed

Lavranos, Giagkos; Angelopoulou, Roxani; Manolakou, Panagiota; Katsiki, Evangelia

2013-09-01

Sexual dimorphism is a characteristic of a large number of species, ranging from lower invertebrates to mammals and, last but not least, humans. Recognition of the various factors regulating sexual dimorphism initial establishment (i.e. sex determination and differentiation) and subsequent life-long adaptation to distinct functional and behavioural patterns has remained a hot topic for several decades. As our understanding of the various molecular pathways involved in this process increases, the significant role of sex steroids becomes more evident. At the same time, the recognition of new sites of steroid production (e.g. parts of the brain) and aromatization, as well as new target cells (owing to the proposed presence of additional receptors to those classically considered as primary steroid receptors) has lead to the need to revisit their spectrum of actions within a novel, multifactorial context. Thus, anthropology and medicine are presented with the challenge to unravel a major mystery, i.e. that of sexual orientation and differentiation and its potential contribution in human evolution and civilization development, taking advantage of the high-tech research tools provided by modern biotechnology. This short review summarizes the basic principles of sex determination and sex steroid function as they have been classically described in the literature and then proceeds to present examples of how modern research methods have started to offer a new insight on the more subtle details of this process, stressing that it is extending to virtually every single part and system of the body.

Antimicrobial peptide hLF1-11 directs granulocyte-macrophage colony-stimulating factor-driven monocyte differentiation toward macrophages with enhanced recognition and clearance of pathogens.

PubMed

van der Does, Anne M; Bogaards, Sylvia J P; Ravensbergen, Bep; Beekhuizen, Henry; van Dissel, Jaap T; Nibbering, Peter H

2010-02-01

The human lactoferrin-derived peptide hLF1-11 displays antimicrobial activities in vitro and is effective against infections with antibiotic-resistant bacteria and fluconazole-resistant Candida albicans in animals. However, the mechanisms underlying these activities remain largely unclear. Since hLF1-11 is ineffective in vitro at physiological salt concentrations, we suggested modulation of the immune system as an additional mechanism of action of the peptide. We investigated whether hLF1-11 affects human monocyte-macrophage differentiation and determined the antimicrobial activities of the resulting macrophages. Monocytes were cultured for 7 days with GM-CSF in the presence of hLF1-11, control peptide, or saline for various intervals. At day 6, the cells were stimulated with lipopolysaccharide (LPS), lipoteichoic acid (LTA), or heat-killed C. albicans for 24 h. Thereafter, the levels of cytokines in the culture supernatants, the expression of pathogen recognition receptors, and the antimicrobial activities of these macrophages were determined. The results showed that a short exposure of monocytes to hLF1-11 during GM-CSF-driven differentiation is sufficient to direct differentiation of monocytes toward a macrophage subset characterized by both pro- and anti-inflammatory cytokine production and increased responsiveness to microbial structures. Moreover, these macrophages are highly effective against C. albicans and Staphylococcus aureus. In conclusion, hLF1-11 directs GM-CSF-driven differentiation of monocytes toward macrophages with enhanced effector functions.

Gender Recognition from Point-Light Walkers

ERIC Educational Resources Information Center

Pollick, Frank E.; Kay, Jim W.; Heim, Katrin; Stringer, Rebecca

2005-01-01

Point-light displays of human gait provide information sufficient to recognize the gender of a walker and are taken as evidence of the exquisite tuning of the visual system to biological motion. The authors revisit this topic with the goals of quantifying human efficiency at gender recognition. To achieve this, the authors first derive an ideal…

Development of Flexible Visual Recognition Memory in Human Infants

ERIC Educational Resources Information Center

Robinson, Astri J.; Pascalis, Olivier

2004-01-01

Research using the visual paired comparison task has shown that visual recognition memory across changing contexts is dependent on the integrity of the hippocampal formation in human adults and in monkeys. The acquisition of contextual flexibility may contribute to the change in memory performance that occurs late in the first year of life. To…

Human gait recognition by pyramid of HOG feature on silhouette images

NASA Astrophysics Data System (ADS)

Yang, Guang; Yin, Yafeng; Park, Jeanrok; Man, Hong

2013-03-01

As a uncommon biometric modality, human gait recognition has a great advantage of identify people at a distance without high resolution images. It has attracted much attention in recent years, especially in the fields of computer vision and remote sensing. In this paper, we propose a human gait recognition framework that consists of a reliable background subtraction method followed by the pyramid of Histogram of Gradient (pHOG) feature extraction on the silhouette image, and a Hidden Markov Model (HMM) based classifier. Through background subtraction, the silhouette of human gait in each frame is extracted and normalized from the raw video sequence. After removing the shadow and noise in each region of interest (ROI), pHOG feature is computed on the silhouettes images. Then the pHOG features of each gait class will be used to train a corresponding HMM. In the test stage, pHOG feature will be extracted from each test sequence and used to calculate the posterior probability toward each trained HMM model. Experimental results on the CASIA Gait Dataset B1 demonstrate that with our proposed method can achieve very competitive recognition rate.

Analysis of the ability of pramlintide to inhibit amyloid formation by human islet amyloid polypeptide reveals a balance between optimal recognition and reduced amyloidogenicity.

PubMed

Wang, Hui; Ridgway, Zachary; Cao, Ping; Ruzsicska, Bela; Raleigh, Daniel P

2015-11-10

The hormone human islet amyloid polypeptide (hIAPP or amylin) plays a role in glucose metabolism, but forms amyloid in the pancreas in type 2 diabetes (T2D) and is associated with β-cell death and dysfunction in the disease. Inhibitors of islet amyloid have therapeutic potential; however, there are no clinically approved inhibitors, and the mode of action of existing inhibitors is not well understood. Rat IAPP (rIAPP) differs from hIAPP at six positions, does not form amyloid, and is an inhibitor of amyloid formation by hIAPP. Five of the six differences are located within the segment of residues 20-29, and three of them are Pro residues, which are well-known disruptors of β-sheet structure. rIAPP is thus a natural example of a "β-breaker inhibitor", a molecule that combines a recognition element with an entity that inhibits β-sheet formation. Pramlintide (PM) is a peptide drug approved for use as an adjunct to insulin therapy for treatment of diabetes. PM was developed by introducing the three Pro substitutions found in rIAPP into hIAPP. Thus, it more closely resembles the human peptide than does rIAPP. Here we examine and compare the ability of rIAPP, PM, and a set of designed analogues of hIAPP to inhibit amyloid formation by hIAPP, to elucidate the factors that lead to effective peptide-based inhibitors. Our results reveal, for this class of molecules, a balance between the reduced amyloidogenicity of the inhibitory sequence on one hand and its ability to recognize hIAPP on the other.

Aluminum Exposure at Human Dietary Levels for 60 Days Reaches a Threshold Sufficient to Promote Memory Impairment in Rats.

PubMed

Martinez, Caroline S; Alterman, Caroline D C; Peçanha, Franck M; Vassallo, Dalton V; Mello-Carpes, Pâmela B; Miguel, Marta; Wiggers, Giulia A

2017-01-01

Aluminum (Al) is a significant environmental contaminant. While a good deal of research has been conducted on the acute neurotoxic effects of Al, little is known about the effects of longer-term exposure at human dietary Al levels. Therefore, the purpose of this study was to investigate the effects of 60-day Al exposure at low doses for comparison with a model of exposure known to produce neurotoxicity in rats. Three-month-old male Wistar rats were divided into two major groups: (1) low aluminum levels, and (2) a high aluminum level. Group 1 rats were treated orally by drinking water for 60 days as follows: (a) control-received ultrapure drinking water; (b) aluminum at 1.5 mg/kg b.w., and (c) aluminum at 8.3 mg/kg b.w. Group 2 rats were treated through oral gavages for 42 days as follows: (a) control-received ultrapure water; (b) aluminum at 100 mg/kg b.w. We analyzed cognitive parameters, biomarkers of oxidative stress and acetylcholinesterase (AChE) activity in hippocampus and prefrontal cortex. Al treatment even at low doses promoted recognition memory impairment seen in object recognition memory testing. Moreover, Al increased hippocampal reactive oxygen species and lipid peroxidation, reduced antioxidant capacity, and decreased AChE activity. Our data demonstrate that 60-day subchronic exposure to low doses of Al from feed and added to the water, which reflect human dietary Al intake, reaches a threshold sufficient to promote memory impairment and neurotoxicity. The elevation of oxidative stress and cholinergic dysfunction highlight pathways of toxic actions for this metal.

Spatial-frequency spectra of printed characters and human visual perception.

PubMed

Põder, Endel

2003-06-01

It is well known that certain spatial frequency (SF) bands are more important than others for character recognition. Solomon and Pelli [Nature 369 (1994) 395-397] have concluded that human pattern recognition mechanism is able to use only a narrow band from available SF spectrum of letters. However, the SF spectra of letters themselves have not been studied carefully. Here I report the results of an analysis of SF spectra of printed characters and discuss their relationship to the observed band-pass nature of letter recognition.

Status report on speech research. A report on the status and progress of studies of the nature of speech, instrumentation for its investigation and practical applications

NASA Astrophysics Data System (ADS)

Studdert-Kennedy, M.; Obrien, N.

1983-05-01

This report is one of a regular series on the status and progress of studies on the nature of speech, instrumentation for its investigation, and practical applications. Manuscripts cover the following topics: The influence of subcategorical mismatches on lexical access; The Serbo-Croatian orthography constraints the reader to a phonologically analytic strategy; Grammatical priming effects between pronouns and inflected verb forms; Misreadings by beginning readers of Serrbo-Croatian; Bi-alphabetism and work recognition; Orthographic and phonemic coding for word identification: Evidence for Hebrew; Stress and vowel duration effects on syllable recognition; Phonetic and auditory trading relations between acoustic cues in speech perception: Further results; Linguistic coding by deaf children in relation beginning reading success; Determinants of spelling ability in deaf and hearing adults: Access to linguistic structures; A dynamical basis for action systems; On the space-time structure of human interlimb coordination; Some acoustic and physiological observations on diphthongs; Relationship between pitch control and vowel articulation; Laryngeal vibrations: A comparison between high-speed filming and glottographic techniques; Compensatory articulation in hearing impaired speakers: A cinefluorographic study; and Review (Pierre Delattre: Studies in comparative phonetics.)

The role of the hippocampus in recognition memory.

PubMed

Bird, Chris M

2017-08-01

Many theories of declarative memory propose that it is supported by partially separable processes underpinned by different brain structures. The hippocampus plays a critical role in binding together item and contextual information together and processing the relationships between individual items. By contrast, the processing of individual items and their later recognition can be supported by extrahippocampal regions of the medial temporal lobes (MTL), particularly when recognition is based on feelings of familiarity without the retrieval of any associated information. These theories are domain-general in that "items" might be words, faces, objects, scenes, etc. However, there is mixed evidence that item recognition does not require the hippocampus, or that familiarity-based recognition can be supported by extrahippocampal regions. By contrast, there is compelling evidence that in humans, hippocampal damage does not affect recognition memory for unfamiliar faces, whilst recognition memory for several other stimulus classes is impaired. I propose that regions outside of the hippocampus can support recognition of unfamiliar faces because they are perceived as discrete items and have no prior conceptual associations. Conversely, extrahippocampal processes are inadequate for recognition of items which (a) have been previously experienced, (b) are conceptually meaningful, or (c) are perceived as being comprised of individual elements. This account reconciles findings from primate and human studies of recognition memory. Furthermore, it suggests that while the hippocampus is critical for binding and relational processing, these processes are required for item recognition memory in most situations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Quality of life differences in patients with right- versus left-sided facial paralysis: Universal preference of right-sided human face recognition.

PubMed

Ryu, Nam Gyu; Lim, Byung Woo; Cho, Jae Keun; Kim, Jin

2016-09-01

We investigated whether experiencing right- or left-sided facial paralysis would affect an individual's ability to recognize one side of the human face using hybrid hemi-facial photos by preliminary study. Further investigation looked at the relationship between facial recognition ability, stress, and quality of life. To investigate predominance of one side of the human face for face recognition, 100 normal participants (right-handed: n = 97, left-handed: n = 3, right brain dominance: n = 56, left brain dominance: n = 44) answered a questionnaire that included hybrid hemi-facial photos developed to determine decide superiority of one side for human face recognition. To determine differences of stress level and quality of life between individuals experiencing right- and left-sided facial paralysis, 100 patients (right side:50, left side:50, not including traumatic facial nerve paralysis) answered a questionnaire about facial disability index test and quality of life (SF-36 Korean version). Regardless of handedness or hemispheric dominance, the proportion of predominance of the right side in human face recognition was larger than the left side (71% versus 12%, neutral: 17%). Facial distress index of the patients with right-sided facial paralysis was lower than that of left-sided patients (68.8 ± 9.42 versus 76.4 ± 8.28), and the SF-36 scores of right-sided patients were lower than left-sided patients (119.07 ± 15.24 versus 123.25 ± 16.48, total score: 166). Universal preference for the right side in human face recognition showed worse psychological mood and social interaction in patients with right-side facial paralysis than left-sided paralysis. This information is helpful to clinicians in that psychological and social factors should be considered when treating patients with facial-paralysis. Copyright © 2016 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

The current state of play of research on the social, political and legal dimensions of HIV

PubMed Central

Paiva, Vera; Ferguson, Laura; Aggleton, Peter; Mane, Purnima; Kelly-Hanku, Angela; Giang, Le Minh; Barbosa, Regina M.; Caceres, Carlos F.; Parker, Richard

2015-01-01

This paper offers a critical overview of social science research presented at the 2014 International AIDS Conference in Melbourne, Australia. In an era of major biomedical advance, the political nature of HIV remains of fundamental importance. No new development can be rolled out successfully without taking into account its social and political context, and consequences. Four main themes ran throughout the conference track on social and political research, law, policy and human rights: first, the importance of work with socially vulnerable groups, now increasingly referred to as “key populations”; second, continued recognition that actions and programs need to be tailored locally and contextually; third, the need for an urgent response to a rapidly growing epidemic of HIV among young people; and fourth, the negative effects of the growing criminalization of minority sexualities and people living with HIV. Lack of stress on human rights and community participation is resulting in poorer policy globally. A new research agenda is needed to respond to these challenges. PMID:25859715

Manufacturing Natural Killer Cells as Medicinal Products

PubMed Central

Chabannon, Christian; Mfarrej, Bechara; Guia, Sophie; Ugolini, Sophie; Devillier, Raynier; Blaise, Didier; Vivier, Eric; Calmels, Boris

2016-01-01

Natural Killer (NK) cells are innate lymphoid cells (ILC) with cytotoxic and regulatory properties. Their functions are tightly regulated by an array of inhibitory and activating receptors, and their mechanisms of activation strongly differ from antigen recognition in the context of human leukocyte antigen presentation as needed for T-cell activation. NK cells thus offer unique opportunities for new and improved therapeutic manipulation, either in vivo or in vitro, in a variety of human diseases, including cancers. NK cell activity can possibly be modulated in vivo through direct or indirect actions exerted by small molecules or monoclonal antibodies. NK cells can also be adoptively transferred following more or less substantial modifications through cell and gene manufacturing, in order to empower them with new or improved functions and ensure their controlled persistence and activity in the recipient. In the present review, we will focus on the technological and regulatory challenges of NK cell manufacturing and discuss conditions in which these innovative cellular therapies can be brought to the clinic. PMID:27895646

Recognition Is Still a Top Motivator.

ERIC Educational Resources Information Center

Cherrington, David J.; Wixom, B. Jackson, Jr.

1983-01-01

Motivation theories can be generalized to a common principle of human behavior: people do what they are reinforced or rewarded for doing. The most successful motivational recognition programs share five key elements: a recognition symbol, an attractive means of display, a meaningful presentation, effective promotion, and periodic evaluation. (MLF)

Cognitive Processing Hardware Elements

DTIC Science & Technology

2005-01-31

characters. Results will be presented below. 1 4. Recognition of human faces. There are many other possible applications such as facial recognition and...For the experiments in facial recognition , we have used a 3-layer autoassociative neural network having the following specifications: "* The input...using the facial recognition system described in the section above as an example. This system uses an autoassociative neural network containing over 10

Influence of music with different volumes and styles on recognition activity in humans.

PubMed

Pavlygina, R A; Sakharov, D S; Davydov, V I; Avdonkin, A V

2010-10-01

The efficiency of the recognition of masked visual images (Arabic numerals) increased when accompanied by classical (62 dB) and rock music (25 dB). These changes were accompanied by increases in the coherence of potentials in the frontal areas seen on recognition without music. Changes in intercenter EEG relationships correlated with the formation a dominant at the behavioral level. When loud music (85 dB) and music of other styles was used, these changes in behavior and the EEG were not seen; however, the coherence of potentials in the temporal and motor cortex of the right hemisphere increased and the latent periods of motor reactions of the hands decreased. These results provide evidence that the "recognition" dominant is formed when there are particular ratios of the levels of excitation in the corresponding centers, which should be considered when there is a need to increase the efficiency of recognition activity in humans.

Advances in image compression and automatic target recognition; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

NASA Technical Reports Server (NTRS)

Tescher, Andrew G. (Editor)

1989-01-01

Various papers on image compression and automatic target recognition are presented. Individual topics addressed include: target cluster detection in cluttered SAR imagery, model-based target recognition using laser radar imagery, Smart Sensor front-end processor for feature extraction of images, object attitude estimation and tracking from a single video sensor, symmetry detection in human vision, analysis of high resolution aerial images for object detection, obscured object recognition for an ATR application, neural networks for adaptive shape tracking, statistical mechanics and pattern recognition, detection of cylinders in aerial range images, moving object tracking using local windows, new transform method for image data compression, quad-tree product vector quantization of images, predictive trellis encoding of imagery, reduced generalized chain code for contour description, compact architecture for a real-time vision system, use of human visibility functions in segmentation coding, color texture analysis and synthesis using Gibbs random fields.

Feature Selection in Classification of Eye Movements Using Electrooculography for Activity Recognition

PubMed Central

Mala, S.; Latha, K.

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition. PMID:25574185

Feature selection in classification of eye movements using electrooculography for activity recognition.

PubMed

Mala, S; Latha, K

2014-01-01

Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.

A Random Forest-based ensemble method for activity recognition.

PubMed

Feng, Zengtao; Mo, Lingfei; Li, Meng

2015-01-01

This paper presents a multi-sensor ensemble approach to human physical activity (PA) recognition, using random forest. We designed an ensemble learning algorithm, which integrates several independent Random Forest classifiers based on different sensor feature sets to build a more stable, more accurate and faster classifier for human activity recognition. To evaluate the algorithm, PA data collected from the PAMAP (Physical Activity Monitoring for Aging People), which is a standard, publicly available database, was utilized to train and test. The experimental results show that the algorithm is able to correctly recognize 19 PA types with an accuracy of 93.44%, while the training is faster than others. The ensemble classifier system based on the RF (Random Forest) algorithm can achieve high recognition accuracy and fast calculation.

Multi-resolution analysis for ear recognition using wavelet features

NASA Astrophysics Data System (ADS)

Shoaib, M.; Basit, A.; Faye, I.

2016-11-01

Security is very important and in order to avoid any physical contact, identification of human when they are moving is necessary. Ear biometric is one of the methods by which a person can be identified using surveillance cameras. Various techniques have been proposed to increase the ear based recognition systems. In this work, a feature extraction method for human ear recognition based on wavelet transforms is proposed. The proposed features are approximation coefficients and specific details of level two after applying various types of wavelet transforms. Different wavelet transforms are applied to find the suitable wavelet. Minimum Euclidean distance is used as a matching criterion. Results achieved by the proposed method are promising and can be used in real time ear recognition system.

"Doing the heavy lifting: health care workers take back their backs".

PubMed

Morse, Tim; Fekieta, Renee; Rubenstein, Harriet; Warren, Nick; Alexander, Darryl; Wawzyniecki, Patricia

2008-01-01

Health care workers have the highest musculoskeletal disorder prevalence and incidence of any occupational/industry group, and patient handling tasks are so biomechanically demanding that they cannot be made safe through the commonly used, technique-oriented methods such as "back school" training programs. Although there is standard-setting activity for "no-lift" programs in some states, there is still no federal standard. Health care worker unions and nurses' associations have begun to take action through training members in equipment need, use, and acceptance in programs to encourage adoption of no-lifting programs. Acceptance of lifting equipment is increasing due to recognition of the high human and economic costs of MSD, consistent documentation of cost savings from no-lift programs, major improvements in lifting equipment, and shortages of health care staff. An action-oriented training program for health care workers is described that provides knowledge about the 1) Scope of the current problem of back injuries in health care, 2) Costs of injuries, both to workers and to the hospital, 3) Elements of a safe patient-handling program, and 4) Success stories. The program also builds skills through: 1) Hands-on experience with safe lifting equipment, and 2) Assessing organizational and union readiness and planning for action at the workplace.

Lumbee Recognition Act

THOMAS, 112th Congress

Sen. Burr, Richard [R-NC

2011-06-16

Senate - 08/02/2012 Placed on Senate Legislative Calendar under General Orders. Calendar No. 490. (All Actions) Tracker: This bill has the status IntroducedHere are the steps for Status of Legislation:

Lumbee Recognition Act

THOMAS, 111th Congress

Sen. Burr, Richard [R-NC

2009-10-01

Senate - 01/20/2010 Placed on Senate Legislative Calendar under General Orders. Calendar No. 256. (All Actions) Tracker: This bill has the status IntroducedHere are the steps for Status of Legislation:

Human target acquisition performance

NASA Astrophysics Data System (ADS)

Teaney, Brian P.; Du Bosq, Todd W.; Reynolds, Joseph P.; Thompson, Roger; Aghera, Sameer; Moyer, Steven K.; Flug, Eric; Espinola, Richard; Hixson, Jonathan

2012-06-01

The battlefield has shifted from armored vehicles to armed insurgents. Target acquisition (identification, recognition, and detection) range performance involving humans as targets is vital for modern warfare. The acquisition and neutralization of armed insurgents while at the same time minimizing fratricide and civilian casualties is a mounting concern. U.S. Army RDECOM CERDEC NVESD has conducted many experiments involving human targets for infrared and reflective band sensors. The target sets include human activities, hand-held objects, uniforms & armament, and other tactically relevant targets. This paper will define a set of standard task difficulty values for identification and recognition associated with human target acquisition performance.

Sketching for Military Courses of Action Diagrams

DTIC Science & Technology

2003-01-01

the glyph bar and (optionally) spoken input2. Avoiding the need for recognition in glyphs Glyphs in nuSketch systems have two parts. The ink is the...time-stamped collection of ink strokes that comprise the base- level visual representation of the glyph. The content of the glyph is an entity in...preferred having a neat symbol drawn where they wanted it. Those who had tried ink recognition systems particularly appreciated never having to

Interpreting Chicken-Scratch: Lexical Access for Handwritten Words

ERIC Educational Resources Information Center

Barnhart, Anthony S.; Goldinger, Stephen D.

2010-01-01

Handwritten word recognition is a field of study that has largely been neglected in the psychological literature, despite its prevalence in society. Whereas studies of spoken word recognition almost exclusively employ natural, human voices as stimuli, studies of visual word recognition use synthetic typefaces, thus simplifying the process of word…

Recognition-by-Components: A Theory of Human Image Understanding.

ERIC Educational Resources Information Center

Biederman, Irving

1987-01-01

The theory proposed (recognition-by-components) hypothesizes the perceptual recognition of objects to be a process in which the image of the input is segmented at regions of deep concavity into an arrangement of simple geometric components. Experiments on the perception of briefly presented pictures support the theory. (Author/LMO)

Real-time 3D human pose recognition from reconstructed volume via voxel classifiers

NASA Astrophysics Data System (ADS)

Yoo, ByungIn; Choi, Changkyu; Han, Jae-Joon; Lee, Changkyo; Kim, Wonjun; Suh, Sungjoo; Park, Dusik; Kim, Junmo

2014-03-01

This paper presents a human pose recognition method which simultaneously reconstructs a human volume based on ensemble of voxel classifiers from a single depth image in real-time. The human pose recognition is a difficult task since a single depth camera can capture only visible surfaces of a human body. In order to recognize invisible (self-occluded) surfaces of a human body, the proposed algorithm employs voxel classifiers trained with multi-layered synthetic voxels. Specifically, ray-casting onto a volumetric human model generates a synthetic voxel, where voxel consists of a 3D position and ID corresponding to the body part. The synthesized volumetric data which contain both visible and invisible body voxels are utilized to train the voxel classifiers. As a result, the voxel classifiers not only identify the visible voxels but also reconstruct the 3D positions and the IDs of the invisible voxels. The experimental results show improved performance on estimating the human poses due to the capability of inferring the invisible human body voxels. It is expected that the proposed algorithm can be applied to many fields such as telepresence, gaming, virtual fitting, wellness business, and real 3D contents control on real 3D displays.

Model-Driven Study of Visual Memory

DTIC Science & Technology

2004-12-01

dimensional stimuli (synthetic human faces ) afford important insights into episodic recognition memory. The results were well accommodated by a summed...the unusual properties of the z-transformed ROCS. 15. SUBJECT TERMS Memory, visual memory, computational model, human memory, faces , identity 16...3 Accomplishments/New Findings 3 Work on Objective One: Recognition Memory for Synthetic Faces . 3 Experim ent 1

Multimedia Content Development as a Facial Expression Datasets for Recognition of Human Emotions

NASA Astrophysics Data System (ADS)

Mamonto, N. E.; Maulana, H.; Liliana, D. Y.; Basaruddin, T.

2018-02-01

Datasets that have been developed before contain facial expression from foreign people. The development of multimedia content aims to answer the problems experienced by the research team and other researchers who will conduct similar research. The method used in the development of multimedia content as facial expression datasets for human emotion recognition is the Villamil-Molina version of the multimedia development method. Multimedia content developed with 10 subjects or talents with each talent performing 3 shots with each capturing talent having to demonstrate 19 facial expressions. After the process of editing and rendering, tests are carried out with the conclusion that the multimedia content can be used as a facial expression dataset for recognition of human emotions.

Recognition of Propionibacterium acnes by human TLR2 heterodimers.

PubMed

Su, Qi; Grabowski, Maria; Weindl, Günther

2017-02-01

Propionibacterium acnes has been considered as a crucial contributor to the pathogenesis of acne vulgaris. The interaction between P. acnes and the host is mainly mediated by Toll like receptor (TLR) 2 recognition. TLR2 homodimers recognize P. acnes in mice, but here we describe the prerequisite of TLR2/1 and TLR2/6 heterodimers in human cells for P. acnes recognition. P. acnes-induced NF-κB and AP-1activation observed in HEK hTLR2-transfected but not control cells confirmed the specificity of TLR2 recognition. The activation was blocked by neutralizing antibodies against TLR2, TLR1 and TLR6, as well as the TLR2 antagonist CU-CPT22, which showed no selectivity towards human TLR2 heterodimers. The combination of anti-TLR1 and anti-TLR6 antibodies completely abrogated activation by P. acnes. In primary human keratinocytes, P. acnes-increased NF-κB phosphorylation was inhibited by anti-TLR6 and anti-TLR2 antibodies. Furthermore, P. acnes-induced inflammatory responses were impaired by anti-TLR2 neutralizing antibodies and fully blocked by CU-CPT22. Our study suggests species-specific recognition of P. acnes by TLR2 heterodimers which can be exploited therapeutically by small molecules targeting TLR2 for the control of inflammatory responses. Copyright © 2016 Elsevier GmbH. All rights reserved.

Humans and Deep Networks Largely Agree on Which Kinds of Variation Make Object Recognition Harder.

PubMed

Kheradpisheh, Saeed R; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée

2016-01-01

View-invariant object recognition is a challenging problem that has attracted much attention among the psychology, neuroscience, and computer vision communities. Humans are notoriously good at it, even if some variations are presumably more difficult to handle than others (e.g., 3D rotations). Humans are thought to solve the problem through hierarchical processing along the ventral stream, which progressively extracts more and more invariant visual features. This feed-forward architecture has inspired a new generation of bio-inspired computer vision systems called deep convolutional neural networks (DCNN), which are currently the best models for object recognition in natural images. Here, for the first time, we systematically compared human feed-forward vision and DCNNs at view-invariant object recognition task using the same set of images and controlling the kinds of transformation (position, scale, rotation in plane, and rotation in depth) as well as their magnitude, which we call "variation level." We used four object categories: car, ship, motorcycle, and animal. In total, 89 human subjects participated in 10 experiments in which they had to discriminate between two or four categories after rapid presentation with backward masking. We also tested two recent DCNNs (proposed respectively by Hinton's group and Zisserman's group) on the same tasks. We found that humans and DCNNs largely agreed on the relative difficulties of each kind of variation: rotation in depth is by far the hardest transformation to handle, followed by scale, then rotation in plane, and finally position (much easier). This suggests that DCNNs would be reasonable models of human feed-forward vision. In addition, our results show that the variation levels in rotation in depth and scale strongly modulate both humans' and DCNNs' recognition performances. We thus argue that these variations should be controlled in the image datasets used in vision research.

Lumbee Recognition Act

THOMAS, 113th Congress

Sen. Burr, Richard [R-NC

2013-06-11

Senate - 10/30/2013 Committee on Indian Affairs. Hearings held. Hearings printed: S.Hrg. 113-219. (All Actions) Tracker: This bill has the status IntroducedHere are the steps for Status of Legislation:

Segmenting Continuous Motions with Hidden Semi-markov Models and Gaussian Processes

PubMed Central

Nakamura, Tomoaki; Nagai, Takayuki; Mochihashi, Daichi; Kobayashi, Ichiro; Asoh, Hideki; Kaneko, Masahide

2017-01-01

Humans divide perceived continuous information into segments to facilitate recognition. For example, humans can segment speech waves into recognizable morphemes. Analogously, continuous motions are segmented into recognizable unit actions. People can divide continuous information into segments without using explicit segment points. This capacity for unsupervised segmentation is also useful for robots, because it enables them to flexibly learn languages, gestures, and actions. In this paper, we propose a Gaussian process-hidden semi-Markov model (GP-HSMM) that can divide continuous time series data into segments in an unsupervised manner. Our proposed method consists of a generative model based on the hidden semi-Markov model (HSMM), the emission distributions of which are Gaussian processes (GPs). Continuous time series data is generated by connecting segments generated by the GP. Segmentation can be achieved by using forward filtering-backward sampling to estimate the model's parameters, including the lengths and classes of the segments. In an experiment using the CMU motion capture dataset, we tested GP-HSMM with motion capture data containing simple exercise motions; the results of this experiment showed that the proposed GP-HSMM was comparable with other methods. We also conducted an experiment using karate motion capture data, which is more complex than exercise motion capture data; in this experiment, the segmentation accuracy of GP-HSMM was 0.92, which outperformed other methods. PMID:29311889

A triboelectric motion sensor in wearable body sensor network for human activity recognition.

PubMed

Hui Huang; Xian Li; Ye Sun

2016-08-01

The goal of this study is to design a novel triboelectric motion sensor in wearable body sensor network for human activity recognition. Physical activity recognition is widely used in well-being management, medical diagnosis and rehabilitation. Other than traditional accelerometers, we design a novel wearable sensor system based on triboelectrification. The triboelectric motion sensor can be easily attached to human body and collect motion signals caused by physical activities. The experiments are conducted to collect five common activity data: sitting and standing, walking, climbing upstairs, downstairs, and running. The k-Nearest Neighbor (kNN) clustering algorithm is adopted to recognize these activities and validate the feasibility of this new approach. The results show that our system can perform physical activity recognition with a successful rate over 80% for walking, sitting and standing. The triboelectric structure can also be used as an energy harvester for motion harvesting due to its high output voltage in random low-frequency motion.

Sound quality recognition using optimal wavelet-packet transform and artificial neural network methods

NASA Astrophysics Data System (ADS)

Xing, Y. F.; Wang, Y. S.; Shi, L.; Guo, H.; Chen, H.

2016-01-01

According to the human perceptional characteristics, a method combined by the optimal wavelet-packet transform and artificial neural network, so-called OWPT-ANN model, for psychoacoustical recognition is presented. Comparisons of time-frequency analysis methods are performed, and an OWPT with 21 critical bands is designed for feature extraction of a sound, as is a three-layer back-propagation ANN for sound quality (SQ) recognition. Focusing on the loudness and sharpness, the OWPT-ANN model is applied on vehicle noises under different working conditions. Experimental verifications show that the OWPT can effectively transfer a sound into a time-varying energy pattern as that in the human auditory system. The errors of loudness and sharpness of vehicle noise from the OWPT-ANN are all less than 5%, which suggest a good accuracy of the OWPT-ANN model in SQ recognition. The proposed methodology might be regarded as a promising technique for signal processing in the human-hearing related fields in engineering.

Hand gesture recognition in confined spaces with partial observability and occultation constraints

NASA Astrophysics Data System (ADS)

Shirkhodaie, Amir; Chan, Alex; Hu, Shuowen

2016-05-01

Human activity detection and recognition capabilities have broad applications for military and homeland security. These tasks are very complicated, however, especially when multiple persons are performing concurrent activities in confined spaces that impose significant obstruction, occultation, and observability uncertainty. In this paper, our primary contribution is to present a dedicated taxonomy and kinematic ontology that are developed for in-vehicle group human activities (IVGA). Secondly, we describe a set of hand-observable patterns that represents certain IVGA examples. Thirdly, we propose two classifiers for hand gesture recognition and compare their performance individually and jointly. Finally, we present a variant of Hidden Markov Model for Bayesian tracking, recognition, and annotation of hand motions, which enables spatiotemporal inference to human group activity perception and understanding. To validate our approach, synthetic (graphical data from virtual environment) and real physical environment video imagery are employed to verify the performance of these hand gesture classifiers, while measuring their efficiency and effectiveness based on the proposed Hidden Markov Model for tracking and interpreting dynamic spatiotemporal IVGA scenarios.

Threshold concepts: implications for the management of natural resources

USGS Publications Warehouse

Guntenspergen, Glenn R.; Gross, John

2014-01-01

Threshold concepts can have broad relevance in natural resource management. However, the concept of ecological thresholds has not been widely incorporated or adopted in management goals. This largely stems from the uncertainty revolving around threshold levels and the post hoc analyses that have generally been used to identify them. Natural resource managers have a need for new tools and approaches that will help them assess the existence and detection of conditions that demand management actions. Recognition of additional threshold concepts include: utility thresholds (which are based on human values about ecological systems) and decision thresholds (which reflect management objectives and values and include ecological knowledge about a system) as well as ecological thresholds. All of these concepts provide a framework for considering the use of threshold concepts in natural resource decision making.

Error Rates in Users of Automatic Face Recognition Software

PubMed Central

White, David; Dunn, James D.; Schmid, Alexandra C.; Kemp, Richard I.

2015-01-01

In recent years, wide deployment of automatic face recognition systems has been accompanied by substantial gains in algorithm performance. However, benchmarking tests designed to evaluate these systems do not account for the errors of human operators, who are often an integral part of face recognition solutions in forensic and security settings. This causes a mismatch between evaluation tests and operational accuracy. We address this by measuring user performance in a face recognition system used to screen passport applications for identity fraud. Experiment 1 measured target detection accuracy in algorithm-generated ‘candidate lists’ selected from a large database of passport images. Accuracy was notably poorer than in previous studies of unfamiliar face matching: participants made over 50% errors for adult target faces, and over 60% when matching images of children. Experiment 2 then compared performance of student participants to trained passport officers–who use the system in their daily work–and found equivalent performance in these groups. Encouragingly, a group of highly trained and experienced “facial examiners” outperformed these groups by 20 percentage points. We conclude that human performance curtails accuracy of face recognition systems–potentially reducing benchmark estimates by 50% in operational settings. Mere practise does not attenuate these limits, but superior performance of trained examiners suggests that recruitment and selection of human operators, in combination with effective training and mentorship, can improve the operational accuracy of face recognition systems. PMID:26465631

The processing of linear perspective and binocular information for action and perception.

PubMed

Bruggeman, Hugo; Yonas, Albert; Konczak, Jürgen

2007-04-08

To investigate the processing of linear perspective and binocular information for action and for the perceptual judgment of depth, we presented viewers with an actual Ames trapezoidal window. The display, when presented perpendicular to the line of sight, provided perspective information for a rectangular window slanted in depth, while binocular information specified a planar surface in the fronto-parallel plane. We compared pointing towards the display-edges with perceptual judgment of their positions in depth as the display orientation was varied under monocular and binocular view. On monocular trials, pointing and depth judgment were based on the perspective information and failed to respond accurately to changes in display orientation because pictorial information did not vary sufficiently to specify the small differences in orientation. For binocular trials, pointing was based on binocular information and precisely matched the changes in display orientation whereas depth judgment was short of such adjustment and based upon both binocular and perspective-specified slant information. The finding, that on binocular trials pointing was considerably less responsive to the illusion than perceptual judgment, supports an account of two separate processing streams in the human visual system, a ventral pathway involved in object recognition and a dorsal pathway that produces visual information for the control of actions. Previously, similar differences between perception and action were explained by an alternate explanation, that is, viewers selectively attend to different parts of a display in the two tasks. The finding that under monocular view participants responded to perspective information in both the action and the perception task rules out the attention-based argument.

Transfer Learning for Improved Audio-Based Human Activity Recognition.

PubMed

Ntalampiras, Stavros; Potamitis, Ilyas

2018-06-25

Human activities are accompanied by characteristic sound events, the processing of which might provide valuable information for automated human activity recognition. This paper presents a novel approach addressing the case where one or more human activities are associated with limited audio data, resulting in a potentially highly imbalanced dataset. Data augmentation is based on transfer learning; more specifically, the proposed method: (a) identifies the classes which are statistically close to the ones associated with limited data; (b) learns a multiple input, multiple output transformation; and (c) transforms the data of the closest classes so that it can be used for modeling the ones associated with limited data. Furthermore, the proposed framework includes a feature set extracted out of signal representations of diverse domains, i.e., temporal, spectral, and wavelet. Extensive experiments demonstrate the relevance of the proposed data augmentation approach under a variety of generative recognition schemes.

Vasopressin modulates social recognition-related activity in the left temporoparietal junction in humans.

PubMed

Zink, C F; Kempf, L; Hakimi, S; Rainey, C A; Stein, J L; Meyer-Lindenberg, A

2011-04-04

The neuropeptide vasopressin is a key molecular mediator of social behavior in animals and humans, implicated in anxiety and autism. Social recognition, the ability to assess the familiarity of others, is essential for appropriate social interactions and enhanced by vasopressin; however, the neural mechanisms mediating this effect in humans are unknown. Using functional magnetic resonance imaging (fMRI) and an implicit social recognition matching task, we employed a double-blinded procedure in which 20 healthy male volunteers self-administered 40 UI of vasopressin or placebo intranasally, 45 min before performing the matching task in the scanner. In a random-effects fMRI analysis, we show that vasopressin induces a regionally specific alteration in a key node of the theory of mind network, the left temporoparietal junction, identifying a neurobiological mechanism for prosocial neuropeptide effects in humans that suggests novel treatment strategies.

Specificity and multiplicity in the recognition of individuals: implications for the evolution of social behaviour.

PubMed

Wiley, R H

2013-02-01

Recognition of conspecifics occurs when individuals classify sets of conspecifics based on sensory input from them and associate these sets with different responses. Classification of conspecifics can vary in specificity (the number of individuals included in a set) and multiplicity (the number of sets differentiated). In other words, the information transmitted varies in complexity. Although recognition of conspecifics has been reported in a wide variety of organisms, few reports have addressed the specificity or multiplicity of this capability. This review discusses examples of these patterns, the mechanisms that can produce them, and the evolution of these mechanisms. Individual recognition is one end of a spectrum of specificity, and binary classification of conspecifics is one end of a spectrum of multiplicity. In some cases, recognition requires no more than simple forms of learning, such as habituation, yet results in individually specific recognition. In other cases, recognition of individuals involves complex associations of multiple cues with multiple previous experiences in particular contexts. Complex mechanisms for recognition are expected to evolve only when simpler mechanisms do not provide sufficient specificity and multiplicity to obtain the available advantages. In particular, the evolution of cooperation and deception is always promoted by specificity and multiplicity in recognition. Nevertheless, there is only one demonstration that recognition of specific individuals contributes to cooperation in animals other than primates. Human capacities for individual recognition probably have a central role in the evolution of complex forms of human cooperation and deception. Although relatively little studied, this capability probably rivals cognitive abilities for language. © 2012 The Author. Biological Reviews © 2012 Cambridge Philosophical Society.

EMD-Based Symbolic Dynamic Analysis for the Recognition of Human and Nonhuman Pyroelectric Infrared Signals.

PubMed

Zhao, Jiaduo; Gong, Weiguo; Tang, Yuzhen; Li, Weihong

2016-01-20

In this paper, we propose an effective human and nonhuman pyroelectric infrared (PIR) signal recognition method to reduce PIR detector false alarms. First, using the mathematical model of the PIR detector, we analyze the physical characteristics of the human and nonhuman PIR signals; second, based on the analysis results, we propose an empirical mode decomposition (EMD)-based symbolic dynamic analysis method for the recognition of human and nonhuman PIR signals. In the proposed method, first, we extract the detailed features of a PIR signal into five symbol sequences using an EMD-based symbolization method, then, we generate five feature descriptors for each PIR signal through constructing five probabilistic finite state automata with the symbol sequences. Finally, we use a weighted voting classification strategy to classify the PIR signals with their feature descriptors. Comparative experiments show that the proposed method can effectively classify the human and nonhuman PIR signals and reduce PIR detector's false alarms.

Adaptive Parameter Estimation of Person Recognition Model in a Stochastic Human Tracking Process

NASA Astrophysics Data System (ADS)

Nakanishi, W.; Fuse, T.; Ishikawa, T.

2015-05-01

This paper aims at an estimation of parameters of person recognition models using a sequential Bayesian filtering method. In many human tracking method, any parameters of models used for recognize the same person in successive frames are usually set in advance of human tracking process. In real situation these parameters may change according to situation of observation and difficulty level of human position prediction. Thus in this paper we formulate an adaptive parameter estimation using general state space model. Firstly we explain the way to formulate human tracking in general state space model with their components. Then referring to previous researches, we use Bhattacharyya coefficient to formulate observation model of general state space model, which is corresponding to person recognition model. The observation model in this paper is a function of Bhattacharyya coefficient with one unknown parameter. At last we sequentially estimate this parameter in real dataset with some settings. Results showed that sequential parameter estimation was succeeded and were consistent with observation situations such as occlusions.

Linking functional diversity and social actor strategies in a framework for interdisciplinary analysis of nature's benefits to society

PubMed Central

Díaz, Sandra; Cáceres, Daniel M.; Trainor, Sarah F.; Pérez-Harguindeguy, Natalia; Bret-Harte, M. Syndonia; Finegan, Bryan; Peña-Claros, Marielos; Poorter, Lourens

2011-01-01

The crucial role of biodiversity in the links between ecosystems and societies has been repeatedly highlighted both as source of wellbeing and as a target of human actions, but not all aspects of biodiversity are equally important to different ecosystem services. Similarly, different social actors have different perceptions of and access to ecosystem services, and therefore, they have different wants and capacities to select directly or indirectly for particular biodiversity and ecosystem characteristics. Their choices feed back onto the ecosystem services provided to all parties involved and in turn, affect future decisions. Despite this recognition, the research communities addressing biodiversity, ecosystem services, and human outcomes have yet to develop frameworks that adequately treat the multiple dimensions and interactions in the relationship. Here, we present an interdisciplinary framework for the analysis of relationships between functional diversity, ecosystem services, and human actions that is applicable to specific social environmental systems at local scales. We connect the mechanistic understanding of the ecological role of diversity with its social relevance: ecosystem services. The framework permits connections between functional diversity components and priorities of social actors using land use decisions and ecosystem services as the main links between these ecological and social components. We propose a matrix-based method that provides a transparent and flexible platform for quantifying and integrating social and ecological information and negotiating potentially conflicting land uses among multiple social actors. We illustrate the applicability of our framework by way of land use examples from temperate to subtropical South America, an area of rapid social and ecological change. PMID:21220325

The Timing of the Cognitive Cycle

PubMed Central

Madl, Tamas; Baars, Bernard J.; Franklin, Stan

2011-01-01

We propose that human cognition consists of cascading cycles of recurring brain events. Each cognitive cycle senses the current situation, interprets it with reference to ongoing goals, and then selects an internal or external action in response. While most aspects of the cognitive cycle are unconscious, each cycle also yields a momentary “ignition” of conscious broadcasting. Neuroscientists have independently proposed ideas similar to the cognitive cycle, the fundamental hypothesis of the LIDA model of cognition. High-level cognition, such as deliberation, planning, etc., is typically enabled by multiple cognitive cycles. In this paper we describe a timing model LIDA's cognitive cycle. Based on empirical and simulation data we propose that an initial phase of perception (stimulus recognition) occurs 80–100 ms from stimulus onset under optimal conditions. It is followed by a conscious episode (broadcast) 200–280 ms after stimulus onset, and an action selection phase 60–110 ms from the start of the conscious phase. One cognitive cycle would therefore take 260–390 ms. The LIDA timing model is consistent with brain evidence indicating a fundamental role for a theta-gamma wave, spreading forward from sensory cortices to rostral corticothalamic regions. This posteriofrontal theta-gamma wave may be experienced as a conscious perceptual event starting at 200–280 ms post stimulus. The action selection component of the cycle is proposed to involve frontal, striatal and cerebellar regions. Thus the cycle is inherently recurrent, as the anatomy of the thalamocortical system suggests. The LIDA model fits a large body of cognitive and neuroscientific evidence. Finally, we describe two LIDA-based software agents: the LIDA Reaction Time agent that simulates human performance in a simple reaction time task, and the LIDA Allport agent which models phenomenal simultaneity within timeframes comparable to human subjects. While there are many models of reaction time performance, these results fall naturally out of a biologically and computationally plausible cognitive architecture. PMID:21541015

Recruitment of Foveal Retinotopic Cortex During Haptic Exploration of Shapes and Actions in the Dark.

PubMed

Monaco, Simona; Gallivan, Jason P; Figley, Teresa D; Singhal, Anthony; Culham, Jody C

2017-11-29

The role of the early visual cortex and higher-order occipitotemporal cortex has been studied extensively for visual recognition and to a lesser degree for haptic recognition and visually guided actions. Using a slow event-related fMRI experiment, we investigated whether tactile and visual exploration of objects recruit the same "visual" areas (and in the case of visual cortex, the same retinotopic zones) and if these areas show reactivation during delayed actions in the dark toward haptically explored objects (and if so, whether this reactivation might be due to imagery). We examined activation during visual or haptic exploration of objects and action execution (grasping or reaching) separated by an 18 s delay. Twenty-nine human volunteers (13 females) participated in this study. Participants had their eyes open and fixated on a point in the dark. The objects were placed below the fixation point and accordingly visual exploration activated the cuneus, which processes retinotopic locations in the lower visual field. Strikingly, the occipital pole (OP), representing foveal locations, showed higher activation for tactile than visual exploration, although the stimulus was unseen and location in the visual field was peripheral. Moreover, the lateral occipital tactile-visual area (LOtv) showed comparable activation for tactile and visual exploration. Psychophysiological interaction analysis indicated that the OP showed stronger functional connectivity with anterior intraparietal sulcus and LOtv during the haptic than visual exploration of shapes in the dark. After the delay, the cuneus, OP, and LOtv showed reactivation that was independent of the sensory modality used to explore the object. These results show that haptic actions not only activate "visual" areas during object touch, but also that this information appears to be used in guiding grasping actions toward targets after a delay. SIGNIFICANCE STATEMENT Visual presentation of an object activates shape-processing areas and retinotopic locations in early visual areas. Moreover, if the object is grasped in the dark after a delay, these areas show "reactivation." Here, we show that these areas are also activated and reactivated for haptic object exploration and haptically guided grasping. Touch-related activity occurs not only in the retinotopic location of the visual stimulus, but also at the occipital pole (OP), corresponding to the foveal representation, even though the stimulus was unseen and located peripherally. That is, the same "visual" regions are implicated in both visual and haptic exploration; however, touch also recruits high-acuity central representation within early visual areas during both haptic exploration of objects and subsequent actions toward them. Functional connectivity analysis shows that the OP is more strongly connected with ventral and dorsal stream areas when participants explore an object in the dark than when they view it. Copyright © 2017 the authors 0270-6474/17/3711572-20$15.00/0.

Do you remember proposing marriage to the Pepsi machine? False recollections from a campus walk.

PubMed

Seamon, John G; Philbin, Morgan M; Harrison, Liza G

2006-10-01

During a campus walk, participants were given familiar or bizarre action statements (e.g., "Check the Pepsi machine for change" vs. "Propose marriage to the Pepsi machine") with instructions either to perform the actions or imagine performing the actions (Group 1) or to watch the experimenter perform the actions or imagine the experimenter performing the actions (Group 2). One day later, some actions were repeated, along with new actions, on a second walk. Two weeks later, the participants took a recognition test for actions presented during the first walk, and they specified whether a recognized action was imagined or performed. Imagining themselves or the experimenter performing familiar or bizarre actions just once led to false recollections of performance for both types of actions. This study extends previous research on imagination inflation by demonstrating that these false performance recollections can occur in a natural, real-life setting following just one imagining.

42 CFR 403.322 - Termination of agreements for Medicare recognition of State systems.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 42 Public Health 2 2010-10-01 2010-10-01 false Termination of agreements for Medicare recognition of State systems. 403.322 Section 403.322 Public Health CENTERS FOR MEDICARE & MEDICAID SERVICES, DEPARTMENT OF HEALTH AND HUMAN SERVICES GENERAL PROVISIONS SPECIAL PROGRAMS AND PROJECTS Recognition of State...

21 CFR 310.533 - Drug products containing active ingredients offered over-the-counter (OTC) for human use as an...

Code of Federal Regulations, 2010 CFR

2010-04-01

...); however, there are inadequate data to establish general recognition of the effectiveness of this... milligram) but there are inadequate data to establish general recognition of the effectiveness of these... are inadequate safety and effectiveness data to establish general recognition of the safety and/or...

21 CFR 316.34 - FDA recognition of exclusive approval.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 5 2011-04-01 2011-04-01 false FDA recognition of exclusive approval. 316.34... (CONTINUED) DRUGS FOR HUMAN USE ORPHAN DRUGS Orphan-drug Exclusive Approval § 316.34 FDA recognition of exclusive approval. (a) FDA will send the sponsor (or, the permanent-resident agent, if applicable) timely...

21 CFR 316.34 - FDA recognition of exclusive approval.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 5 2012-04-01 2012-04-01 false FDA recognition of exclusive approval. 316.34... (CONTINUED) DRUGS FOR HUMAN USE ORPHAN DRUGS Orphan-drug Exclusive Approval § 316.34 FDA recognition of exclusive approval. (a) FDA will send the sponsor (or, the permanent-resident agent, if applicable) timely...

21 CFR 316.34 - FDA recognition of exclusive approval.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 5 2013-04-01 2013-04-01 false FDA recognition of exclusive approval. 316.34... (CONTINUED) DRUGS FOR HUMAN USE ORPHAN DRUGS Orphan-drug Exclusive Approval § 316.34 FDA recognition of exclusive approval. (a) FDA will send the sponsor (or, the permanent-resident agent, if applicable) timely...

21 CFR 316.34 - FDA recognition of exclusive approval.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 5 2014-04-01 2014-04-01 false FDA recognition of exclusive approval. 316.34... (CONTINUED) DRUGS FOR HUMAN USE ORPHAN DRUGS Orphan-drug Exclusive Approval § 316.34 FDA recognition of exclusive approval. (a) FDA will send the sponsor (or, the permanent-resident agent, if applicable) timely...

21 CFR 316.34 - FDA recognition of exclusive approval.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 5 2010-04-01 2010-04-01 false FDA recognition of exclusive approval. 316.34... (CONTINUED) DRUGS FOR HUMAN USE ORPHAN DRUGS Orphan-drug Exclusive Approval § 316.34 FDA recognition of exclusive approval. (a) FDA will send the sponsor (or, the permanent-resident agent, if applicable) timely...