Accurate identification of motor unit discharge patterns from high-density surface EMG and validation with a novel signal-based performance metric

NASA Astrophysics Data System (ADS)

Holobar, A.; Minetto, M. A.; Farina, D.

2014-02-01

Objective. A signal-based metric for assessment of accuracy of motor unit (MU) identification from high-density surface electromyograms (EMG) is introduced. This metric, so-called pulse-to-noise-ratio (PNR), is computationally efficient, does not require any additional experimental costs and can be applied to every MU that is identified by the previously developed convolution kernel compensation technique. Approach. The analytical derivation of the newly introduced metric is provided, along with its extensive experimental validation on both synthetic and experimental surface EMG signals with signal-to-noise ratios ranging from 0 to 20 dB and muscle contraction forces from 5% to 70% of the maximum voluntary contraction. Main results. In all the experimental and simulated signals, the newly introduced metric correlated significantly with both sensitivity and false alarm rate in identification of MU discharges. Practically all the MUs with PNR > 30 dB exhibited sensitivity >90% and false alarm rates <2%. Therefore, a threshold of 30 dB in PNR can be used as a simple method for selecting only reliably decomposed units. Significance. The newly introduced metric is considered a robust and reliable indicator of accuracy of MU identification. The study also shows that high-density surface EMG can be reliably decomposed at contraction forces as high as 70% of the maximum.

An EMG-CT method using multiple surface electrodes in the forearm.

PubMed

Nakajima, Yasuhiro; Keeratihattayakorn, Saran; Yoshinari, Satoshi; Tadano, Shigeru

2014-12-01

Electromyography computed tomography (EMG-CT) method is proposed for visualizing the individual muscle activities in the human forearm. An EMG conduction model was formulated for reverse-estimation of muscle activities using EMG signals obtained with multi surface electrodes. The optimization process was calculated using sequential quadratic programming by comparing the estimated EMG values from the model with the measured values. The individual muscle activities in the deep region were estimated and used to produce an EMG tomographic image. For validation of the method, isometric contractions of finger muscles were examined for three subjects, applying a flexion load (4.9, 7.4 and 9.8 N) to the proximal interphalangeal joint of the middle finger. EMG signals in the forearm were recorded during the tasks using multiple surface electrodes, which were bound around the subject's forearm. The EMG-CT method illustrates the distribution of muscle activities within the forearm. The change in amplitude and area of activated muscles can be observed. The normalized muscle activities of all three subjects appear to increase monotonically with increases in the load. Kinesiologically, this method was able to estimate individual muscle activation values and could provide a novel tool for studying hand function and development of an examination for evaluating rehabilitation. Copyright © 2014 Elsevier Ltd. All rights reserved.

Surface EMG system for use in long-term vigorous activities

NASA Astrophysics Data System (ADS)

de Luca, G.; Bergman, P.; de Luca, C.

The purpose of the project was to develop an advanced surface electromyographic (EMG) system that is portable, un-tethered, and able to detect high-fidelity EMG signals from multiple channels. The innovation was specifically designed to extend NASA's capability to perform neurological status monitoring for long-term, vigorous activities. These features are a necessary requirement of ground-based and in-flight studies planned for the International Space Station and human expeditions to Mars. The project consisted of developing 1) a portable EMG digital data logger using a handheld PC for acquiring the signal and storing the data from as many as 8 channels, and 2) an EMG electrode/skin interface to improve signal fidelity and skin adhesion in the presence of sweat and mechanical disturbances encountered during vigorous activities. The system, referred to as a MyoMonitor, was configured with a communication port for downloading the data from the data logger to the PC computer workstation. Software specifications were developed and implemented for programming of acquisition protocols, power management, and transferring data to the PC for processing and graphical display. The prototype MyoMonitor was implemented using a handheld PC that features a color LCD screen, enhanced keyboard, extended Lithium Ion battery and recharger, and 128 Mbytes of F ash Memory. The system was designed to be belt-worn,l thereby allowing its use under vigorous activities. The Monitor utilizes up to 8 differential surface EMG sensors. The prototype allowed greater than 2 hours of continuous 8-channel EMG data to be collected, or 17.2 hours of continuous single channel EMG data. Standardized tests in human subjects were conducted to develop the mechanical and electrical properties of the prototype electrode/interface system. Tests conducted during treadmill running and repetitive lifting demonstrated that the prototype interface significantly reduced the detrimental effects of sweat

Techniques of EMG signal analysis: detection, processing, classification and applications

PubMed Central

Hussain, M.S.; Mohd-Yasin, F.

2006-01-01

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications. PMID:16799694

Objective models of EMG signals for cyclic processes such as a human gait

NASA Astrophysics Data System (ADS)

Babska, Luiza; Selegrat, Monika; Dusza, Jacek J.

2016-09-01

EMG signals are small potentials appearing at the surface of human skin during muscle work. They arise due to changes in the physiological state of cell membranes in the muscle fibers. They are characterized by a relatively low frequency range (500 Hz) and a low amplitude signal (of the order of μV), making it difficult to record. Raw EMG signal is inherently random shape. However we can distinguish certain features related to the activation of the muscles of a deterministic or quasi-deterministic associated with the movement and its parametric description. Objective models of EMG signals were created on the base of actual data obtained from the VICON system installed at the University of Physical Education in Warsaw. The object of research (healthy woman) moved repeatedly after a fixed track. On her body 35 reflective markers to record the gait kinematics and 8 electrodes to record EMG signals were placed. We obtained research data included more than 1,000 EMG signals synchronized with the phases of gait. Test result of the work is an algorithm for obtaining the average EMG signal received from the multiple registration gait cycles carried out in the same reproducible conditions. The method described in the article is essentially a pre-finding measurement data from the two quasi-synchronous signals at different sampling frequencies for further processing. This signal is characterized by a significant reduction of high frequency noise and emphasis on the specific characteristics of individual records found in muscle activity.

Features extraction of EMG signal using time domain analysis for arm rehabilitation device

NASA Astrophysics Data System (ADS)

Jali, Mohd Hafiz; Ibrahim, Iffah Masturah; Sulaima, Mohamad Fani; Bukhari, W. M.; Izzuddin, Tarmizi Ahmad; Nasir, Mohamad Na'im

2015-05-01

Rehabilitation device is used as an exoskeleton for people who had failure of their limb. Arm rehabilitation device may help the rehab program whom suffers from arm disability. The device that is used to facilitate the tasks of the program should improve the electrical activity in the motor unit and minimize the mental effort of the user. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. In order to prevent the muscles from paralysis becomes spasticity, the force of movements should minimize the mental efforts. Therefore, the rehabilitation device should analyze the surface EMG signal of normal people that can be implemented to the device. The signal is collected according to procedure of surface electromyography for non-invasive assessment of muscles (SENIAM). The EMG signal is implemented to set the movements' pattern of the arm rehabilitation device. The filtered EMG signal was extracted for features of Standard Deviation (STD), Mean Absolute Value (MAV) and Root Mean Square (RMS) in time-domain. The extraction of EMG data is important to have the reduced vector in the signal features with less of error. In order to determine the best features for any movements, several trials of extraction methods are used by determining the features with less of errors. The accurate features can be use for future works of rehabilitation control in real-time.

A motion-classification strategy based on sEMG-EEG signal combination for upper-limb amputees.

PubMed

Li, Xiangxin; Samuel, Oluwarotimi Williams; Zhang, Xu; Wang, Hui; Fang, Peng; Li, Guanglin

2017-01-07

Most of the modern motorized prostheses are controlled with the surface electromyography (sEMG) recorded on the residual muscles of amputated limbs. However, the residual muscles are usually limited, especially after above-elbow amputations, which would not provide enough sEMG for the control of prostheses with multiple degrees of freedom. Signal fusion is a possible approach to solve the problem of insufficient control commands, where some non-EMG signals are combined with sEMG signals to provide sufficient information for motion intension decoding. In this study, a motion-classification method that combines sEMG and electroencephalography (EEG) signals were proposed and investigated, in order to improve the control performance of upper-limb prostheses. Four transhumeral amputees without any form of neurological disease were recruited in the experiments. Five motion classes including hand-open, hand-close, wrist-pronation, wrist-supination, and no-movement were specified. During the motion performances, sEMG and EEG signals were simultaneously acquired from the skin surface and scalp of the amputees, respectively. The two types of signals were independently preprocessed and then combined as a parallel control input. Four time-domain features were extracted and fed into a classifier trained by the Linear Discriminant Analysis (LDA) algorithm for motion recognition. In addition, channel selections were performed by using the Sequential Forward Selection (SFS) algorithm to optimize the performance of the proposed method. The classification performance achieved by the fusion of sEMG and EEG signals was significantly better than that obtained by single signal source of either sEMG or EEG. An increment of more than 14% in classification accuracy was achieved when using a combination of 32-channel sEMG and 64-channel EEG. Furthermore, based on the SFS algorithm, two optimized electrode arrangements (10-channel sEMG + 10-channel EEG, 10-channel sEMG + 20-channel

siGnum: graphical user interface for EMG signal analysis.

PubMed

Kaur, Manvinder; Mathur, Shilpi; Bhatia, Dinesh; Verma, Suresh

2015-01-01

Electromyography (EMG) signals that represent the electrical activity of muscles can be used for various clinical and biomedical applications. These are complicated and highly varying signals that are dependent on anatomical location and physiological properties of the muscles. EMG signals acquired from the muscles require advanced methods for detection, decomposition and processing. This paper proposes a novel Graphical User Interface (GUI) siGnum developed in MATLAB that will apply efficient and effective techniques on processing of the raw EMG signals and decompose it in a simpler manner. It could be used independent of MATLAB software by employing a deploy tool. This would enable researcher's to gain good understanding of EMG signal and its analysis procedures that can be utilized for more powerful, flexible and efficient applications in near future.

EMG-Torque Relation in Chronic Stroke: A Novel EMG Complexity Representation With a Linear Electrode Array.

PubMed

Zhang, Xu; Wang, Dongqing; Yu, Zaiyang; Chen, Xiang; Li, Sheng; Zhou, Ping

2017-11-01

This study examines the electromyogram (EMG)-torque relation for chronic stroke survivors using a novel EMG complexity representation. Ten stroke subjects performed a series of submaximal isometric elbow flexion tasks using their affected and contralateral arms, respectively, while a 20-channel linear electrode array was used to record surface EMG from the biceps brachii muscles. The sample entropy (SampEn) of surface EMG signals was calculated with both global and local tolerance schemes. A regression analysis was performed between SampEn of each channel's surface EMG and elbow flexion torque. It was found that a linear regression can be used to well describe the relation between surface EMG SampEn and the torque. Each channel's root mean square (RMS) amplitude of surface EMG signal in the different torque level was computed to determine the channel with the highest EMG amplitude. The slope of the regression (observed from the channel with the highest EMG amplitude) was smaller on the impaired side than on the nonimpaired side in 8 of the 10 subjects, regardless of the tolerance scheme (global or local) and the range of torques (full or matched range) used for comparison. The surface EMG signals from the channels above the estimated muscle innervation zones demonstrated significantly lower levels of complexity compared with other channels between innervation zones and muscle tendons. The study provides a novel point of view of the EMG-torque relation in the complexity domain, and reveals its alterations post stroke, which are associated with complex neural and muscular changes post stroke. The slope difference between channels with regard to innervation zones also confirms the relevance of electrode position in surface EMG analysis.

Long-term surface EMG monitoring using K-means clustering and compressive sensing

NASA Astrophysics Data System (ADS)

Balouchestani, Mohammadreza; Krishnan, Sridhar

2015-05-01

In this work, we present an advanced K-means clustering algorithm based on Compressed Sensing theory (CS) in combination with the K-Singular Value Decomposition (K-SVD) method for Clustering of long-term recording of surface Electromyography (sEMG) signals. The long-term monitoring of sEMG signals aims at recording of the electrical activity produced by muscles which are very useful procedure for treatment and diagnostic purposes as well as for detection of various pathologies. The proposed algorithm is examined for three scenarios of sEMG signals including healthy person (sEMG-Healthy), a patient with myopathy (sEMG-Myopathy), and a patient with neuropathy (sEMG-Neuropathr), respectively. The proposed algorithm can easily scan large sEMG datasets of long-term sEMG recording. We test the proposed algorithm with Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) dimensionality reduction methods. Then, the output of the proposed algorithm is fed to K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers in order to calclute the clustering performance. The proposed algorithm achieves a classification accuracy of 99.22%. This ability allows reducing 17% of Average Classification Error (ACE), 9% of Training Error (TE), and 18% of Root Mean Square Error (RMSE). The proposed algorithm also reduces 14% clustering energy consumption compared to the existing K-Means clustering algorithm.

Dynamical characteristics of surface EMG signals of hand grasps via recurrence plot.

PubMed

Ouyang, Gaoxiang; Zhu, Xiangyang; Ju, Zhaojie; Liu, Honghai

2014-01-01

Recognizing human hand grasp movements through surface electromyogram (sEMG) is a challenging task. In this paper, we investigated nonlinear measures based on recurrence plot, as a tool to evaluate the hidden dynamical characteristics of sEMG during four different hand movements. A series of experimental tests in this study show that the dynamical characteristics of sEMG data with recurrence quantification analysis (RQA) can distinguish different hand grasp movements. Meanwhile, adaptive neuro-fuzzy inference system (ANFIS) is applied to evaluate the performance of the aforementioned measures to identify the grasp movements. The experimental results show that the recognition rate (99.1%) based on the combination of linear and nonlinear measures is much higher than those with only linear measures (93.4%) or nonlinear measures (88.1%). These results suggest that the RQA measures might be a potential tool to reveal the sEMG hidden characteristics of hand grasp movements and an effective supplement for the traditional linear grasp recognition methods.

Analysis of surface EMG baseline for detection of hidden muscle activity

NASA Astrophysics Data System (ADS)

Zhang, Xu; Zhou, Ping

2014-02-01

Objective. This study explored the feasibility of detecting hidden muscle activity in surface electromyogram (EMG) baseline. Approach. Power spectral density (PSD) analysis and multi-scale entropy (MSE) analysis were used. Both analyses were applied to computer simulations of surface EMG baseline with the presence (representing activity data) or absence (representing reference data) of hidden muscle activity, as well as surface electrode array EMG baseline recordings of healthy control and amyotrophic lateral sclerosis (ALS) subjects. Main results. Although the simulated reference data and the activity data yielded no distinguishable difference in the time domain, they demonstrated a significant difference in the frequency and signal complexity domains with the PSD and MSE analyses. For a comparison using pooled data, such a difference was also observed when the PSD and MSE analyses were applied to surface electrode array EMG baseline recordings of healthy control and ALS subjects, which demonstrated no distinguishable difference in the time domain. Compared with the PSD analysis, the MSE analysis appeared to be more sensitive for detecting the difference in surface EMG baselines between the two groups. Significance. The findings implied the presence of a hidden muscle activity in surface EMG baseline recordings from the ALS subjects. To promote the presented analysis as a useful diagnostic or investigatory tool, future studies are necessary to assess the pathophysiological nature or origins of the hidden muscle activity, as well as the baseline difference at the individual subject level.

Analysis of Surface EMG Baseline for Detection of Hidden Muscle Activity

PubMed Central

Zhang, Xu; Zhou, Ping

2014-01-01

Objective This study explored the feasibility of detecting hidden muscle activity in surface electromyogram (EMG) baseline. Approach Power spectral density (PSD) analysis and multi-scale entropy (MSE) analysis were used respectively. Both analyses were applied to computer simulations of surface EMG baseline with presence (representing activity data) or absence (representing reference data) of hidden muscle activity, as well as surface electrode array EMG baseline recordings of healthy control and amyotrophic lateral sclerosis (ALS) subjects. Main results Although the simulated reference data and the activity data yielded no distinguishable difference in the time domain, they demonstrated a significant difference in the frequency and signal complexity domains with the PSD and MSE analyses. For a comparison using pooled data, such a difference was also observed when the PSD and MSE analyses were applied to surface electrode array EMG baseline recordings of healthy control and ALS subjects, which demonstrated no distinguishable difference in the time domain. Compared with the PSD analysis, the MSE analysis appeared to be more sensitive for detecting the difference in surface EMG baselines between the two groups. Significance The findings implied presence of hidden muscle activity in surface EMG baseline recordings from the ALS subjects. To promote the presented analysis as a useful diagnostic or investigatory tool, future studies are necessary to assess the pathophysiological nature or origins of the hidden muscle activity, as well as the baseline difference at the individual subject level. PMID:24445526

Relationship between grasping force and features of single-channel intramuscular EMG signals.

PubMed

Kamavuako, Ernest Nlandu; Farina, Dario; Yoshida, Ken; Jensen, Winnie

2009-12-15

The surface electromyographic (sEMG) signal can be used for force prediction and control in prosthetic devices. Because of technological advances on implantable sensors, the use of intramuscular EMG (iEMG) is becoming a potential alternative to sEMG for the control of multiple degrees-of-freedom (DOF). An invasive system is not affected by crosstalk, typical of sEMG, and provides more stable and independent control sites. However, intramuscular recordings provide more local information because of their high selectivity, and may thus be less representative of the global muscle activity with respect to sEMG. This study investigates the capacity of selective single-channel iEMG recordings to represent the grasping force with respect to the use of sEMG with the aim of assessing if iEMG can be an effective method for proportional myoelectric control. sEMG and iEMG were recorded concurrently from 10 subjects who exerted six grasping force profiles from 0 to 25/50N. The linear correlation coefficient between features extracted from iEMG and force was approximately 0.9 and was not significantly different from the degree of correlation between sEMG and force. This result indicates that a selective iEMG recording is representative of the applied grasping force and can be used for proportional control.

Frequency domain surface EMG sensor fusion for estimating finger forces.

PubMed

Potluri, Chandrasekhar; Kumar, Parmod; Anugolu, Madhavi; Urfer, Alex; Chiu, Steve; Naidu, D; Schoen, Marco P

2010-01-01

Extracting or estimating skeletal hand/finger forces using surface electro myographic (sEMG) signals poses many challenges due to cross-talk, noise, and a temporal and spatially modulated signal characteristics. Normal sEMG measurements are based on single sensor data. In this paper, array sensors are used along with a proposed sensor fusion scheme that result in a simple Multi-Input-Single-Output (MISO) transfer function. Experimental data is used along with system identification to find this MISO system. A Genetic Algorithm (GA) approach is employed to optimize the characteristics of the MISO system. The proposed fusion-based approach is tested experimentally and indicates improvement in finger/hand force estimation.

Motor unit size estimation: confrontation of surface EMG with macro EMG.

PubMed

Roeleveld, K; Stegeman, D F; Falck, B; Stålberg, E V

1997-06-01

Surface EMG (SEMG) is little used for diagnostic purposes in clinical neurophysiology, mainly because it provides little direct information on individual motor units (MUs). One of the techniques to estimate the MU size is intra-muscular Macro EMG. The present study compares SEMG with Macro EMG. Fifty-eight channel SEMG was recorded simultaneously with Macro EMG. Individual MUPs were obtained by single fiber triggered averaging. All recordings were made from the biceps brachii of healthy subjects during voluntary contraction at low force. High positive correlations were found between all Macro and Surface motor unit potential (MUP) parameters: area, peak-to-peak amplitude, negative peak amplitude and positive peak amplitude. The MUPs recorded with SEMG were dependent on the distance between the MU and the skin surface. Normalizing the SEMG parameters for MU location did not improve the correlation coefficient between the parameters of both techniques. The two measurement techniques had almost the same relative range in MUP parameters in any individual subject compared to the others, especially after normalizing the surface MUP parameters for MU location. MUPs recorded with this type of SEMG provide useful information about the MU size.

Identification of regional activation by factorization of high-density surface EMG signals: A comparison of Principal Component Analysis and Non-negative Matrix factorization.

PubMed

Gallina, Alessio; Garland, S Jayne; Wakeling, James M

2018-05-22

In this study, we investigated whether principal component analysis (PCA) and non-negative matrix factorization (NMF) perform similarly for the identification of regional activation within the human vastus medialis. EMG signals from 64 locations over the VM were collected from twelve participants while performing a low-force isometric knee extension. The envelope of the EMG signal of each channel was calculated by low-pass filtering (8 Hz) the monopolar EMG signal after rectification. The data matrix was factorized using PCA and NMF, and up to 5 factors were considered for each algorithm. Association between explained variance, spatial weights and temporal scores between the two algorithms were compared using Pearson correlation. For both PCA and NMF, a single factor explained approximately 70% of the variance of the signal, while two and three factors explained just over 85% or 90%. The variance explained by PCA and NMF was highly comparable (R > 0.99). Spatial weights and temporal scores extracted with non-negative reconstruction of PCA and NMF were highly associated (all p < 0.001, mean R > 0.97). Regional VM activation can be identified using high-density surface EMG and factorization algorithms. Regional activation explains up to 30% of the variance of the signal, as identified through both PCA and NMF. Copyright © 2018 Elsevier Ltd. All rights reserved.

Effect of spatial filtering on crosstalk reduction in surface EMG recordings.

PubMed

Mesin, Luca; Smith, Stuart; Hugo, Suzanne; Viljoen, Suretha; Hanekom, Tania

2009-04-01

Increasing the selectivity of the detection system in surface electromyography (EMG) is beneficial in the collection of information of a specific portion of the investigated muscle and to reduce the contribution of undesired components, such as non-propagating components (due to generation or end-of-fibre effects) or crosstalk from nearby muscles. A comparison of the ability of different spatial filters to reduce the amount of crosstalk in surface EMG measurements was conducted in this paper using simulated signals. It focused on the influence of different properties of the muscle anatomy (changing subcutaneous layer thickness, skin conductivity, fibre length) and detection system (single, double and normal double differential, with two inter-electrode distances - IED) on the amount of crosstalk present in the measurements. A cylindrical multilayer (skin, subcutaneous tissue, muscle, bone) analytical model was used to simulate single fibre action potentials (SFAPs). Fibres were grouped together in motor units (MUs) and motor unit action potentials (MUAPs) were obtained by adding the SFAPs of the corresponding fibres. Interference surface EMG signals were obtained, modelling the recruitment of MUs and rate coding. The average rectified value (ARV) and mean frequency (MNF) content of the EMG signals were studied and used as a basis for determining the selectivity of each spatial filter. From these results it was found that the selectivity of each spatial filter varies depending on the transversal location of the measurement electrodes and on the anatomy. An increase in skin conductivity favourably affects the selectivity of normal double differential filters as does an increase in subcutaneous layer thickness. An increase in IED decreases the selectivity of all the analysed filters.

Speedup computation of HD-sEMG signals using a motor unit-specific electrical source model.

PubMed

Carriou, Vincent; Boudaoud, Sofiane; Laforet, Jeremy

2018-01-23

Nowadays, bio-reliable modeling of muscle contraction is becoming more accurate and complex. This increasing complexity induces a significant increase in computation time which prevents the possibility of using this model in certain applications and studies. Accordingly, the aim of this work is to significantly reduce the computation time of high-density surface electromyogram (HD-sEMG) generation. This will be done through a new model of motor unit (MU)-specific electrical source based on the fibers composing the MU. In order to assess the efficiency of this approach, we computed the normalized root mean square error (NRMSE) between several simulations on single generated MU action potential (MUAP) using the usual fiber electrical sources and the MU-specific electrical source. This NRMSE was computed for five different simulation sets wherein hundreds of MUAPs are generated and summed into HD-sEMG signals. The obtained results display less than 2% error on the generated signals compared to the same signals generated with fiber electrical sources. Moreover, the computation time of the HD-sEMG signal generation model is reduced to about 90% compared to the fiber electrical source model. Using this model with MU electrical sources, we can simulate HD-sEMG signals of a physiological muscle (hundreds of MU) in less than an hour on a classical workstation. Graphical Abstract Overview of the simulation of HD-sEMG signals using the fiber scale and the MU scale. Upscaling the electrical source to the MU scale reduces the computation time by 90% inducing only small deviation of the same simulated HD-sEMG signals.

A mechatronics platform to study prosthetic hand control using EMG signals.

PubMed

Geethanjali, P

2016-09-01

In this paper, a low-cost mechatronics platform for the design and development of robotic hands as well as a surface electromyogram (EMG) pattern recognition system is proposed. This paper also explores various EMG classification techniques using a low-cost electronics system in prosthetic hand applications. The proposed platform involves the development of a four channel EMG signal acquisition system; pattern recognition of acquired EMG signals; and development of a digital controller for a robotic hand. Four-channel surface EMG signals, acquired from ten healthy subjects for six different movements of the hand, were used to analyse pattern recognition in prosthetic hand control. Various time domain features were extracted and grouped into five ensembles to compare the influence of features in feature-selective classifiers (SLR) with widely considered non-feature-selective classifiers, such as neural networks (NN), linear discriminant analysis (LDA) and support vector machines (SVM) applied with different kernels. The results divulged that the average classification accuracy of the SVM, with a linear kernel function, outperforms other classifiers with feature ensembles, Hudgin's feature set and auto regression (AR) coefficients. However, the slight improvement in classification accuracy of SVM incurs more processing time and memory space in the low-level controller. The Kruskal-Wallis (KW) test also shows that there is no significant difference in the classification performance of SLR with Hudgin's feature set to that of SVM with Hudgin's features along with AR coefficients. In addition, the KW test shows that SLR was found to be better in respect to computation time and memory space, which is vital in a low-level controller. Similar to SVM, with a linear kernel function, other non-feature selective LDA and NN classifiers also show a slight improvement in performance using twice the features but with the drawback of increased memory space requirement and time

Functional mapping of the pelvic floor and sphincter muscles from high-density surface EMG recordings.

PubMed

Peng, Yun; He, Jinbao; Khavari, Rose; Boone, Timothy B; Zhang, Yingchun

2016-11-01

Knowledge of the innervation of pelvic floor and sphincter muscles is of great importance to understanding the pathophysiology of female pelvic floor dysfunctions. This report presents our high-density intravaginal and intrarectal electromyography (EMG) probes and a comprehensive innervation zone (IZ) imaging technique based on high-density EMG readings to characterize the IZ distribution. Both intravaginal and intrarectal probes are covered with a high-density surface electromyography electrode grid (8 × 8). Surface EMG signals were acquired in ten healthy women performing maximum voluntary contractions of their pelvic floor. EMG decomposition was performed to separate motor-unit action potentials (MUAPs) and then localize their IZs. High-density surface EMG signals were successfully acquired over the vaginal and rectal surfaces. The propagation patterns of muscle activity were clearly visualized for multiple muscle groups of the pelvic floor and anal sphincter. During each contraction, up to 218 and 456 repetitions of motor units were detected by the vaginal and rectal probes, respectively. MUAPs were separated with their IZs identified at various orientations and depths. The proposed probes are capable of providing a comprehensive mapping of IZs of the pelvic floor and sphincter muscles. They can be employed as diagnostic and preventative tools in clinical practices.

Design of microcontroller-based EMG and the analysis of EMG signals.

PubMed

Güler, Nihal Fatma; Hardalaç, Firat

2002-04-01

In this work, a microcontroller-based EMG designed and tested on 40 patients. When the patients are in rest, the fast Fourier transform (FFT) analysis was applied to EMG signals recorded from right leg peroneal region. The histograms are constructed from the results of the FFT analysis. The analysis results shows that the amplitude of fibrillation potential of the muscle fiber of 30 patients measured from peroneal region is low and the duration is short. This is the reason why the motor nerves degenerated and 10 patients were found to be healthy.

Assessment of the non-Gaussianity and non-linearity levels of simulated sEMG signals on stationary segments.

PubMed

Messaoudi, Noureddine; Bekka, Raïs El'hadi; Ravier, Philippe; Harba, Rachid

2017-02-01

The purpose of this paper was to evaluate the effects of the longitudinal single differential (LSD), the longitudinal double differential (LDD) and the normal double differential (NDD) spatial filters, the electrode shape, the inter-electrode distance (IED) on non-Gaussianity and non-linearity levels of simulated surface EMG (sEMG) signals when the maximum voluntary contraction (MVC) varied from 10% to 100% by a step of 10%. The effects of recruitment range thresholds (RR), the firing rate (FR) strategy and the peak firing rate (PFR) of motor units were also considered. A cylindrical multilayer model of the volume conductor and a model of motor unit (MU) recruitment and firing rate were used to simulate sEMG signals in a pool of 120 MUs for 5s. Firstly, the stationarity of sEMG signals was tested by the runs, the reverse arrangements (RA) and the modified reverse arrangements (MRA) tests. Then the non-Gaussianity was characterised with bicoherence and kurtosis, and non-linearity levels was evaluated with linearity test. The kurtosis analysis showed that the sEMG signals detected by the LSD filter were the most Gaussian and those detected by the NDD filter were the least Gaussian. In addition, the sEMG signals detected by the LSD filter were the most linear. For a given filter, the sEMG signals detected by using rectangular electrodes were more Gaussian and more linear than that detected with circular electrodes. Moreover, the sEMG signals are less non-Gaussian and more linear with reverse onion-skin firing rate strategy than those with onion-skin strategy. The levels of sEMG signal Gaussianity and linearity increased with the increase of the IED, RR and PFR. Copyright © 2016 Elsevier Ltd. All rights reserved.

Recurrence quantification analysis of electrically evoked surface EMG signal.

PubMed

Liu, Chunling; Wang, Xu

2005-01-01

Recurrence Plot is a quite useful tool used in time-series analysis, in particular for measuring unstable periodic orbits embedded in a chaotic dynamical system. This paper introduced the structures of the Recurrence Plot and the ways of the plot coming into being. Then the way of the quantification of the Recurrence Plot is defined. In this paper, one of the possible applications of Recurrence Quantification Analysis (RQA) strategy to the analysis of electrical stimulation evoked surface EMG. The result shows the percent determination is increased along with stimulation intensity.

A Simple Network to Remove Interference in Surface EMG Signal from Single Gene Affected Phenylketonuria Patients for Proper Diagnosis

NASA Astrophysics Data System (ADS)

Mohanty, Madhusmita; Basu, Mousumi; Pattanayak, Deba Narayan; Mohapatra, Sumant Kumar

2018-04-01

Recently Autosomal Recessive Single Gene (ARSG) diseases are highly effective to the children within the age of 5-10 years. One of the most ARSG disease is a Phenylketonuria (PKU). This single gene disease is associated with mutations in the gene that encodes the enzyme phenylalanine hydroxylase (PAH, Gene 612349). Through this mutation process, PAH of the gene affected patient can not properly manufacture PAH as a result the patients suffer from decreased muscle tone which shows abnormality in EMG signal. Here the extraction of the quality of the PKU affected EMG (PKU-EMG) signal is a keen interest, so it is highly necessary to remove the added ECG signal as well as the biological and instrumental noises. In the Present paper we proposed a method for detection and classification of the PKU affected EMG signal. Here Discrete Wavelet Transformation is implemented for extraction of the features of the PKU affected EMG signal. Adaptive Neuro-Fuzzy Inference System (ANFIS) network is used for the classification of the signal. Modified Particle Swarm Optimization (MPSO) and Modified Genetic Algorithm (MGA) are used to train the ANFIS network. Simulation result shows that the proposed method gives better performance as compared to existing approaches. Also it gives better accuracy of 98.02% for the detection of PKU-EMG signal. The advantages of the proposed model is to use MGA and MPSO to train the parameters of ANFIS network for classification of ECG and EMG signal of PKU affected patients. The proposed method obtained the high SNR (18.13 ± 0.36 dB), SNR (0.52 ± 1.62 dB), RE (0.02 ± 0.32), MSE (0.64 ± 2.01), CC (0.99 ± 0.02), RMSE (0.75 ± 0.35) and MFRE (0.01 ± 0.02), RMSE (0.75 ± 0.35) and MFRE (0.01 ± 0.02). From authors knowledge, this is the first time a composite method is used for diagnosis of PKU affected patients. The accuracy (98.02%), sensitivity (100%) and specificity (98.59%) helps for proper clinical treatment. It can help for readers

Analysis of EMG Signals in Aggressive and Normal Activities by Using Higher-Order Spectra

PubMed Central

Sezgin, Necmettin

2012-01-01

The analysis and classification of electromyography (EMG) signals are very important in order to detect some symptoms of diseases, prosthetic arm/leg control, and so on. In this study, an EMG signal was analyzed using bispectrum, which belongs to a family of higher-order spectra. An EMG signal is the electrical potential difference of muscle cells. The EMG signals used in the present study are aggressive or normal actions. The EMG dataset was obtained from the machine learning repository. First, the aggressive and normal EMG activities were analyzed using bispectrum and the quadratic phase coupling of each EMG episode was determined. Next, the features of the analyzed EMG signals were fed into learning machines to separate the aggressive and normal actions. The best classification result was 99.75%, which is sufficient to significantly classify the aggressive and normal actions. PMID:23193379

Examination of Poststroke Alteration in Motor Unit Firing Behavior Using High-Density Surface EMG Decomposition.

PubMed

Li, Xiaoyan; Holobar, Ales; Gazzoni, Marco; Merletti, Roberto; Rymer, William Zev; Zhou, Ping

2015-05-01

Recent advances in high-density surface electromyogram (EMG) decomposition have made it a feasible task to discriminate single motor unit activity from surface EMG interference patterns, thus providing a noninvasive approach for examination of motor unit control properties. In the current study, we applied high-density surface EMG recording and decomposition techniques to assess motor unit firing behavior alterations poststroke. Surface EMG signals were collected using a 64-channel 2-D electrode array from the paretic and contralateral first dorsal interosseous (FDI) muscles of nine hemiparetic stroke subjects at different isometric discrete contraction levels between 2 to 10 N with a 2 N increment step. Motor unit firing rates were extracted through decomposition of the high-density surface EMG signals and compared between paretic and contralateral muscles. Across the nine tested subjects, paretic FDI muscles showed decreased motor unit firing rates compared with contralateral muscles at different contraction levels. Regression analysis indicated a linear relation between the mean motor unit firing rate and the muscle contraction level for both paretic and contralateral muscles (p < 0.001), with the former demonstrating a lower increment rate (0.32 pulses per second (pps)/N) compared with the latter (0.67 pps/N). The coefficient of variation (averaged over the contraction levels) of the motor unit firing rates for the paretic muscles (0.21 ± 0.012) was significantly higher than for the contralateral muscles (0.17 ± 0.014) (p < 0.05). This study provides direct evidence of motor unit firing behavior alterations poststroke using surface EMG, which can be an important factor contributing to hemiparetic muscle weakness.

S-EMG signal compression based on domain transformation and spectral shape dynamic bit allocation

PubMed Central

2014-01-01

Background Surface electromyographic (S-EMG) signal processing has been emerging in the past few years due to its non-invasive assessment of muscle function and structure and because of the fast growing rate of digital technology which brings about new solutions and applications. Factors such as sampling rate, quantization word length, number of channels and experiment duration can lead to a potentially large volume of data. Efficient transmission and/or storage of S-EMG signals are actually a research issue. That is the aim of this work. Methods This paper presents an algorithm for the data compression of surface electromyographic (S-EMG) signals recorded during isometric contractions protocol and during dynamic experimental protocols such as the cycling activity. The proposed algorithm is based on discrete wavelet transform to proceed spectral decomposition and de-correlation, on a dynamic bit allocation procedure to code the wavelets transformed coefficients, and on an entropy coding to minimize the remaining redundancy and to pack all data. The bit allocation scheme is based on mathematical decreasing spectral shape models, which indicates a shorter digital word length to code high frequency wavelets transformed coefficients. Four bit allocation spectral shape methods were implemented and compared: decreasing exponential spectral shape, decreasing linear spectral shape, decreasing square-root spectral shape and rotated hyperbolic tangent spectral shape. Results The proposed method is demonstrated and evaluated for an isometric protocol and for a dynamic protocol using a real S-EMG signal data bank. Objective performance evaluations metrics are presented. In addition, comparisons with other encoders proposed in scientific literature are shown. Conclusions The decreasing bit allocation shape applied to the quantized wavelet coefficients combined with arithmetic coding results is an efficient procedure. The performance comparisons of the proposed S-EMG data

An artificial EMG generation model based on signal-dependent noise and related application to motion classification

PubMed Central

Hayashi, Hideaki; Nakamura, Go; Chin, Takaaki; Tsuji, Toshio

2017-01-01

This paper proposes an artificial electromyogram (EMG) signal generation model based on signal-dependent noise, which has been ignored in existing methods, by introducing the stochastic construction of the EMG signals. In the proposed model, an EMG signal variance value is first generated from a probability distribution with a shape determined by a commanded muscle force and signal-dependent noise. Artificial EMG signals are then generated from the associated Gaussian distribution with a zero mean and the generated variance. This facilitates representation of artificial EMG signals with signal-dependent noise superimposed according to the muscle activation levels. The frequency characteristics of the EMG signals are also simulated via a shaping filter with parameters determined by an autoregressive model. An estimation method to determine EMG variance distribution using rectified and smoothed EMG signals, thereby allowing model parameter estimation with a small number of samples, is also incorporated in the proposed model. Moreover, the prediction of variance distribution with strong muscle contraction from EMG signals with low muscle contraction and related artificial EMG generation are also described. The results of experiments conducted, in which the reproduction capability of the proposed model was evaluated through comparison with measured EMG signals in terms of amplitude, frequency content, and EMG distribution demonstrate that the proposed model can reproduce the features of measured EMG signals. Further, utilizing the generated EMG signals as training data for a neural network resulted in the classification of upper limb motion with a higher precision than by learning from only measured EMG signals. This indicates that the proposed model is also applicable to motion classification. PMID:28640883

Compression of high-density EMG signals for trapezius and gastrocnemius muscles.

PubMed

Itiki, Cinthia; Furuie, Sergio S; Merletti, Roberto

2014-03-10

New technologies for data transmission and multi-electrode arrays increased the demand for compressing high-density electromyography (HD EMG) signals. This article aims the compression of HD EMG signals recorded by two-dimensional electrode matrices at different muscle-contraction forces. It also shows methodological aspects of compressing HD EMG signals for non-pinnate (upper trapezius) and pinnate (medial gastrocnemius) muscles, using image compression techniques. HD EMG signals were placed in image rows, according to two distinct electrode orders: parallel and perpendicular to the muscle longitudinal axis. For the lossless case, the images obtained from single-differential signals as well as their differences in time were compressed. For the lossy algorithm, the images associated to the recorded monopolar or single-differential signals were compressed for different compression levels. Lossless compression provided up to 59.3% file-size reduction (FSR), with lower contraction forces associated to higher FSR. For lossy compression, a 90.8% reduction on the file size was attained, while keeping the signal-to-noise ratio (SNR) at 21.19 dB. For a similar FSR, higher contraction forces corresponded to higher SNR CONCLUSIONS: The computation of signal differences in time improves the performance of lossless compression while the selection of signals in the transversal order improves the lossy compression of HD EMG, for both pinnate and non-pinnate muscles.

Compression of high-density EMG signals for trapezius and gastrocnemius muscles

PubMed Central

2014-01-01

Background New technologies for data transmission and multi-electrode arrays increased the demand for compressing high-density electromyography (HD EMG) signals. This article aims the compression of HD EMG signals recorded by two-dimensional electrode matrices at different muscle-contraction forces. It also shows methodological aspects of compressing HD EMG signals for non-pinnate (upper trapezius) and pinnate (medial gastrocnemius) muscles, using image compression techniques. Methods HD EMG signals were placed in image rows, according to two distinct electrode orders: parallel and perpendicular to the muscle longitudinal axis. For the lossless case, the images obtained from single-differential signals as well as their differences in time were compressed. For the lossy algorithm, the images associated to the recorded monopolar or single-differential signals were compressed for different compression levels. Results Lossless compression provided up to 59.3% file-size reduction (FSR), with lower contraction forces associated to higher FSR. For lossy compression, a 90.8% reduction on the file size was attained, while keeping the signal-to-noise ratio (SNR) at 21.19 dB. For a similar FSR, higher contraction forces corresponded to higher SNR Conclusions The computation of signal differences in time improves the performance of lossless compression while the selection of signals in the transversal order improves the lossy compression of HD EMG, for both pinnate and non-pinnate muscles. PMID:24612604

Simultaneous, proportional, multi-axis prosthesis control using multichannel surface EMG.

PubMed

Yatsenko, Dimitri; McDonnall, Daniel; Guillory, K Shane

2007-01-01

Most upper limb prosthesis controllers only allow the individual selection and control of single joints of the limb. The main limiting factor for simultaneous multi-joint control is usually the availability of reliable independent control signals that can intuitively be used. In this paper, a novel method is presented for extraction of individual muscle source signals from surface EMG array recordings, based on EMG energy orthonormalization along principle movement vectors. In cases where independently-controllable muscles are present in residual limbs, this method can be used to provide simultaneous, multi-axis, proportional control of prosthetic systems. Initial results are presented for simultaneous control of wrist rotation, wrist flexion/extension, and grip open/close for two intact subjects under both isometric and non-isometric conditions and for one subject with transradial amputation.

Examination of Post-stroke Alteration in Motor Unit Firing Behavior Using High Density Surface EMG Decomposition

PubMed Central

Li, Xiaoyan; Holobar, Aleš; Gazzoni, Marco; Merletti, Roberto; Rymer, William Z.; Zhou, Ping

2014-01-01

Recent advances in high density surface electromyogram (EMG) decomposition have made it a feasible task to discriminate single motor unit activity from surface EMG interference patterns, thus providing a noninvasive approach for examination of motor unit control properties. In the current study we applied high density surface EMG recording and decomposition techniques to assess motor unit firing behavior alterations post-stroke. Surface EMG signals were collected using a 64-channel 2-dimensional electrode array from the paretic and contralateral first dorsal interosseous (FDI) muscles of nine hemiparetic stroke subjects at different isometric discrete contraction levels between 2 N to 10 N with a 2 N increment step. Motor unit firing rates were extracted through decomposition of the high density surface EMG signals, and compared between paretic and contralateral muscles. Across the nine tested subjects, paretic FDI muscles showed decreased motor unit firing rates compared with contralateral muscles at different contraction levels. Regression analysis indicated a linear relation between the mean motor unit firing rate and the muscle contraction level for both paretic and contralateral muscles (p < 0.001), with the former demonstrating a lower increment rate (0.32 pulses per second (pps)/N) compared with the latter (0.67 pps/N). The coefficient of variation (CoV, averaged over the contraction levels) of the motor unit firing rates for the paretic muscles (0.21 ± 0.012) was significantly higher than for the contralateral muscles (0.17 ± 0.014) (p < 0.05). This study provides direct evidence of motor unit firing behavior alterations post-stroke using surface EMG, which can be an important factor contributing to hemiparetic muscle weakness. PMID:25389239

A method for discrimination of noise and EMG signal regions recorded during rhythmic behaviors.

PubMed

Ying, Rex; Wall, Christine E

2016-12-08

Analyses of muscular activity during rhythmic behaviors provide critical data for biomechanical studies. Electrical potentials measured from muscles using electromyography (EMG) require discrimination of noise regions as the first step in analysis. An experienced analyst can accurately identify the onset and offset of EMG but this process takes hours to analyze a short (10-15s) record of rhythmic EMG bursts. Existing computational techniques reduce this time but have limitations. These include a universal threshold for delimiting noise regions (i.e., a single signal value for identifying the EMG signal onset and offset), pre-processing using wide time intervals that dampen sensitivity for EMG signal characteristics, poor performance when a low frequency component (e.g., DC offset) is present, and high computational complexity leading to lack of time efficiency. We present a new statistical method and MATLAB script (EMG-Extractor) that includes an adaptive algorithm to discriminate noise regions from EMG that avoids these limitations and allows for multi-channel datasets to be processed. We evaluate the EMG-Extractor with EMG data on mammalian jaw-adductor muscles during mastication, a rhythmic behavior typified by low amplitude onsets/offsets and complex signal pattern. The EMG-Extractor consistently and accurately distinguishes noise from EMG in a manner similar to that of an experienced analyst. It outputs the raw EMG signal region in a form ready for further analysis. Copyright © 2016 Elsevier Ltd. All rights reserved.

Power line interference attenuation in multi-channel sEMG signals: Algorithms and analysis.

PubMed

Soedirdjo, S D H; Ullah, K; Merletti, R

2015-08-01

Electromyogram (EMG) recordings are often corrupted by power line interference (PLI) even though the skin is prepared and well-designed instruments are used. This study focuses on the analysis of some of the recent and classical existing digital signal processing approaches have been used to attenuate, if not eliminate, the power line interference from EMG signals. A comparison of the signal to interference ratio (SIR) of the output signals is presented, for four methods: classical notch filter, spectral interpolation, adaptive noise canceller with phase locked loop (ANC-PLL) and adaptive filter, applied to simulated multichannel monopolar EMG signals with different SIR. The effect of each method on the shape of the EMG signals is also analyzed. The results show that ANC-PLL method gives the best output SIR and lowest shape distortion compared to the other methods. Classical notch filtering is the simplest method but some information might be lost as it removes both the interference and the EMG signals. Thus, it is obvious that notch filter has the lowest performance and it introduces distortion into the resulting signals.

Augmenting the decomposition of EMG signals using supervised feature extraction techniques.

PubMed

Parsaei, Hossein; Gangeh, Mehrdad J; Stashuk, Daniel W; Kamel, Mohamed S

2012-01-01

Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its constituent motor unit potential trains (MUPTs). In this work, the possibility of improving the decomposing results using two supervised feature extraction methods, i.e., Fisher discriminant analysis (FDA) and supervised principal component analysis (SPCA), is explored. Using the MUP labels provided by a decomposition-based quantitative EMG system as a training data for FDA and SPCA, the MUPs are transformed into a new feature space such that the MUPs of a single MU become as close as possible to each other while those created by different MUs become as far as possible. The MUPs are then reclassified using a certainty-based classification algorithm. Evaluation results using 10 simulated EMG signals comprised of 3-11 MUPTs demonstrate that FDA and SPCA on average improve the decomposition accuracy by 6%. The improvement for the most difficult-to-decompose signal is about 12%, which shows the proposed approach is most beneficial in the decomposition of more complex signals.

Control of an optimal finger exoskeleton based on continuous joint angle estimation from EMG signals.

PubMed

Ngeo, Jimson; Tamei, Tomoya; Shibata, Tomohiro; Orlando, M F Felix; Behera, Laxmidhar; Saxena, Anupam; Dutta, Ashish

2013-01-01

Patients suffering from loss of hand functions caused by stroke and other spinal cord injuries have driven a surge in the development of wearable assistive devices in recent years. In this paper, we present a system made up of a low-profile, optimally designed finger exoskeleton continuously controlled by a user's surface electromyographic (sEMG) signals. The mechanical design is based on an optimal four-bar linkage that can model the finger's irregular trajectory due to the finger's varying lengths and changing instantaneous center. The desired joint angle positions are given by the predictive output of an artificial neural network with an EMG-to-Muscle Activation model that parameterizes electromechanical delay (EMD). After confirming good prediction accuracy of multiple finger joint angles we evaluated an index finger exoskeleton by obtaining a subject's EMG signals from the left forearm and using the signal to actuate a finger on the right hand with the exoskeleton. Our results show that our sEMG-based control strategy worked well in controlling the exoskeleton, obtaining the intended positions of the device, and that the subject felt the appropriate motion support from the device.

Analysis of sEMG signals using discrete wavelet transform for muscle fatigue detection

NASA Astrophysics Data System (ADS)

Flórez-Prias, L. A.; Contreras-Ortiz, S. H.

2017-11-01

The purpose of the present article is to characterize sEMG signals to determine muscular fatigue levels. To do this, the signal is decomposed using the discrete wavelet transform, which offers noise filtering features, simplicity and efficiency. sEMG signals on the forearm were acquired and analyzed during the execution of cyclic muscular contractions in the presence and absence of fatigue. When the muscle fatigues, the sEMG signal shows a more erratic behavior of the signal as more energy is required to maintain the effort levels.

Effectiveness of the Wavelet Transform on the Surface EMG to Understand the Muscle Fatigue During Walk

NASA Astrophysics Data System (ADS)

Hussain, M. S.; Mamun, Md.

2012-01-01

Muscle fatigue is the decline in ability of a muscle to create force. Electromyography (EMG) is a medical technique for measuring muscle response to nervous stimulation. During a sustained muscle contraction, the power spectrum of the EMG shifts towards lower frequencies. These effects are due to muscle fatigue. Muscle fatigue is often a result of unhealthy work practice. In this research, the effectiveness of the wavelet transform applied to the surface EMG (SEMG) signal as a means of understanding muscle fatigue during walk is presented. Power spectrum and bispectrum analysis on the EMG signal getting from right rectus femoris muscle is executed utilizing various wavelet functions (WFs). It is possible to recognize muscle fatigue appreciably with the proper choice of the WF. The outcome proves that the most momentous changes in the EMG power spectrum are symbolized by WF Daubechies45. Moreover, this research has compared bispectrum properties to the other WFs. To determine muscle fatigue during gait, Daubechies45 is used in this research to analyze the SEMG signal.

Detection of Multiple Innervation Zones from Multi-Channel Surface EMG Recordings with Low Signal-to-Noise Ratio Using Graph-Cut Segmentation.

PubMed

Marateb, Hamid Reza; Farahi, Morteza; Rojas, Monica; Mañanas, Miguel Angel; Farina, Dario

2016-01-01

Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fully-automatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was performed

Detection of Multiple Innervation Zones from Multi-Channel Surface EMG Recordings with Low Signal-to-Noise Ratio Using Graph-Cut Segmentation

PubMed Central

Farahi, Morteza; Rojas, Monica; Mañanas, Miguel Angel; Farina, Dario

2016-01-01

Knowledge of the location of muscle Innervation Zones (IZs) is important in many applications, e.g. for minimizing the quantity of injected botulinum toxin for the treatment of spasticity or for deciding on the type of episiotomy during child delivery. Surface EMG (sEMG) can be noninvasively recorded to assess physiological and morphological characteristics of contracting muscles. However, it is not often possible to record signals of high quality. Moreover, muscles could have multiple IZs, which should all be identified. We designed a fully-automatic algorithm based on the enhanced image Graph-Cut segmentation and morphological image processing methods to identify up to five IZs in 60-ms intervals of very-low to moderate quality sEMG signal detected with multi-channel electrodes (20 bipolar channels with Inter Electrode Distance (IED) of 5 mm). An anisotropic multilayered cylinder model was used to simulate 750 sEMG signals with signal-to-noise ratio ranging from -5 to 15 dB (using Gaussian noise) and in each 60-ms signal frame, 1 to 5 IZs were included. The micro- and macro- averaged performance indices were then reported for the proposed IZ detection algorithm. In the micro-averaging procedure, the number of True Positives, False Positives and False Negatives in each frame were summed up to generate cumulative measures. In the macro-averaging, on the other hand, precision and recall were calculated for each frame and their averages are used to determine F1-score. Overall, the micro (macro)-averaged sensitivity, precision and F1-score of the algorithm for IZ channel identification were 82.7% (87.5%), 92.9% (94.0%) and 87.5% (90.6%), respectively. For the correctly identified IZ locations, the average bias error was of 0.02±0.10 IED ratio. Also, the average absolute conduction velocity estimation error was 0.41±0.40 m/s for such frames. The sensitivity analysis including increasing IED and reducing interpolation coefficient for time samples was performed

Adaptive EMG noise reduction in ECG signals using noise level approximation

NASA Astrophysics Data System (ADS)

Marouf, Mohamed; Saranovac, Lazar

2017-12-01

In this paper the usage of noise level approximation for adaptive Electromyogram (EMG) noise reduction in the Electrocardiogram (ECG) signals is introduced. To achieve the adequate adaptiveness, a translation-invariant noise level approximation is employed. The approximation is done in the form of a guiding signal extracted as an estimation of the signal quality vs. EMG noise. The noise reduction framework is based on a bank of low pass filters. So, the adaptive noise reduction is achieved by selecting the appropriate filter with respect to the guiding signal aiming to obtain the best trade-off between the signal distortion caused by filtering and the signal readability. For the evaluation purposes; both real EMG and artificial noises are used. The tested ECG signals are from the MIT-BIH Arrhythmia Database Directory, while both real and artificial records of EMG noise are added and used in the evaluation process. Firstly, comparison with state of the art methods is conducted to verify the performance of the proposed approach in terms of noise cancellation while preserving the QRS complex waves. Additionally, the signal to noise ratio improvement after the adaptive noise reduction is computed and presented for the proposed method. Finally, the impact of adaptive noise reduction method on QRS complexes detection was studied. The tested signals are delineated using a state of the art method, and the QRS detection improvement for different SNR is presented.

Preliminary Study on Continuous Recognition of Elbow Flexion/Extension Using sEMG Signals for Bilateral Rehabilitation

PubMed Central

Song, Zhibin; Zhang, Songyuan

2016-01-01

Surface electromyography (sEMG) signals are closely related to the activation of human muscles and the motion of the human body, which can be used to estimate the dynamics of human limbs in the rehabilitation field. They also have the potential to be used in the application of bilateral rehabilitation, where hemiplegic patients can train their affected limbs following the motion of unaffected limbs via some rehabilitation devices. Traditional methods to process the sEMG focused on motion pattern recognition, namely, discrete patterns, which are not satisfactory for use in bilateral rehabilitation. In order to overcome this problem, in this paper, we built a relationship between sEMG signals and human motion in elbow flexion and extension on the sagittal plane. During the conducted experiments, four participants were required to perform elbow flexion and extension on the sagittal plane smoothly with only an inertia sensor in their hands, where forearm dynamics were not considered. In these circumstances, sEMG signals were weak compared to those with heavy loads or high acceleration. The contrastive experimental results show that continuous motion can also be obtained within an acceptable precision range. PMID:27775573

Preliminary Study on Continuous Recognition of Elbow Flexion/Extension Using sEMG Signals for Bilateral Rehabilitation.

PubMed

Song, Zhibin; Zhang, Songyuan

2016-10-19

Surface electromyography (sEMG) signals are closely related to the activation of human muscles and the motion of the human body, which can be used to estimate the dynamics of human limbs in the rehabilitation field. They also have the potential to be used in the application of bilateral rehabilitation, where hemiplegic patients can train their affected limbs following the motion of unaffected limbs via some rehabilitation devices. Traditional methods to process the sEMG focused on motion pattern recognition, namely, discrete patterns, which are not satisfactory for use in bilateral rehabilitation. In order to overcome this problem, in this paper, we built a relationship between sEMG signals and human motion in elbow flexion and extension on the sagittal plane. During the conducted experiments, four participants were required to perform elbow flexion and extension on the sagittal plane smoothly with only an inertia sensor in their hands, where forearm dynamics were not considered. In these circumstances, sEMG signals were weak compared to those with heavy loads or high acceleration. The contrastive experimental results show that continuous motion can also be obtained within an acceptable precision range.

Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis: a review.

PubMed

Naik, Ganesh R; Arjunan, Sridhar; Kumar, Dinesh

2011-06-01

The surface electromyography (sEMG) signal separation and decphompositions has always been an interesting research topic in the field of rehabilitation and medical research. Subtle myoelectric control is an advanced technique concerned with the detection, processing, classification, and application of myoelectric signals to control human-assisting robots or rehabilitation devices. This paper reviews recent research and development in independent component analysis and Fractal dimensional analysis for sEMG pattern recognition, and presents state-of-the-art achievements in terms of their type, structure, and potential application. Directions for future research are also briefly outlined.

Detection of driving fatigue by using noncontact EMG and ECG signals measurement system.

PubMed

Fu, Rongrong; Wang, Hong

2014-05-01

Driver fatigue can be detected by constructing a discriminant mode using some features obtained from physiological signals. There exist two major challenges of this kind of methods. One is how to collect physiological signals from subjects while they are driving without any interruption. The other is to find features of physiological signals that are of corresponding change with the loss of attention caused by driver fatigue. Driving fatigue is detected based on the study of surface electromyography (EMG) and electrocardiograph (ECG) during the driving period. The noncontact data acquisition system was used to collect physiological signals from the biceps femoris of each subject to tackle the first challenge. Fast independent component analysis (FastICA) and digital filter were utilized to process the original signals. Based on the statistical analysis results given by Kolmogorov-Smirnov Z test, the peak factor of EMG (p < 0.001) and the maximum of the cross-relation curve of EMG and ECG (p < 0.001) were selected as the combined characteristic to detect fatigue of drivers. The discriminant criterion of fatigue was obtained from the training samples by using Mahalanobis distance, and then the average classification accuracy was given by 10-fold cross-validation. The results showed that the method proposed in this paper can give well performance in distinguishing the normal state and fatigue state. The noncontact, onboard vehicle drivers' fatigue detection system was developed to reduce fatigue-related risks.

Simultaneous and Continuous Estimation of Shoulder and Elbow Kinematics from Surface EMG Signals

PubMed Central

Zhang, Qin; Liu, Runfeng; Chen, Wenbin; Xiong, Caihua

2017-01-01

In this paper, we present a simultaneous and continuous kinematics estimation method for multiple DoFs across shoulder and elbow joint. Although simultaneous and continuous kinematics estimation from surface electromyography (EMG) is a feasible way to achieve natural and intuitive human-machine interaction, few works investigated multi-DoF estimation across the significant joints of upper limb, shoulder and elbow joints. This paper evaluates the feasibility to estimate 4-DoF kinematics at shoulder and elbow during coordinated arm movements. Considering the potential applications of this method in exoskeleton, prosthetics and other arm rehabilitation techniques, the estimation performance is presented with different muscle activity decomposition and learning strategies. Principle component analysis (PCA) and independent component analysis (ICA) are respectively employed for EMG mode decomposition with artificial neural network (ANN) for learning the electromechanical association. Four joint angles across shoulder and elbow are simultaneously and continuously estimated from EMG in four coordinated arm movements. By using ICA (PCA) and single ANN, the average estimation accuracy 91.12% (90.23%) is obtained in 70-s intra-cross validation and 87.00% (86.30%) is obtained in 2-min inter-cross validation. This result suggests it is feasible and effective to use ICA (PCA) with single ANN for multi-joint kinematics estimation in variant application conditions. PMID:28611573

Emg Amplitude Estimators Based on Probability Distribution for Muscle-Computer Interface

NASA Astrophysics Data System (ADS)

Phinyomark, Angkoon; Quaine, Franck; Laurillau, Yann; Thongpanja, Sirinee; Limsakul, Chusak; Phukpattaranont, Pornchai

To develop an advanced muscle-computer interface (MCI) based on surface electromyography (EMG) signal, the amplitude estimations of muscle activities, i.e., root mean square (RMS) and mean absolute value (MAV) are widely used as a convenient and accurate input for a recognition system. Their classification performance is comparable to advanced and high computational time-scale methods, i.e., the wavelet transform. However, the signal-to-noise-ratio (SNR) performance of RMS and MAV depends on a probability density function (PDF) of EMG signals, i.e., Gaussian or Laplacian. The PDF of upper-limb motions associated with EMG signals is still not clear, especially for dynamic muscle contraction. In this paper, the EMG PDF is investigated based on surface EMG recorded during finger, hand, wrist and forearm motions. The results show that on average the experimental EMG PDF is closer to a Laplacian density, particularly for male subject and flexor muscle. For the amplitude estimation, MAV has a higher SNR, defined as the mean feature divided by its fluctuation, than RMS. Due to a same discrimination of RMS and MAV in feature space, MAV is recommended to be used as a suitable EMG amplitude estimator for EMG-based MCIs.

Bilinear modeling of EMG signals to extract user-independent features for multiuser myoelectric interface.

PubMed

Matsubara, Takamitsu; Morimoto, Jun

2013-08-01

In this study, we propose a multiuser myoelectric interface that can easily adapt to novel users. When a user performs different motions (e.g., grasping and pinching), different electromyography (EMG) signals are measured. When different users perform the same motion (e.g., grasping), different EMG signals are also measured. Therefore, designing a myoelectric interface that can be used by multiple users to perform multiple motions is difficult. To cope with this problem, we propose for EMG signals a bilinear model that is composed of two linear factors: 1) user dependent and 2) motion dependent. By decomposing the EMG signals into these two factors, the extracted motion-dependent factors can be used as user-independent features. We can construct a motion classifier on the extracted feature space to develop the multiuser interface. For novel users, the proposed adaptation method estimates the user-dependent factor through only a few interactions. The bilinear EMG model with the estimated user-dependent factor can extract the user-independent features from the novel user data. We applied our proposed method to a recognition task of five hand gestures for robotic hand control using four-channel EMG signals measured from subject forearms. Our method resulted in 73% accuracy, which was statistically significantly different from the accuracy of standard nonmultiuser interfaces, as the result of a two-sample t -test at a significance level of 1%.

Evaluation of jaw and neck muscle activities while chewing using EMG-EMG transfer function and EMG-EMG coherence function analyses in healthy subjects.

PubMed

Ishii, Tomohiro; Narita, Noriyuki; Endo, Hiroshi

2016-06-01

This study aims to quantitatively clarify the physiological features in rhythmically coordinated jaw and neck muscle EMG activities while chewing gum using EMG-EMG transfer function and EMG-EMG coherence function analyses in 20 healthy subjects. The chewing side masseter muscle EMG signal was used as the reference signal, while the other jaw (non-chewing side masseter muscle, bilateral anterior temporal muscles, and bilateral anterior digastric muscles) and neck muscle (bilateral sternocleidomastoid muscles) EMG signals were used as the examined signals in EMG-EMG transfer function and EMG-EMG coherence function analyses. Chewing-related jaw and neck muscle activities were aggregated in the first peak of the power spectrum in rhythmic chewing. The gain in the peak frequency represented the power relationships between jaw and neck muscle activities during rhythmic chewing. The phase in the peak frequency represented the temporal relationships between the jaw and neck muscle activities, while the non-chewing side neck muscle presented a broad range of distributions across jaw closing and opening phases. Coherence in the peak frequency represented the synergistic features in bilateral jaw closing muscles and chewing side neck muscle activities. The coherence and phase in non-chewing side neck muscle activities exhibited a significant negative correlation. From above, the bilateral coordination between the jaw and neck muscle activities is estimated while chewing when the non-chewing side neck muscle is synchronously activated with the jaw closing muscles, while the unilateral coordination is estimated when the non-chewing side neck muscle is irregularly activated in the jaw opening phase. Thus, the occurrence of bilateral or unilateral coordinated features in the jaw and neck muscle activities may correspond to the phase characteristics in the non-chewing side neck muscle activities during rhythmical chewing. Considering these novel findings in healthy subjects, EMG-EMG

Two-dimensional compression of surface electromyographic signals using column-correlation sorting and image encoders.

PubMed

Costa, Marcus V C; Carvalho, Joao L A; Berger, Pedro A; Zaghetto, Alexandre; da Rocha, Adson F; Nascimento, Francisco A O

2009-01-01

We present a new preprocessing technique for two-dimensional compression of surface electromyographic (S-EMG) signals, based on correlation sorting. We show that the JPEG2000 coding system (originally designed for compression of still images) and the H.264/AVC encoder (video compression algorithm operating in intraframe mode) can be used for compression of S-EMG signals. We compare the performance of these two off-the-shelf image compression algorithms for S-EMG compression, with and without the proposed preprocessing step. Compression of both isotonic and isometric contraction S-EMG signals is evaluated. The proposed methods were compared with other S-EMG compression algorithms from the literature.

Surface EMG crosstalk during phasic involuntary muscle activation in the nociceptive withdrawal reflex.

PubMed

Frahm, Ken S; Jensen, Michael B; Farina, Dario; Andersen, Ole K

2012-08-01

The human nociceptive withdrawal reflex is typically assessed using surface electromyography (sEMG). Based on sEMG, the reflex receptive field (RRF) can be mapped. However, EMG crosstalk can cause erroneous results in the RRF determination. Single differential (SD) vs. double differential (DD) surface EMG were evaluated. Different electrode areas and inter-electrode-distances (IED) were evaluated. The reflexes were elicited by electrical stimulation of the sole of the foot. EMG was obtained from both tibialis anterior (TA) and soleus (SOL) using both surface and intramuscular EMG (iEMG). The amount of crosstalk was significantly higher in SD recordings than in DD recordings (P < 0.05). Crosstalk increased when electrode measuring area increased (P < 0.05) and when IED increased (P < 0.05). Reflex detection sensitivity decreases with increasing measuring area and increasing IED. These results stress that for determination of RRF and similar tasks, DD recordings should be applied. Copyright © 2012 Wiley Periodicals, Inc.

Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders.

PubMed

Subasi, Abdulhamit

2013-06-01

Support vector machine (SVM) is an extensively used machine learning method with many biomedical signal classification applications. In this study, a novel PSO-SVM model has been proposed that hybridized the particle swarm optimization (PSO) and SVM to improve the EMG signal classification accuracy. This optimization mechanism involves kernel parameter setting in the SVM training procedure, which significantly influences the classification accuracy. The experiments were conducted on the basis of EMG signal to classify into normal, neurogenic or myopathic. In the proposed method the EMG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT) and a set of statistical features were extracted from these sub-bands to represent the distribution of wavelet coefficients. The obtained results obviously validate the superiority of the SVM method compared to conventional machine learning methods, and suggest that further significant enhancements in terms of classification accuracy can be achieved by the proposed PSO-SVM classification system. The PSO-SVM yielded an overall accuracy of 97.41% on 1200 EMG signals selected from 27 subject records against 96.75%, 95.17% and 94.08% for the SVM, the k-NN and the RBF classifiers, respectively. PSO-SVM is developed as an efficient tool so that various SVMs can be used conveniently as the core of PSO-SVM for diagnosis of neuromuscular disorders. Copyright © 2013 Elsevier Ltd. All rights reserved.

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions

PubMed Central

Nazmi, Nurhazimah; Abdul Rahman, Mohd Azizi; Yamamoto, Shin-Ichiroh; Ahmad, Siti Anom; Zamzuri, Hairi; Mazlan, Saiful Amri

2016-01-01

In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above. PMID:27548165

Force Control Is Related to Low-Frequency Oscillations in Force and Surface EMG

PubMed Central

Moon, Hwasil; Kim, Changki; Kwon, Minhyuk; Chen, Yen Ting; Onushko, Tanya; Lodha, Neha; Christou, Evangelos A.

2014-01-01

Force variability during constant force tasks is directly related to oscillations below 0.5 Hz in force. However, it is unknown whether such oscillations exist in muscle activity. The purpose of this paper, therefore, was to determine whether oscillations below 0.5 Hz in force are evident in the activation of muscle. Fourteen young adults (21.07±2.76 years, 7 women) performed constant isometric force tasks at 5% and 30% MVC by abducting the left index finger. We recorded the force output from the index finger and surface EMG from the first dorsal interosseous (FDI) muscle and quantified the following outcomes: 1) variability of force using the SD of force; 2) power spectrum of force below 2 Hz; 3) EMG bursts; 4) power spectrum of EMG bursts below 2 Hz; and 5) power spectrum of the interference EMG from 10–300 Hz. The SD of force increased significantly from 5 to 30% MVC and this increase was significantly related to the increase in force oscillations below 0.5 Hz (R 2 = 0.82). For both force levels, the power spectrum for force and EMG burst was similar and contained most of the power from 0–0.5 Hz. Force and EMG burst oscillations below 0.5 Hz were highly coherent (coherence = 0.68). The increase in force oscillations below 0.5 Hz from 5 to 30% MVC was related to an increase in EMG burst oscillations below 0.5 Hz (R 2 = 0.51). Finally, there was a strong association between the increase in EMG burst oscillations below 0.5 Hz and the interference EMG from 35–60 Hz (R 2 = 0.95). In conclusion, this finding demonstrates that bursting of the EMG signal contains low-frequency oscillations below 0.5 Hz, which are associated with oscillations in force below 0.5 Hz. PMID:25372038

Improving EMG based classification of basic hand movements using EMD.

PubMed

Sapsanis, Christos; Georgoulas, George; Tzes, Anthony; Lymberopoulos, Dimitrios

2013-01-01

This paper presents a pattern recognition approach for the identification of basic hand movements using surface electromyographic (EMG) data. The EMG signal is decomposed using Empirical Mode Decomposition (EMD) into Intrinsic Mode Functions (IMFs) and subsequently a feature extraction stage takes place. Various combinations of feature subsets are tested using a simple linear classifier for the detection task. Our results suggest that the use of EMD can increase the discrimination ability of the conventional feature sets extracted from the raw EMG signal.

Robust myoelectric signal detection based on stochastic resonance using multiple-surface-electrode array made of carbon nanotube composite paper

NASA Astrophysics Data System (ADS)

Shirata, Kento; Inden, Yuki; Kasai, Seiya; Oya, Takahide; Hagiwara, Yosuke; Kaeriyama, Shunichi; Nakamura, Hideyuki

2016-04-01

We investigated the robust detection of surface electromyogram (EMG) signals based on the stochastic resonance (SR) phenomenon, in which the response to weak signals is optimized by adding noise, combined with multiple surface electrodes. Flexible carbon nanotube composite paper (CNT-cp) was applied to the surface electrode, which showed good performance that is comparable to that of conventional Ag/AgCl electrodes. The SR-based EMG signal system integrating an 8-Schmitt-trigger network and the multiple-CNT-cp-electrode array successfully detected weak EMG signals even when the subject’s body is in the motion, which was difficult to achieve using the conventional technique. The feasibility of the SR-based EMG detection technique was confirmed by demonstrating its applicability to robot hand control.

EMG-force relationship during static contraction: effects on sensor placement locations on biceps brachii muscle.

PubMed

Ahamed, Nizam Uddin; Sundaraj, Kenneth; Alqahtani, Mahdi; Altwijri, Omar; Ali, Md Asraf; Islam, Md Anamul

2014-01-01

The relationship between surface electromyography (EMG) and force have been the subject of ongoing investigations and remain a subject of controversy. Even under static conditions, the relationships at different sensor placement locations in the biceps brachii (BB) muscle are complex. The aim of this study was to compare the activity and relationship between surface EMG and static force from the BB muscle in terms of three sensor placement locations. Twenty-one right hand dominant male subjects (age 25.3±1.2 years) participated in the study. Surface EMG signals were detected from the subject's right BB muscle. The muscle activation during force was determined as the root mean square (RMS) electromyographic signal normalized to the peak RMS EMG signal of isometric contraction for 10 s. The statistical analysis included linear regression to examine the relationship between EMG amplitude and force of contraction [40-100% of maximal voluntary contraction (MVC)], repeated measures ANOVA to assess differences among the sensor placement locations, and coefficient of variation (CoV) for muscle activity variation. The results demonstrated that when the sensor was placed on the muscle belly, the linear slope coefficient was significantly greater for EMG versus force testing (r2=0.62, P<0.05) than when placed on the lower part (r2=0.31, P>0.05) and upper part of the muscle belly (r2=0.29, P<0.05). In addition, the EMG signal activity on the muscle belly had less variability than the upper and lower parts (8.55% vs. 15.12% and 12.86%, respectively). These findings indicate the importance of applying the surface EMG sensor at the appropriate locations that follow muscle fiber orientation of the BB muscle during static contraction. As a result, EMG signals of three different placements may help to understand the difference in the amplitude of the signals due to placement.

Subauditory Speech Recognition based on EMG/EPG Signals

NASA Technical Reports Server (NTRS)

Jorgensen, Charles; Lee, Diana Dee; Agabon, Shane; Lau, Sonie (Technical Monitor)

2003-01-01

Sub-vocal electromyogram/electro palatogram (EMG/EPG) signal classification is demonstrated as a method for silent speech recognition. Recorded electrode signals from the larynx and sublingual areas below the jaw are noise filtered and transformed into features using complex dual quad tree wavelet transforms. Feature sets for six sub-vocally pronounced words are trained using a trust region scaled conjugate gradient neural network. Real time signals for previously unseen patterns are classified into categories suitable for primitive control of graphic objects. Feature construction, recognition accuracy and an approach for extension of the technique to a variety of real world application areas are presented.

EMG Versus Torque Control of Human-Machine Systems: Equalizing Control Signal Variability Does not Equalize Error or Uncertainty.

PubMed

Johnson, Reva E; Kording, Konrad P; Hargrove, Levi J; Sensinger, Jonathon W

2017-06-01

In this paper we asked the question: if we artificially raise the variability of torque control signals to match that of EMG, do subjects make similar errors and have similar uncertainty about their movements? We answered this question using two experiments in which subjects used three different control signals: torque, torque+noise, and EMG. First, we measured error on a simple target-hitting task in which subjects received visual feedback only at the end of their movements. We found that even when the signal-to-noise ratio was equal across EMG and torque+noise control signals, EMG resulted in larger errors. Second, we quantified uncertainty by measuring the just-noticeable difference of a visual perturbation. We found that for equal errors, EMG resulted in higher movement uncertainty than both torque and torque+noise. The differences suggest that performance and confidence are influenced by more than just the noisiness of the control signal, and suggest that other factors, such as the user's ability to incorporate feedback and develop accurate internal models, also have significant impacts on the performance and confidence of a person's actions. We theorize that users have difficulty distinguishing between random and systematic errors for EMG control, and future work should examine in more detail the types of errors made with EMG control.

Three-Dimensional Innervation Zone Imaging from Multi-Channel Surface EMG Recordings.

PubMed

Liu, Yang; Ning, Yong; Li, Sheng; Zhou, Ping; Rymer, William Z; Zhang, Yingchun

2015-09-01

There is an unmet need to accurately identify the locations of innervation zones (IZs) of spastic muscles, so as to guide botulinum toxin (BTX) injections for the best clinical outcome. A novel 3D IZ imaging (3DIZI) approach was developed by combining the bioelectrical source imaging and surface electromyogram (EMG) decomposition methods to image the 3D distribution of IZs in the target muscles. Surface IZ locations of motor units (MUs), identified from the bipolar map of their MU action potentials (MUAPs) were employed as a prior knowledge in the 3DIZI approach to improve its imaging accuracy. The performance of the 3DIZI approach was first optimized and evaluated via a series of designed computer simulations, and then validated with the intramuscular EMG data, together with simultaneously recorded 128-channel surface EMG data from the biceps of two subjects. Both simulation and experimental validation results demonstrate the high performance of the 3DIZI approach in accurately reconstructing the distributions of IZs and the dynamic propagation of internal muscle activities in the biceps from high-density surface EMG recordings.

THREE-DIMENSIONAL INNERVATION ZONE IMAGING FROM MULTI-CHANNEL SURFACE EMG RECORDINGS

PubMed Central

LIU, YANG; NING, YONG; LI, SHENG; ZHOU, PING; RYMER, WILLIAM Z.; ZHANG, YINGCHUN

2017-01-01

There is an unmet need to accurately identify the locations of innervation zones (IZs) of spastic muscles, so as to guide botulinum toxin (BTX) injections for the best clinical outcome. A novel 3-dimensional IZ imaging (3DIZI) approach was developed by combining the bioelectrical source imaging and surface electromyogram (EMG) decomposition methods to image the 3D distribution of IZs in the target muscles. Surface IZ locations of motor units (MUs), identified from the bipolar map of their motor unit action potentials (MUAPs) were employed as a prior knowledge in the 3DIZI approach to improve its imaging accuracy. The performance of the 3DIZI approach was first optimized and evaluated via a series of designed computer simulations, and then validated with the intramuscular EMG data, together with simultaneously recorded 128-channel surface EMG data from the biceps of two subjects. Both simulation and experimental validation results demonstrate the high performance of the 3DIZI approach in accurately reconstructing the distributions of IZs and the dynamic propagation of internal muscle activities in the biceps from high-density surface EMG recordings. PMID:26160432

Estimation of continuous multi-DOF finger joint kinematics from surface EMG using a multi-output Gaussian Process.

PubMed

Ngeo, Jimson; Tamei, Tomoya; Shibata, Tomohiro

2014-01-01

Surface electromyographic (EMG) signals have often been used in estimating upper and lower limb dynamics and kinematics for the purpose of controlling robotic devices such as robot prosthesis and finger exoskeletons. However, in estimating multiple and a high number of degrees-of-freedom (DOF) kinematics from EMG, output DOFs are usually estimated independently. In this study, we estimate finger joint kinematics from EMG signals using a multi-output convolved Gaussian Process (Multi-output Full GP) that considers dependencies between outputs. We show that estimation of finger joints from muscle activation inputs can be improved by using a regression model that considers inherent coupling or correlation within the hand and finger joints. We also provide a comparison of estimation performance between different regression methods, such as Artificial Neural Networks (ANN) which is used by many of the related studies. We show that using a multi-output GP gives improved estimation compared to multi-output ANN and even dedicated or independent regression models.

Analysis of linear electrode array EMG for assessment of hemiparetic biceps brachii muscles.

PubMed

Yao, Bo; Zhang, Xu; Li, Sheng; Li, Xiaoyan; Chen, Xiang; Klein, Cliff S; Zhou, Ping

2015-01-01

This study presents a frequency analysis of surface electromyogram (EMG) signals acquired by a linear electrode array from the biceps brachii muscles bilaterally in 14 hemiparetic stroke subjects. For different levels of isometric contraction ranging from 10 to 80% of the maximum voluntary contraction (MVC), the power spectra of 19 bipolar surface EMG channels arranged proximally to distally along the muscle fibers were examined in both paretic and contralateral muscles. It was found that across all stroke subjects, the median frequency (MF) and the mean power frequency (MPF), averaged from different surface EMG channels, were significantly smaller in the paretic muscle compared to the contralateral muscle at each of the matched percent MVC contractions. The muscle fiber conduction velocity (MFCV) was significantly slower in the paretic muscle than in the contralateral muscle. No significant correlation between the averaged MF, MPF, or MFCV vs. torque was found in both paretic and contralateral muscles. However, there was a significant positive correlation between the global MFCV and MF. Examination of individual EMG channels showed that electrodes closest to the estimated muscle innervation zones produced surface EMG signals with significantly higher MF and MPF than more proximal or distal locations in both paretic and contralateral sides. These findings suggest complex central and peripheral neuromuscular alterations (such as selective loss of large motor units, disordered control of motor units, increased motor unit synchronization, and atrophy of muscle fibers, etc.) which can collectively influence the surface EMG signals. The frequency difference with regard to the innervation zone also confirms the relevance of electrode position in surface EMG analysis.

Surface Electromyography Signal Processing and Classification Techniques

PubMed Central

Chowdhury, Rubana H.; Reaz, Mamun B. I.; Ali, Mohd Alauddin Bin Mohd; Bakar, Ashrif A. A.; Chellappan, Kalaivani; Chang, Tae. G.

2013-01-01

Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above. PMID:24048337

Electromyography (EMG) signal recognition using combined discrete wavelet transform based adaptive neuro-fuzzy inference systems (ANFIS)

NASA Astrophysics Data System (ADS)

Arozi, Moh; Putri, Farika T.; Ariyanto, Mochammad; Khusnul Ari, M.; Munadi, Setiawan, Joga D.

2017-01-01

People with disabilities are increasing from year to year either due to congenital factors, sickness, accident factors and war. One form of disability is the case of interruptions of hand function. The condition requires and encourages the search for solutions in the form of creating an artificial hand with the ability as a human hand. The development of science in the field of neuroscience currently allows the use of electromyography (EMG) to control the motion of artificial prosthetic hand into the necessary use of EMG as an input signal to control artificial prosthetic hand. This study is the beginning of a significant research planned in the development of artificial prosthetic hand with EMG signal input. This initial research focused on the study of EMG signal recognition. Preliminary results show that the EMG signal recognition using combined discrete wavelet transform and Adaptive Neuro-Fuzzy Inference System (ANFIS) produces accuracy 98.3 % for training and 98.51% for testing. Thus the results can be used as an input signal for Simulink block diagram of a prosthetic hand that will be developed on next study. The research will proceed with the construction of artificial prosthetic hand along with Simulink program controlling and integrating everything into one system.

[Data collection of signals in the multi-channel sEMG system of masticatory muscles and development and preliminary clinical application of an analytic system].

PubMed

Du, Hongliang; Li, Xin; Li, Shan; Zhang, Rui; Song, Rong; Li, Lan; Wang, Wei; Kang, Hong

2014-02-01

The aim of this study was to design a simple, economic, with high Common Mode Rejection Ratio (CMRR), preamplifier and multi-channel masticatory muscle surface electromyography (sEMG) signal acquisition system assisting to diagnose temporomandibular disorders (TMD). We used the USB interface technology in the EMG data with the aid of the windows to operate system and graphical interface. Eight patients with TMD and eight controls were analyzed separately using this system. In this system, we analyzed sEMG by an optional combination of time domain, frequency domain, time-frequency, several spectral analysis, wavelets and other special algorithms under multi-parameter. Multi-channel sEMG System of Masticatory Muscles is a simple, economic system. It has high sensitivity and specificity. The sEMG signals were changed in patients with TMD. The system would pave the way for diagnosis TMD and help us to assess the treatment effect. A novel and objective method is provided for diagnosis and treatment of oral-maxillofacial disease and functional reconstruction.

EMG-force relationship during static contraction: Effects on sensor placement locations on biceps brachii muscle.

PubMed

Ahamed, Nizam Uddin; Sundaraj, Kenneth; Alqahtani, Mahdi; Altwijri, Omar; Ali, Md Asraf; Islam, Md Anamul

2014-10-15

The relationship between surface electromyography (EMG) and force have been the subject of ongoing investigations and remain a subject of controversy. Even under static conditions, the relationships at different sensor placement locations in the biceps brachii (BB) muscle are complex. The aim of this study was to compare the activity and relationship between surface EMG and static force from the BB muscle in terms of three sensor placement locations. Twenty-one right hand dominant male subjects (age 25.3 ± 1.2 years) participated in the study. Surface EMG signals were detected from the subject's right BB muscle. The muscle activation during force was determined as the root mean square (RMS) electromyographic signal normalized to the peak RMS EMG signal of isometric contraction for 10 s. The statistical analysis included linear regression to examine the relationship between EMG amplitude and force of contraction [40-100% of maximal voluntary contraction (MVC)], repeated measures ANOVA to assess differences among the sensor placement locations, and coefficient of variation (CoV) for muscle activity variation. The results demonstrated that when the sensor was placed on the muscle belly, the linear slope coefficient was significantly greater for EMG versus force testing (r^{2} = 0.61, P > 0.05) than when placed on the lower part (r^{2}=0.31, P< 0.05) and upper part of the muscle belly (r^{2}=0.29, P > 0.05). In addition, the EMG signal activity on the muscle belly had less variability than the upper and lower parts (8.55% vs. 15.12% and 12.86%, respectively). These findings indicate the importance of applying the surface EMG sensor at the appropriate locations that follow muscle fiber orientation of the BB muscle during static contraction. As a result, EMG signals of three different placements may help to understand the difference in the amplitude of the signals due to placement.

Model for nerve visualization in preoperative image data based on intraoperatively gained EMG signals.

PubMed

Strauss, Mario; Lueders, Christian; Strauss, Gero; Stopp, Sebastian; Shi, Jiaxi; Lueth, Tim C

2008-01-01

While removing bone tissue of the mastoid, the facial nerve is at risk of being injured. In this contribution a model for nerve visualization in preoperative image data based on intraoperatively gained EMG signals is proposed. A neuro monitor can assist the surgeon locating and preserving the nerve. With the proposed model gained EMG signals can be spatially related to the patient resp. the image data. During navigation the detected nerve course will be visualized and hence permanently available for assessing the situs.

Low-cost assistive device for hand gesture recognition using sEMG

NASA Astrophysics Data System (ADS)

Kainz, Ondrej; Cymbalák, Dávid; Kardoš, Slavomír.; Fecil'ak, Peter; Jakab, František

2016-07-01

In this paper a low-cost solution for surface EMG (sEMG) signal retrieval is presented. The principal goal is to enable reading the temporal parameters of muscles activity by a computer device, with its further processing. Paper integrates design and deployment of surface electrodes and amplifier following the prior researches. Bearing in mind the goal of creating low-cost solution, the Arduino micro-controller was utilized for analog-to-digital conversion and communication. The software part of the system employs support vector machine (SVM) to classify the EMG signal, as acquired from sensors. Accuracy of the proposed solution achieves over 90 percent for six hand movements. Proposed solution is to be tested as an assistive device for several cases, involving people with motor disabilities and amputees.

Motor unit action potential conduction velocity estimated from surface electromyographic signals using image processing techniques.

PubMed

Soares, Fabiano Araujo; Carvalho, João Luiz Azevedo; Miosso, Cristiano Jacques; de Andrade, Marcelino Monteiro; da Rocha, Adson Ferreira

2015-09-17

In surface electromyography (surface EMG, or S-EMG), conduction velocity (CV) refers to the velocity at which the motor unit action potentials (MUAPs) propagate along the muscle fibers, during contractions. The CV is related to the type and diameter of the muscle fibers, ion concentration, pH, and firing rate of the motor units (MUs). The CV can be used in the evaluation of contractile properties of MUs, and of muscle fatigue. The most popular methods for CV estimation are those based on maximum likelihood estimation (MLE). This work proposes an algorithm for estimating CV from S-EMG signals, using digital image processing techniques. The proposed approach is demonstrated and evaluated, using both simulated and experimentally-acquired multichannel S-EMG signals. We show that the proposed algorithm is as precise and accurate as the MLE method in typical conditions of noise and CV. The proposed method is not susceptible to errors associated with MUAP propagation direction or inadequate initialization parameters, which are common with the MLE algorithm. Image processing -based approaches may be useful in S-EMG analysis to extract different physiological parameters from multichannel S-EMG signals. Other new methods based on image processing could also be developed to help solving other tasks in EMG analysis, such as estimation of the CV for individual MUs, localization and tracking of innervation zones, and study of MU recruitment strategies.

Generating Control Commands From Gestures Sensed by EMG

NASA Technical Reports Server (NTRS)

Wheeler, Kevin R.; Jorgensen, Charles

2006-01-01

An effort is under way to develop noninvasive neuro-electric interfaces through which human operators could control systems as diverse as simple mechanical devices, computers, aircraft, and even spacecraft. The basic idea is to use electrodes on the surface of the skin to acquire electromyographic (EMG) signals associated with gestures, digitize and process the EMG signals to recognize the gestures, and generate digital commands to perform the actions signified by the gestures. In an experimental prototype of such an interface, the EMG signals associated with hand gestures are acquired by use of several pairs of electrodes mounted in sleeves on a subject s forearm (see figure). The EMG signals are sampled and digitized. The resulting time-series data are fed as input to pattern-recognition software that has been trained to distinguish gestures from a given gesture set. The software implements, among other things, hidden Markov models, which are used to recognize the gestures as they are being performed in real time. Thus far, two experiments have been performed on the prototype interface to demonstrate feasibility: an experiment in synthesizing the output of a joystick and an experiment in synthesizing the output of a computer or typewriter keyboard. In the joystick experiment, the EMG signals were processed into joystick commands for a realistic flight simulator for an airplane. The acting pilot reached out into the air, grabbed an imaginary joystick, and pretended to manipulate the joystick to achieve left and right banks and up and down pitches of the simulated airplane. In the keyboard experiment, the subject pretended to type on a numerical keypad, and the EMG signals were processed into keystrokes. The results of the experiments demonstrate the basic feasibility of this method while indicating the need for further research to reduce the incidence of errors (including confusion among gestures). Topics that must be addressed include the numbers and arrangements

A comparative study of surface EMG classification by fuzzy relevance vector machine and fuzzy support vector machine.

PubMed

Xie, Hong-Bo; Huang, Hu; Wu, Jianhua; Liu, Lei

2015-02-01

We present a multiclass fuzzy relevance vector machine (FRVM) learning mechanism and evaluate its performance to classify multiple hand motions using surface electromyographic (sEMG) signals. The relevance vector machine (RVM) is a sparse Bayesian kernel method which avoids some limitations of the support vector machine (SVM). However, RVM still suffers the difficulty of possible unclassifiable regions in multiclass problems. We propose two fuzzy membership function-based FRVM algorithms to solve such problems, based on experiments conducted on seven healthy subjects and two amputees with six hand motions. Two feature sets, namely, AR model coefficients and room mean square value (AR-RMS), and wavelet transform (WT) features, are extracted from the recorded sEMG signals. Fuzzy support vector machine (FSVM) analysis was also conducted for wide comparison in terms of accuracy, sparsity, training and testing time, as well as the effect of training sample sizes. FRVM yielded comparable classification accuracy with dramatically fewer support vectors in comparison with FSVM. Furthermore, the processing delay of FRVM was much less than that of FSVM, whilst training time of FSVM much faster than FRVM. The results indicate that FRVM classifier trained using sufficient samples can achieve comparable generalization capability as FSVM with significant sparsity in multi-channel sEMG classification, which is more suitable for sEMG-based real-time control applications.

EOG-sEMG Human Interface for Communication

PubMed Central

Tamura, Hiroki; Yan, Mingmin; Sakurai, Keiko; Tanno, Koichi

2016-01-01

The aim of this study is to present electrooculogram (EOG) and surface electromyogram (sEMG) signals that can be used as a human-computer interface. Establishing an efficient alternative channel for communication without overt speech and hand movements is important for increasing the quality of life for patients suffering from amyotrophic lateral sclerosis, muscular dystrophy, or other illnesses. In this paper, we propose an EOG-sEMG human-computer interface system for communication using both cross-channels and parallel lines channels on the face with the same electrodes. This system could record EOG and sEMG signals as “dual-modality” for pattern recognition simultaneously. Although as much as 4 patterns could be recognized, dealing with the state of the patients, we only choose two classes (left and right motion) of EOG and two classes (left blink and right blink) of sEMG which are easily to be realized for simulation and monitoring task. From the simulation results, our system achieved four-pattern classification with an accuracy of 95.1%. PMID:27418924

EOG-sEMG Human Interface for Communication.

PubMed

Tamura, Hiroki; Yan, Mingmin; Sakurai, Keiko; Tanno, Koichi

2016-01-01

The aim of this study is to present electrooculogram (EOG) and surface electromyogram (sEMG) signals that can be used as a human-computer interface. Establishing an efficient alternative channel for communication without overt speech and hand movements is important for increasing the quality of life for patients suffering from amyotrophic lateral sclerosis, muscular dystrophy, or other illnesses. In this paper, we propose an EOG-sEMG human-computer interface system for communication using both cross-channels and parallel lines channels on the face with the same electrodes. This system could record EOG and sEMG signals as "dual-modality" for pattern recognition simultaneously. Although as much as 4 patterns could be recognized, dealing with the state of the patients, we only choose two classes (left and right motion) of EOG and two classes (left blink and right blink) of sEMG which are easily to be realized for simulation and monitoring task. From the simulation results, our system achieved four-pattern classification with an accuracy of 95.1%.

Wrist torque estimation during simultaneous and continuously changing movements: surface vs. untargeted intramuscular EMG.

PubMed

Kamavuako, Ernest N; Scheme, Erik J; Englehart, Kevin B

2013-06-01

In this paper, the predictive capability of surface and untargeted intramuscular electromyography (EMG) was compared with respect to wrist-joint torque to quantify which type of measurement better represents joint torque during multiple degrees-of-freedom (DoF) movements for possible application in prosthetic control. Ten able-bodied subjects participated in the study. Surface and intramuscular EMG was recorded concurrently from the right forearm. The subjects were instructed to track continuous contraction profiles using single and combined DoF in two trials. The association between torque and EMG was assessed using an artificial neural network. Results showed a significant difference between the two types of EMG (P < 0.007) for all performance metrics: coefficient of determination (R(2)), Pearson correlation coefficient (PCC), and root mean square error (RMSE). The performance of surface EMG (R(2) = 0.93 ± 0.03; PCC = 0.98 ± 0.01; RMSE = 8.7 ± 2.1%) was found to be superior compared with intramuscular EMG (R(2) = 0.80 ± 0.07; PCC = 0.93 ± 0.03; RMSE = 14.5 ± 2.9%). The higher values of PCC compared with R(2) indicate that both methods are able to track the torque profile well but have some trouble (particularly intramuscular EMG) in estimating the exact amplitude. The possible cause for the difference, thus the low performance of intramuscular EMG, may be attributed to the very high selectivity of the recordings used in this study.

EMG-based speech recognition using hidden markov models with global control variables.

PubMed

Lee, Ki-Seung

2008-03-01

It is well known that a strong relationship exists between human voices and the movement of articulatory facial muscles. In this paper, we utilize this knowledge to implement an automatic speech recognition scheme which uses solely surface electromyogram (EMG) signals. The sequence of EMG signals for each word is modelled by a hidden Markov model (HMM) framework. The main objective of the work involves building a model for state observation density when multichannel observation sequences are given. The proposed model reflects the dependencies between each of the EMG signals, which are described by introducing a global control variable. We also develop an efficient model training method, based on a maximum likelihood criterion. In a preliminary study, 60 isolated words were used as recognition variables. EMG signals were acquired from three articulatory facial muscles. The findings indicate that such a system may have the capacity to recognize speech signals with an accuracy of up to 87.07%, which is superior to the independent probabilistic model.

High efficiency and simple technique for controlling mechanisms by EMG signals

NASA Astrophysics Data System (ADS)

Dugarte, N.; Álvarez, A.; Balacco, J.; Mercado, G.; Gonzalez, A.; Dugarte, E.; Javier, F.; Ceballos, G.; Olivares, A.

2016-04-01

This article reports the development of a simple and efficient system that allows control of mechanisms through electromyography (EMG) signals. The novelty about this instrument is focused on individual control of each motion vector mechanism through independent electronic circuits. Each of electronic circuit does positions a motor according to intensity of EMG signal captured. This action defines movement in one mechanical axis considered from an initial point, based on increased muscle tension. The final displacement of mechanism depends on individual’s ability to handle the levels of muscle tension at different body parts. This is the design of a robotic arm where each degree of freedom is handled with a specific microcontroller that responds to signals taken from a defined muscle. The biophysical interaction between the person and the final positioning of the robotic arm is used as feedback. Preliminary tests showed that the control operates with minimal positioning error margins. The constant use of system with the same operator showed that the person adapts and progressively improves at control technique.

The linear synchronization measures of uterine EMG signals: Evidence of synchronized action potentials during propagation.

PubMed

Domino, Malgorzata; Pawlinski, Bartosz; Gajewski, Zdzislaw

2016-11-01

Evaluation of synchronization between myoelectric signals can give new insights into the functioning of the complex system of porcine myometrium. We propose a model of uterine contractions according to the hypothesis of action potentials similarity which is possible to detect during propagation in the uterine wall. We introduce similarity measures based on the concept of synchronization as used in matching linear signals such as electromyographic (EMG) time series data. The aim was to present linear measures to assess synchronization between contractions in different topographic regions of the uterus. We use the cross-correlation function (ƒx,y[l], ƒy,z[l]) and the cross-coherence function (Cxy[ƒ], Cyz[ƒ]) to assess synchronization between three data series of a diestral uterine EMG bundles in porcine reproductive tract. Spontaneous uterine activity was recorded using telemetry method directly by three-channel transmitter and three silver bipolar needle electrodes sutured on different topographic regions of the reproductive tract in the sow. The results show the usefulness of the cross-coherence function in that synchronization between uterine horn and corpus uteri for multiple action potentials (bundles) could be observed. The EMG bundles synchronization may be used to investigate the direction and velocity of EMG signals propagation in porcine reproductive tract. Copyright © 2016 Elsevier Inc. All rights reserved.

Extracting time-frequency feature of single-channel vastus medialis EMG signals for knee exercise pattern recognition.

PubMed

Zhang, Yi; Li, Peiyang; Zhu, Xuyang; Su, Steven W; Guo, Qing; Xu, Peng; Yao, Dezhong

2017-01-01

The EMG signal indicates the electrophysiological response to daily living of activities, particularly to lower-limb knee exercises. Literature reports have shown numerous benefits of the Wavelet analysis in EMG feature extraction for pattern recognition. However, its application to typical knee exercises when using only a single EMG channel is limited. In this study, three types of knee exercises, i.e., flexion of the leg up (standing), hip extension from a sitting position (sitting) and gait (walking) are investigated from 14 healthy untrained subjects, while EMG signals from the muscle group of vastus medialis and the goniometer on the knee joint of the detected leg are synchronously monitored and recorded. Four types of lower-limb motions including standing, sitting, stance phase of walking, and swing phase of walking, are segmented. The Wavelet Transform (WT) based Singular Value Decomposition (SVD) approach is proposed for the classification of four lower-limb motions using a single-channel EMG signal from the muscle group of vastus medialis. Based on lower-limb motions from all subjects, the combination of five-level wavelet decomposition and SVD is used to comprise the feature vector. The Support Vector Machine (SVM) is then configured to build a multiple-subject classifier for which the subject independent accuracy will be given across all subjects for the classification of four types of lower-limb motions. In order to effectively indicate the classification performance, EMG features from time-domain (e.g., Mean Absolute Value (MAV), Root-Mean-Square (RMS), integrated EMG (iEMG), Zero Crossing (ZC)) and frequency-domain (e.g., Mean Frequency (MNF) and Median Frequency (MDF)) are also used to classify lower-limb motions. The five-fold cross validation is performed and it repeats fifty times in order to acquire the robust subject independent accuracy. Results show that the proposed WT-based SVD approach has the classification accuracy of 91.85%±0.88% which

Does Heel Height Cause Imbalance during Sit-to-Stand Task: Surface EMG Perspective

PubMed Central

Naik, Ganesh R.; Al-Ani, Ahmed; Gobbo, Massimiliano; Nguyen, Hung T.

2017-01-01

The purpose of this study was to determine whether electromyography (EMG) muscle activities around the knee differ during sit-to-stand (STS) and returning task for females wearing shoes with different heel heights. Sixteen healthy young women (age = 25.2 ± 3.9 years, body mass index = 20.8 ± 2.7 kg/m2) participated in this study. Electromyography signals were recorded from the two muscles, vastus medialis (VM) and vastus lateralis (VL) that involve in the extension of knee. The participants wore shoes with five different heights, including 4, 6, 8, 10, and 12 cm. Surface electromyography (sEMG) data were acquired during STS and stand-to-sit-returning (STSR) tasks. The data was filtered using a fourth order Butterworth (band pass) filter of 20–450 Hz frequency range. For each heel height, we extracted median frequency (MDF) and root mean square (RMS) features to measure sEMG activities between VM and VL muscles. The experimental results (based on MDF and RMS-values) indicated that there is imbalance between vasti muscles for more elevated heels. The results are also quantified with statistical measures. The study findings suggest that there would be an increased likelihood of knee imbalance and fatigue with regular usage of high heel shoes (HHS) in women. PMID:28894422

EMG circuit design and AR analysis of EMG signs.

PubMed

Hardalaç, Firat; Canal, Rahmi

2004-12-01

In this study, electromyogram (EMG) circuit was designed and tested on 27 people. Autoregressive (AR) analysis of EMG signals recorded on the ulnar nerve region of the right hand in resting position was performed. AR method, especially in the calculation of the spectrums of stable signs, is used for frequency analysis of signs, which give frequency response as sharp peaks and valleys. In this study, as the result of AR method analysis of EMG signals frequency-time domain, frequency spectrum curves (histogram curves) were obtained. As the images belonging to these histograms were evaluated, fibrillation potential widths of the muscle fibers of the ulnar nerve region of the people (material of the study) were examined. According to the degeneration degrees of the motor nerves, nine people had myopathy, nine had neuropathy, and nine were normal.

Analysis and prediction of meal motion by EMG signals

NASA Astrophysics Data System (ADS)

Horihata, S.; Iwahara, H.; Yano, K.

2007-12-01

The lack of carers for senior citizens and physically handicapped persons in our country has now become a huge issue and has created a great need for carer robots. The usual carer robots (many of which have switches or joysticks for their interfaces), however, are neither easy to use it nor very popular. Therefore, haptic devices have been adopted for a human-machine interface that will enable an intuitive operation. At this point, a method is being tested that seeks to prevent a wrong operation from occurring from the user's signals. This method matches motions with EMG signals.

A novel biometric authentication approach using ECG and EMG signals.

PubMed

Belgacem, Noureddine; Fournier, Régis; Nait-Ali, Amine; Bereksi-Reguig, Fethi

2015-05-01

Security biometrics is a secure alternative to traditional methods of identity verification of individuals, such as authentication systems based on user name and password. Recently, it has been found that the electrocardiogram (ECG) signal formed by five successive waves (P, Q, R, S and T) is unique to each individual. In fact, better than any other biometrics' measures, it delivers proof of subject's being alive as extra information which other biometrics cannot deliver. The main purpose of this work is to present a low-cost method for online acquisition and processing of ECG signals for person authentication and to study the possibility of providing additional information and retrieve personal data from an electrocardiogram signal to yield a reliable decision. This study explores the effectiveness of a novel biometric system resulting from the fusion of information and knowledge provided by ECG and EMG (Electromyogram) physiological recordings. It is shown that biometrics based on these ECG/EMG signals offers a novel way to robustly authenticate subjects. Five ECG databases (MIT-BIH, ST-T, NSR, PTB and ECG-ID) and several ECG signals collected in-house from volunteers were exploited. A palm-based ECG biometric system was developed where the signals are collected from the palm of the subject through a minimally intrusive one-lead ECG set-up. A total of 3750 ECG beats were used in this work. Feature extraction was performed on ECG signals using Fourier descriptors (spectral coefficients). Optimum-Path Forest classifier was used to calculate the degree of similarity between individuals. The obtained results from the proposed approach look promising for individuals' authentication.

Analysis of the sEMG/force relationship using HD-sEMG technique and data fusion: A simulation study.

PubMed

Al Harrach, Mariam; Carriou, Vincent; Boudaoud, Sofiane; Laforet, Jeremy; Marin, Frederic

2017-04-01

The relationship between the surface Electromyogram (sEMG) signal and the force of an individual muscle is still ambiguous due to the complexity of experimental evaluation. However, understanding this relationship should be useful for the assessment of neuromuscular system in healthy and pathological contexts. In this study, we present a global investigation of the factors governing the shape of this relationship. Accordingly, we conducted a focused sensitivity analysis of the sEMG/force relationship form with respect to neural, functional and physiological parameters variation. For this purpose, we used a fast generation cylindrical model for the simulation of an 8×8 High Density-sEMG (HD-sEMG) grid and a twitch based force model for the muscle force generation. The HD-sEMG signals as well as the corresponding force signals were simulated in isometric non-fatiguing conditions and were based on the Biceps Brachii (BB) muscle properties. A total of 10 isometric constant contractions of 5s were simulated for each configuration of parameters. The Root Mean Squared (RMS) value was computed in order to quantify the sEMG amplitude. Then, an image segmentation method was used for data fusion of the 8×8 RMS maps. In addition, a comparative study between recent modeling propositions and the model proposed in this study is presented. The evaluation was made by computing the Normalized Root Mean Squared Error (NRMSE) of their fitting to the simulated relationship functions. Our results indicated that the relationship between the RMS (mV) and muscle force (N) can be modeled using a 3rd degree polynomial equation. Moreover, it appears that the obtained coefficients are patient-specific and dependent on physiological, anatomical and neural parameters. Copyright © 2017 Elsevier Ltd. All rights reserved.

EMG-Torque Dynamics Change With Contraction Bandwidth.

PubMed

Golkar, Mahsa A; Jalaleddini, Kian; Kearney, Robert E

2018-04-01

An accurate model for ElectroMyoGram (EMG)-torque dynamics has many uses. One of its applications which has gained high attention among researchers is its use, in estimating the muscle contraction level for the efficient control of prosthesis. In this paper, the dynamic relationship between the surface EMG and torque during isometric contractions at the human ankle was studied using system identification techniques. Subjects voluntarily modulated their ankle torque in dorsiflexion direction, by activating their tibialis anterior muscle, while tracking a pseudo-random binary sequence in a torque matching task. The effects of contraction bandwidth, described by torque spectrum, on EMG-torque dynamics were evaluated by varying the visual command switching time. Nonparametric impulse response functions (IRF) were estimated between the processed surface EMG and torque. It was demonstrated that: 1) at low contraction bandwidths, the identified IRFs had unphysiological anticipatory (i.e., non-causal) components, whose amplitude decreased as the contraction bandwidth increased. We hypothesized that this non-causal behavior arose, because the EMG input contained a component due to feedback from the output torque, i.e., it was recorded from within a closed-loop. Vision was not the feedback source since the non-causal behavior persisted when visual feedback was removed. Repeating the identification using a nonparametric closed-loop identification algorithm yielded causal IRFs at all bandwidths, supporting this hypothesis. 2) EMG-torque dynamics became faster and the bandwidth of system increased as contraction modulation rate increased. Thus, accurate prediction of torque from EMG signals must take into account the contraction bandwidth sensitivity of this system.

Improving surface EMG burst detection in infrahyoid muscles during swallowing using digital filters and discrete wavelet analysis.

PubMed

Restrepo-Agudelo, Sebastian; Roldan-Vasco, Sebastian; Ramirez-Arbelaez, Lina; Cadavid-Arboleda, Santiago; Perez-Giraldo, Estefania; Orozco-Duque, Andres

2017-08-01

The visual inspection is a widely used method for evaluating the surface electromyographic signal (sEMG) during deglutition, a process highly dependent of the examiners expertise. It is desirable to have a less subjective and automated technique to improve the onset detection in swallowing related muscles, which have a low signal-to-noise ratio. In this work, we acquired sEMG measured in infrahyoid muscles with high baseline noise of ten healthy adults during water swallowing tasks. Two methods were applied to find the combination of cutoff frequencies that achieve the most accurate onset detection: discrete wavelet decomposition based method and fixed steps variations of low and high cutoff frequencies of a digital bandpass filter. Teager-Kaiser Energy operator, root mean square and simple threshold method were applied for both techniques. Results show a narrowing of the effective bandwidth vs. the literature recommended parameters for sEMG acquisition. Both level 3 decomposition with mother wavelet db4 and bandpass filter with cutoff frequencies between 130 and 180Hz were optimal for onset detection in infrahyoid muscles. The proposed methodologies recognized the onset time with predictive power above 0.95, that is similar to previous findings but in larger and more superficial muscles in limbs. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automatic detection of motor unit innervation zones of the external anal sphincter by multichannel surface EMG.

PubMed

Ullah, Khalil; Cescon, Corrado; Afsharipour, Babak; Merletti, Roberto

2014-12-01

A method to detect automatically the location of innervation zones (IZs) from 16-channel surface EMG (sEMG) recordings from the external anal sphincter (EAS) muscle is presented in order to guide episiotomy during child delivery. The new algorithm (2DCorr) is applied to individual motor unit action potential (MUAP) templates and is based on bidimensional cross correlation between the interpolated image of each MUAP template and two images obtained by flipping upside-down (around a horizontal axis) and left-right (around a vertical axis) the original one. The method was tested on 640 simulated MUAP templates of the sphincter muscle and compared with previously developed algorithms (Radon Transform, RT; Template Match, TM). Experimental signals were detected from the EAS of 150 subjects using an intra-anal probe with 16 equally spaced circumferential electrodes. The results of the three algorithms were compared with the actual IZ location (simulated signal) and with IZ location provided by visual analysis (VA) (experimental signals). For simulated signals, the inter quartile error range (IQR) between the estimated and the actual locations of the IZ was 0.20, 0.23, 0.42, and 2.32 interelectrode distances (IED) for the VA, 2DCorr, RT and TM methods respectively. Copyright © 2014 Elsevier Ltd. All rights reserved.

Patterns of motor recruitment can be determined using surface EMG.

PubMed

Wakeling, James M

2009-04-01

Previous studies have reported how different populations of motor units (MUs) can be recruited during dynamic and locomotor tasks. It was hypothesised that the higher-threshold units would contribute higher-frequency components to the sEMG spectra due to their faster conduction velocities, and thus recruitment patterns that increase the proportion of high-threshold units active would lead to higher-frequency elements in the sEMG spectra. This idea was tested by using a model of varying recruitment coupled to a three-layer volume conductor model to generate a series of sEMG signals. The recruitment varied from (A) orderly recruitment where the lowest-threshold MUs were initially activated and higher-threshold MUs were sequentially recruited as the contraction progressed, (B) a recurrent inhibition model that started with orderly recruitment, but as the higher-threshold units were activated they inhibited the lower-threshold MUs (C) nine models with intermediate properties that were graded between these two extremes. The sEMG was processed using wavelet analysis and the spectral properties quantified by their mean frequency, and an angle theta that was determined from the principal components of the spectra. Recruitment strategies that resulted in a greater proportion of faster MUs being active had a significantly lower theta and higher mean frequency.

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography.

PubMed

Siu, Ho Chit; Shah, Julie A; Stirling, Leia A

2016-10-25

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces.

Classification of Anticipatory Signals for Grasp and Release from Surface Electromyography

PubMed Central

Siu, Ho Chit; Shah, Julie A.; Stirling, Leia A.

2016-01-01

Surface electromyography (sEMG) is a technique for recording natural muscle activation signals, which can serve as control inputs for exoskeletons and prosthetic devices. Previous experiments have incorporated these signals using both classical and pattern-recognition control methods in order to actuate such devices. We used the results of an experiment incorporating grasp and release actions with object contact to develop an intent-recognition system based on Gaussian mixture models (GMM) and continuous-emission hidden Markov models (HMM) of sEMG data. We tested this system with data collected from 16 individuals using a forearm band with distributed sEMG sensors. The data contain trials with shifted band alignments to assess robustness to sensor placement. This study evaluated and found that pattern-recognition-based methods could classify transient anticipatory sEMG signals in the presence of shifted sensor placement and object contact. With the best-performing classifier, the effect of label lengths in the training data was also examined. A mean classification accuracy of 75.96% was achieved through a unigram HMM method with five mixture components. Classification accuracy on different sub-movements was found to be limited by the length of the shortest sub-movement, which means that shorter sub-movements within dynamic sequences require larger training sets to be classified correctly. This classification of user intent is a potential control mechanism for a dynamic grasping task involving user contact with external objects and noise. Further work is required to test its performance as part of an exoskeleton controller, which involves contact with actuated external surfaces. PMID:27792155

Biomechanical Correlates of Surface Electromyography Signals Obtained during Swallowing by Healthy Adults

ERIC Educational Resources Information Center

Crary, Michael A.; Carnaby (Mann), Giselle D.; Groher, Michael E.

2006-01-01

Purpose: The purpose of this study was to describe biomechanical correlates of the surface electromyographic signal obtained during swallowing by healthy adult volunteers. Method: Seventeen healthy adults were evaluated with simultaneous videofluoroscopy and surface electromyography (sEMG) while swallowing 5 mL of liquid barium sulfate. Three…

[Evaluation of work-related biomechanical overload: techniques for the acquisition and analysis of surface EMG signal].

PubMed

Pigini, Lucia; Colombini, Daniela; Rabuffetti, M; Ferrarin, M

2010-01-01

The aim of this research was to obtain information concerning muscle fatigue and muscle activation levels by measuring quantitative parameters through the surface electromyographic signal, and use such information to integrate the OCRA (Occupational Repetitive Actions) method for risk assessment of upper limb biomechanical overload Along with the main risk factors associated with the development of work-related upper limb musculoskeletal disorders (UL WMSDs) like posture, movement, frequency of action and organizational factors, this method also takes into account the muscular effort. Unlike the other risk factors that can be directly measured during inspections on farms, muscular effort is currently estimated only via a subjective assessment scale (Borg CR-10 scale). A new apparatus and new procedures were implemented for synchronized EMG and video acquisition, which guarantee a high degree of inter- and intra-subject repeatability, and an ad hoc software for data elaboration was developed They have been specifically designed for "on the field" applications. The methodology was first tested in the laboratory on a group of 12 healthy subjects, studying a repetitive task, running in two different ways, (high/low OCRA index) and interspersed with isometric tests for an indirect measurement of dynamic fatigue. The methodology was then tested in a working environment to compare the muscular effort required during the use of different types of tools for pruning. Results of the laboratory protocol showed onset of fatigue for Anterior Deltoid, Middle Deltoid and Brachial Triceps muscles only for the high-risk index mode, as demonstrated by the significance of the Bonferroni tests (p < 0.05) on MDF (Median Frequency) calculated from isometric tests. They also showed significant differences in terms of higher level of muscle activation, and thus required force, in the case of high OCRA index work task compared to the one at low risk (Wilcoxon, p < 0.05) for all analysed

Grip Force and 3D Push-Pull Force Estimation Based on sEMG and GRNN

PubMed Central

Wu, Changcheng; Zeng, Hong; Song, Aiguo; Xu, Baoguo

2017-01-01

The estimation of the grip force and the 3D push-pull force (push and pull force in the three dimension space) from the electromyogram (EMG) signal is of great importance in the dexterous control of the EMG prosthetic hand. In this paper, an action force estimation method which is based on the eight channels of the surface EMG (sEMG) and the Generalized Regression Neural Network (GRNN) is proposed to meet the requirements of the force control of the intelligent EMG prosthetic hand. Firstly, the experimental platform, the acquisition of the sEMG, the feature extraction of the sEMG and the construction of GRNN are described. Then, the multi-channels of the sEMG when the hand is moving are captured by the EMG sensors attached on eight different positions of the arm skin surface. Meanwhile, a grip force sensor and a three dimension force sensor are adopted to measure the output force of the human's hand. The characteristic matrix of the sEMG and the force signals are used to construct the GRNN. The mean absolute value and the root mean square of the estimation errors, the correlation coefficients between the actual force and the estimated force are employed to assess the accuracy of the estimation. Analysis of variance (ANOVA) is also employed to test the difference of the force estimation. The experiments are implemented to verify the effectiveness of the proposed estimation method and the results show that the output force of the human's hand can be correctly estimated by using sEMG and GRNN method. PMID:28713231

Grip Force and 3D Push-Pull Force Estimation Based on sEMG and GRNN.

PubMed

Wu, Changcheng; Zeng, Hong; Song, Aiguo; Xu, Baoguo

2017-01-01

The estimation of the grip force and the 3D push-pull force (push and pull force in the three dimension space) from the electromyogram (EMG) signal is of great importance in the dexterous control of the EMG prosthetic hand. In this paper, an action force estimation method which is based on the eight channels of the surface EMG (sEMG) and the Generalized Regression Neural Network (GRNN) is proposed to meet the requirements of the force control of the intelligent EMG prosthetic hand. Firstly, the experimental platform, the acquisition of the sEMG, the feature extraction of the sEMG and the construction of GRNN are described. Then, the multi-channels of the sEMG when the hand is moving are captured by the EMG sensors attached on eight different positions of the arm skin surface. Meanwhile, a grip force sensor and a three dimension force sensor are adopted to measure the output force of the human's hand. The characteristic matrix of the sEMG and the force signals are used to construct the GRNN. The mean absolute value and the root mean square of the estimation errors, the correlation coefficients between the actual force and the estimated force are employed to assess the accuracy of the estimation. Analysis of variance (ANOVA) is also employed to test the difference of the force estimation. The experiments are implemented to verify the effectiveness of the proposed estimation method and the results show that the output force of the human's hand can be correctly estimated by using sEMG and GRNN method.

A sEMG model with experimentally based simulation parameters.

PubMed

Wheeler, Katherine A; Shimada, Hiroshima; Kumar, Dinesh K; Arjunan, Sridhar P

2010-01-01

A differential, time-invariant, surface electromyogram (sEMG) model has been implemented. While it is based on existing EMG models, the novelty of this implementation is that it assigns more accurate distributions of variables to create realistic motor unit (MU) characteristics. Variables such as muscle fibre conduction velocity, jitter (the change in the interpulse interval between subsequent action potential firings) and motor unit size have been considered to follow normal distributions about an experimentally obtained mean. In addition, motor unit firing frequencies have been considered to have non-linear and type based distributions that are in accordance with experimental results. Motor unit recruitment thresholds have been considered to be related to the MU type. The model has been used to simulate single channel differential sEMG signals from voluntary, isometric contractions of the biceps brachii muscle. The model has been experimentally verified by conducting experiments on three subjects. Comparison between simulated signals and experimental recordings shows that the Root Mean Square (RMS) increases linearly with force in both cases. The simulated signals also show similar values and rates of change of RMS to the experimental signals.

Stretchable human-machine interface based on skin-conformal sEMG electrodes with self-similar geometry

NASA Astrophysics Data System (ADS)

Dong, Wentao; Zhu, Chen; Hu, Wei; Xiao, Lin; Huang, Yong'an

2018-01-01

Current stretchable surface electrodes have attracted increasing attention owing to their potential applications in biological signal monitoring, wearable human-machine interfaces (HMIs) and the Internet of Things. The paper proposed a stretchable HMI based on a surface electromyography (sEMG) electrode with a self-similar serpentine configuration. The sEMG electrode was transfer-printed onto the skin surface conformally to monitor biological signals, followed by signal classification and controlling of a mobile robot. Such electrodes can bear rather large deformation (such as >30%) under an appropriate areal coverage. The sEMG electrodes have been used to record electrophysiological signals from different parts of the body with sharp curvature, such as the index finger, back of the neck and face, and they exhibit great potential for HMI in the fields of robotics and healthcare. The electrodes placed onto the two wrists would generate two different signals with the fist clenched and loosened. It is classified to four kinds of signals with a combination of the gestures from the two wrists, that is, four control modes. Experiments demonstrated that the electrodes were successfully used as an HMI to control the motion of a mobile robot remotely. Project supported by the National Natural Science Foundation of China (Nos. 51635007, 91323303).

Effect of vibrotactile feedback on an EMG-based proportional cursor control system.

PubMed

Li, Shunchong; Chen, Xingyu; Zhang, Dingguo; Sheng, Xinjun; Zhu, Xiangyang

2013-01-01

Surface electromyography (sEMG) has been introduced into the bio-mechatronics systems, however, most of them are lack of the sensory feedback. In this paper, the effect of vibrotactile feedback for a myoelectric cursor control system is investigated quantitatively. Simultaneous and proportional control signals are extracted from EMG using a muscle synergy model. Different types of feedback including vibrotactile feedback and visual feedback are added, assessed and compared with each other. The results show that vibrotactile feedback is capable of improving the performance of EMG-based human machine interface.

Effect of Vibration Training on Anaerobic Power and Quardroceps Surface EMG in Long Jumpers

ERIC Educational Resources Information Center

Liu, Bin; Luo, Jiong

2015-01-01

Objective: To explore the anaerobic power and surface EMG (sEMG) of quardrocep muscle in lower extremities after single vibration training intervention. Methods: 8 excellent male long jumpers voluntarily participated in this study. Four intervention modes were devised, including high frequency high amplitude (HFHA,30Hz,6mm), low frequency low…

Power frequency spectrum analysis of surface EMG signals of upper limb muscles during elbow flexion - A comparison between healthy subjects and stroke survivors.

PubMed

Angelova, Silvija; Ribagin, Simeon; Raikova, Rositsa; Veneva, Ivanka

2018-02-01

After a stroke, motor units stop working properly and large, fast-twitch units are more frequently affected. Their impaired functions can be investigated during dynamic tasks using electromyographic (EMG) signal analysis. The aim of this paper is to investigate changes in the parameters of the power/frequency function during elbow flexion between affected, non-affected, and healthy muscles. Fifteen healthy subjects and ten stroke survivors participated in the experiments. Electromyographic data from 6 muscles of the upper limbs during elbow flexion were filtered and normalized to the amplitudes of EMG signals during maximal isometric tasks. The moments when motion started and when the flexion angle reached its maximal value were found. Equal intervals of 0.3407 s were defined between these two moments and one additional interval before the start of the flexion (first one) was supplemented. For each of these intervals the power/frequency function of EMG signals was calculated. The mean (MNF) and median frequencies (MDF), the maximal power (MPw) and the area under the power function (APw) were calculated. MNF was always higher than MDF. A significant decrease in these frequencies was found in only three post-stroke survivors. The frequencies in the first time interval were nearly always the highest among all intervals. The maximal power was nearly zero during first time interval and increased during the next ones. The largest values of MPw and APw were found for the flexor muscles and they increased for the muscles of the affected arm compared to the non-affected one of stroke survivors. Copyright © 2017 Elsevier Ltd. All rights reserved.

EMG finger movement classification based on ANFIS

NASA Astrophysics Data System (ADS)

Caesarendra, W.; Tjahjowidodo, T.; Nico, Y.; Wahyudati, S.; Nurhasanah, L.

2018-04-01

An increase number of people suffering from stroke has impact to the rapid development of finger hand exoskeleton to enable an automatic physical therapy. Prior to the development of finger exoskeleton, a research topic yet important i.e. machine learning of finger gestures classification is conducted. This paper presents a study on EMG signal classification of 5 finger gestures as a preliminary study toward the finger exoskeleton design and development in Indonesia. The EMG signals of 5 finger gestures were acquired using Myo EMG sensor. The EMG signal features were extracted and reduced using PCA. The ANFIS based learning is used to classify reduced features of 5 finger gestures. The result shows that the classification of finger gestures is less than the classification of 7 hand gestures.

Fusion of spectral models for dynamic modeling of sEMG and skeletal muscle force.

PubMed

Potluri, Chandrasekhar; Anugolu, Madhavi; Chiu, Steve; Urfer, Alex; Schoen, Marco P; Naidu, D Subbaram

2012-01-01

In this paper, we present a method of combining spectral models using a Kullback Information Criterion (KIC) data fusion algorithm. Surface Electromyographic (sEMG) signals and their corresponding skeletal muscle force signals are acquired from three sensors and pre-processed using a Half-Gaussian filter and a Chebyshev Type- II filter, respectively. Spectral models - Spectral Analysis (SPA), Empirical Transfer Function Estimate (ETFE), Spectral Analysis with Frequency Dependent Resolution (SPFRD) - are extracted from sEMG signals as input and skeletal muscle force as output signal. These signals are then employed in a System Identification (SI) routine to establish the dynamic models relating the input and output. After the individual models are extracted, the models are fused by a probability based KIC fusion algorithm. The results show that the SPFRD spectral models perform better than SPA and ETFE models in modeling the frequency content of the sEMG/skeletal muscle force data.

ECG artifact cancellation in surface EMG signals by fractional order calculus application.

PubMed

Miljković, Nadica; Popović, Nenad; Djordjević, Olivera; Konstantinović, Ljubica; Šekara, Tomislav B

2017-03-01

New aspects for automatic electrocardiography artifact removal from surface electromyography signals by application of fractional order calculus in combination with linear and nonlinear moving window filters are explored. Surface electromyography recordings of skeletal trunk muscles are commonly contaminated with spike shaped artifacts. This artifact originates from electrical heart activity, recorded by electrocardiography, commonly present in the surface electromyography signals recorded in heart proximity. For appropriate assessment of neuromuscular changes by means of surface electromyography, application of a proper filtering technique of electrocardiography artifact is crucial. A novel method for automatic artifact cancellation in surface electromyography signals by applying fractional order calculus and nonlinear median filter is introduced. The proposed method is compared with the linear moving average filter, with and without prior application of fractional order calculus. 3D graphs for assessment of window lengths of the filters, crest factors, root mean square differences, and fractional calculus orders (called WFC and WRC graphs) have been introduced. For an appropriate quantitative filtering evaluation, the synthetic electrocardiography signal and analogous semi-synthetic dataset have been generated. The examples of noise removal in 10 able-bodied subjects and in one patient with muscle dystrophy are presented for qualitative analysis. The crest factors, correlation coefficients, and root mean square differences of the recorded and semi-synthetic electromyography datasets showed that the most successful method was the median filter in combination with fractional order calculus of the order 0.9. Statistically more significant (p < 0.001) ECG peak reduction was obtained by the median filter application compared to the moving average filter in the cases of low level amplitude of muscle contraction compared to ECG spikes. The presented results suggest

Artificial neural network EMG classifier for functional hand grasp movements prediction.

PubMed

Gandolla, Marta; Ferrante, Simona; Ferrigno, Giancarlo; Baldassini, Davide; Molteni, Franco; Guanziroli, Eleonora; Cotti Cottini, Michele; Seneci, Carlo; Pedrocchi, Alessandra

2017-12-01

Objective To design and implement an electromyography (EMG)-based controller for a hand robotic assistive device, which is able to classify the user's motion intention before the effective kinematic movement execution. Methods Multiple degrees-of-freedom hand grasp movements (i.e. pinching, grasp an object, grasping) were predicted by means of surface EMG signals, recorded from 10 bipolar EMG electrodes arranged in a circular configuration around the forearm 2-3 cm from the elbow. Two cascaded artificial neural networks were then exploited to detect the patient's motion intention from the EMG signal window starting from the electrical activity onset to movement onset (i.e. electromechanical delay). Results The proposed approach was tested on eight healthy control subjects (4 females; age range 25-26 years) and it demonstrated a mean ± SD testing performance of 76% ± 14% for correctly predicting healthy users' motion intention. Two post-stroke patients tested the controller and obtained 79% and 100% of correctly classified movements under testing conditions. Conclusion A task-selection controller was developed to estimate the intended movement from the EMG measured during the electromechanical delay.

Artificial neural network EMG classifier for functional hand grasp movements prediction

PubMed Central

Ferrante, Simona; Ferrigno, Giancarlo; Baldassini, Davide; Molteni, Franco; Guanziroli, Eleonora; Cotti Cottini, Michele; Seneci, Carlo; Pedrocchi, Alessandra

2016-01-01

Objective To design and implement an electromyography (EMG)-based controller for a hand robotic assistive device, which is able to classify the user's motion intention before the effective kinematic movement execution. Methods Multiple degrees-of-freedom hand grasp movements (i.e. pinching, grasp an object, grasping) were predicted by means of surface EMG signals, recorded from 10 bipolar EMG electrodes arranged in a circular configuration around the forearm 2–3 cm from the elbow. Two cascaded artificial neural networks were then exploited to detect the patient's motion intention from the EMG signal window starting from the electrical activity onset to movement onset (i.e. electromechanical delay). Results The proposed approach was tested on eight healthy control subjects (4 females; age range 25–26 years) and it demonstrated a mean ± SD testing performance of 76% ± 14% for correctly predicting healthy users' motion intention. Two post-stroke patients tested the controller and obtained 79% and 100% of correctly classified movements under testing conditions. Conclusion A task-selection controller was developed to estimate the intended movement from the EMG measured during the electromechanical delay. PMID:27677300

Extraction of the brachialis muscle activity using HD-sEMG technique and canonical correlation analysis.

PubMed

Al Harrach, M; Afsharipour, B; Boudaoud, S; Carriou, V; Marin, F; Merletti, R

2016-08-01

The Brachialis (BR) is placed under the Biceps Brachii (BB) deep in the upper arm. Therefore, the detection of the corresponding surface Electromyogram (sEMG) is a complex task. The BR is an important elbow flexor, but it is usually not considered in the sEMG based force estimation process. The aim of this study was to attempt to separate the two sEMG activities of the BR and the BB by using a High Density sEMG (HD-sEMG) grid placed at the upper arm and Canonical Component Analysis (CCA) technique. For this purpose, we recorded sEMG signals from seven subjects with two 8 × 4 electrode grids placed over BB and BR. Four isometric voluntary contraction levels were recorded (5, 10, 30 and 50 %MVC) for 90° elbow angle. Then using CCA and image processing tools the sources of each muscle activity were separated. Finally, the corresponding sEMG signals were reconstructed using the remaining canonical components in order to retrieve the activity of the BB and the BR muscles.

Evaluation of higher order statistics parameters for multi channel sEMG using different force levels.

PubMed

Naik, Ganesh R; Kumar, Dinesh K

2011-01-01

The electromyograpy (EMG) signal provides information about the performance of muscles and nerves. The shape of the muscle signal and motor unit action potential (MUAP) varies due to the movement of the position of the electrode or due to changes in contraction level. This research deals with evaluating the non-Gaussianity in Surface Electromyogram signal (sEMG) using higher order statistics (HOS) parameters. To achieve this, experiments were conducted for four different finger and wrist actions at different levels of Maximum Voluntary Contractions (MVCs). Our experimental analysis shows that at constant force and for non-fatiguing contractions, probability density functions (PDF) of sEMG signals were non-Gaussian. For lesser MVCs (below 30% of MVC) PDF measures tends to be Gaussian process. The above measures were verified by computing the Kurtosis values for different MVCs.

Latent Factors Limiting the Performance of sEMG-Interfaces

PubMed Central

Lobov, Sergey; Krilova, Nadia; Kazantsev, Victor

2018-01-01

Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG) have fostered the use of sEMG human–machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here, we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game. We show that the degree of muscle cooperation and the amount of the body fatty tissue are the decisive factors in synthetic tests. Our data suggest that these factors can only be adjusted by long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. Short-term training has no effect on synthetic tests, but significantly increases the game scoring. This implies that it works at a higher decision-making level, not relevant for synthetic gestures. We propose a procedure that enables quantification of the gestures’ fidelity in a dynamic gaming environment. For each individual subject, the approach allows identifying “problematic” gestures that decrease gaming performance. This information can be used for optimizing the training strategy and for adapting the signal processing algorithms to individual users, which could be a way for a qualitative leap in the development of future sEMG-interfaces. PMID:29642410

Latent Factors Limiting the Performance of sEMG-Interfaces.

PubMed

Lobov, Sergey; Krilova, Nadia; Kastalskiy, Innokentiy; Kazantsev, Victor; Makarov, Valeri A

2018-04-06

Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG) have fostered the use of sEMG human-machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here, we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game. We show that the degree of muscle cooperation and the amount of the body fatty tissue are the decisive factors in synthetic tests. Our data suggest that these factors can only be adjusted by long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. Short-term training has no effect on synthetic tests, but significantly increases the game scoring. This implies that it works at a higher decision-making level, not relevant for synthetic gestures. We propose a procedure that enables quantification of the gestures' fidelity in a dynamic gaming environment. For each individual subject, the approach allows identifying "problematic" gestures that decrease gaming performance. This information can be used for optimizing the training strategy and for adapting the signal processing algorithms to individual users, which could be a way for a qualitative leap in the development of future sEMG-interfaces.

Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG

PubMed Central

Belbasis, Aaron; Fuss, Franz Konstantin

2018-01-01

Muscle activity and fatigue performance parameters were obtained and compared between both a smart compression garment and the gold-standard, a surface electromyography (EMG) system during high-speed cycling in seven participants. The smart compression garment, based on force myography (FMG), comprised of integrated pressure sensors that were sandwiched between skin and garment, located on five thigh muscles. The muscle activity was assessed by means of crank cycle diagrams (polar plots) that displayed the muscle activity relative to the crank cycle. The fatigue was assessed by means of the median frequency of the power spectrum of the EMG signal; the fractal dimension (FD) of the EMG signal; and the FD of the pressure signal. The smart compression garment returned performance parameters (muscle activity and fatigue) comparable to the surface EMG. The major differences were that the EMG measured the electrical activity, whereas the pressure sensor measured the mechanical activity. As such, there was a phase shift between electrical and mechanical signals, with the electrical signals preceding the mechanical counterparts in most cases. This is specifically pronounced in high-speed cycling. The fatigue trend over the duration of the cycling exercise was clearly reflected in the fatigue parameters (FDs and median frequency) obtained from pressure and EMG signals. The fatigue parameter of the pressure signal (FD) showed a higher time dependency (R2 = 0.84) compared to the EMG signal. This reflects that the pressure signal puts more emphasis on the fatigue as a function of time rather than on the origin of fatigue (e.g., peripheral or central fatigue). In light of the high-speed activity results, caution should be exerted when using data obtained from EMG for biomechanical models. In contrast to EMG data, activity data obtained from FMG are considered more appropriate and accurate as an input for biomechanical modeling as they truly reflect the mechanical muscle

Muscle Performance Investigated With a Novel Smart Compression Garment Based on Pressure Sensor Force Myography and Its Validation Against EMG.

PubMed

Belbasis, Aaron; Fuss, Franz Konstantin

2018-01-01

Muscle activity and fatigue performance parameters were obtained and compared between both a smart compression garment and the gold-standard, a surface electromyography (EMG) system during high-speed cycling in seven participants. The smart compression garment, based on force myography (FMG), comprised of integrated pressure sensors that were sandwiched between skin and garment, located on five thigh muscles. The muscle activity was assessed by means of crank cycle diagrams (polar plots) that displayed the muscle activity relative to the crank cycle. The fatigue was assessed by means of the median frequency of the power spectrum of the EMG signal; the fractal dimension (FD) of the EMG signal; and the FD of the pressure signal. The smart compression garment returned performance parameters (muscle activity and fatigue) comparable to the surface EMG. The major differences were that the EMG measured the electrical activity, whereas the pressure sensor measured the mechanical activity. As such, there was a phase shift between electrical and mechanical signals, with the electrical signals preceding the mechanical counterparts in most cases. This is specifically pronounced in high-speed cycling. The fatigue trend over the duration of the cycling exercise was clearly reflected in the fatigue parameters (FDs and median frequency) obtained from pressure and EMG signals. The fatigue parameter of the pressure signal (FD) showed a higher time dependency ( R 2 = 0.84) compared to the EMG signal. This reflects that the pressure signal puts more emphasis on the fatigue as a function of time rather than on the origin of fatigue (e.g., peripheral or central fatigue). In light of the high-speed activity results, caution should be exerted when using data obtained from EMG for biomechanical models. In contrast to EMG data, activity data obtained from FMG are considered more appropriate and accurate as an input for biomechanical modeling as they truly reflect the mechanical muscle

An intelligent system with EMG-based joint angle estimation for telemanipulation.

PubMed

Suryanarayanan, S; Reddy, N P; Gupta, V

1996-01-01

Bio-control of telemanipulators is being researched as an alternate control strategy. This study investigates the use of surface EMG from the biceps to predict joint angle during flexion of the arm that can be used to control an anthropomorphic telemanipulator. An intelligent system based on neural networks and fuzzy logic has been developed to use the processed surface EMG signal and predict the joint angle. The system has been tested on various angles of flexion-extension of the arm and at several speeds of flexion-extension. Preliminary results show the RMS error between the predicted angle and the actual angle to be less than 3% during training and less than 15% during testing. The technique of direct bio-control using EMG has the potential as an interface for telemanipulation applications.

Characteristics of EMG frequency bands in temporomandibullar disorders patients.

PubMed

Politti, Fabiano; Casellato, Claudia; Kalytczak, Marcelo Martins; Garcia, Marilia Barbosa Santos; Biasotto-Gonzalez, Daniela Aparecida

2016-12-01

The aim of the present study was to determine whether any specific frequency bands of surface electromyographic (sEMG) signals are more susceptible to alterations in patients with temporomandibular disorders (TMD), when compared with healthy subjects. Twenty-seven healthy adults (19 women and eight men; mean age: 23±6.68years) and 27 TMD patients (20 women and seven men; mean age: 24±5.89years) voluntarily participated in the experiment. sEMG data were recorded from the right and left masseter muscles (RM and LM) and the right and left anterior temporalis muscles (RT and LT) as the participants performed tests of chewing (CHW) and maximal clenching effort (MCE). Frequency domain analysis of the sEMG signal was used to analyze differences between TMD patients and healthy subjects in relation to the Power Spectral Density Function (PSDF). The analysis focused on the median frequency (MDF) of the sEMG signal and PSDF frequency bands after the EMG spectrum was divided into twenty-five frequency band of 20Hz each. The Mann-Whitney test was used to compare MDF between TMD patients and healthy subjects and the frequency bands were analyzed using three-way ANOVA with three factors: frequency band, muscle and group. The results of the analysis confirmed that the median frequency values in TMD patients were significantly higher (p<0.05) than those recorded for healthy subjects in the two experimental conditions (MCE and CHW), for all of the muscles assessed (RM, LM, RT and LT). In addition, frequency content between 20 and 100Hz of the normalized PSDF range was significantly lower (p<0.05) in TMD patients than in healthy. This study contributes to quantitatively identify TMD dysfunctions, by non-invasive sEMGs; this assessment is clinically important and still lacking nowadays. Copyright © 2016 Elsevier Ltd. All rights reserved.

Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model.

PubMed

Ngeo, Jimson G; Tamei, Tomoya; Shibata, Tomohiro

2014-08-14

Surface electromyography (EMG) signals are often used in many robot and rehabilitation applications because these reflect motor intentions of users very well. However, very few studies have focused on the accurate and proportional control of the human hand using EMG signals. Many have focused on discrete gesture classification and some have encountered inherent problems such as electro-mechanical delays (EMD). Here, we present a new method for estimating simultaneous and multiple finger kinematics from multi-channel surface EMG signals. In this study, surface EMG signals from the forearm and finger kinematic data were extracted from ten able-bodied subjects while they were tasked to do individual and simultaneous multiple finger flexion and extension movements in free space. Instead of using traditional time-domain features of EMG, an EMG-to-Muscle Activation model that parameterizes EMD was used and shown to give better estimation performance. A fast feed forward artificial neural network (ANN) and a nonparametric Gaussian Process (GP) regressor were both used and evaluated to estimate complex finger kinematics, with the latter rarely used in the other related literature. The estimation accuracies, in terms of mean correlation coefficient, were 0.85 ± 0.07, 0.78 ± 0.06 and 0.73 ± 0.04 for the metacarpophalangeal (MCP), proximal interphalangeal (PIP) and the distal interphalangeal (DIP) finger joint DOFs, respectively. The mean root-mean-square error in each individual DOF ranged from 5 to 15%. We show that estimation improved using the proposed muscle activation inputs compared to other features, and that using GP regression gave better estimation results when using fewer training samples. The proposed method provides a viable means of capturing the general trend of finger movements and shows a good way of estimating finger joint kinematics using a muscle activation model that parameterizes EMD. The results from this study demonstrates a potential control

M-wave normalization of EMG signal to investigate heat stress and fatigue.

PubMed

Girard, Olivier; Bishop, David J; Racinais, Sébastien

2018-05-01

We examined the extent to which peripheral changes affect EMG signal adjustments during repeated sprinting in temperate and hot conditions. Randomised, crossover study. Ten males performed 10×6-s 'all-out' cycling sprints (recovery=30s) in either a temperate (24°C/30%rH) or a hot (35°C/40%rH) environment with concomitant surface EMG recordings of the vastus lateralis (VL) and rectus femoris (RF). In addition, peak-to-peak M-wave amplitudes were obtained for each muscle after each sprint (i.e., 15s into recovery). For both the VL and RF muscles RMS decreased across sprint repetitions (P<0.01), while significantly lower values for the VL (P=0.012), but not the RF (P=0.096), occurred in hot vs. temperate conditions. M-wave-normalised RMS for VL muscle decreased across sprint repetitions (P=0.030), with no condition or interaction effects (both P>0.621). M-wave-normalised RMS for the RF muscle was lower in the heat (P<0.034), with no significant sprint or interaction effects (both P>0.240). Controlling for changes in maximal M-wave amplitude of the quadriceps muscles after each bout of a repeated cycling exercise in hot and temperate conditions allows researchers to account for fatigue- and/or heat-induced neural and peripheral adjustments. Copyright © 2017 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

EMG normalization to study muscle activation in cycling.

PubMed

Rouffet, David M; Hautier, Christophe A

2008-10-01

The value of electromyography (EMG) is sensitive to many physiological and non-physiological factors. The purpose of the present study was to determine if the torque-velocity test (T-V) can be used to normalize EMG signals into a framework of biological significance. Peak EMG amplitude of gluteus maximus (GMAX), vastus lateralis (VL), rectus femoris (RF), biceps femoris long head (BF), gastrocnemius medialis (GAS) and soleus (SOL) was calculated for nine subjects during isometric maximal voluntary contractions (IMVC) and torque-velocity bicycling tests (T-V). Then, the reference EMG signals obtained from IMVC and T-V bicycling tests were used to normalize the amplitude of the EMG signals collected for 15 different submaximal pedaling conditions. The results of this study showed that the repeatability of the measurements between IMVC (from 10% to 23%) and T-V (from 8% to 20%) was comparable. The amplitude of the peak EMG of VL was 99+/-43% higher (p<0.001) when measured during T-V. Moreover, the inter-individual variability of the EMG patterns calculated for submaximal cycling exercises differed significantly when using T-V bicycling normalization method (GMAX: 0.33+/-0.16 vs. 1.09+/-0.04, VL: 0.07+/-0.02 vs. 0.64+/-0.14, SOL: 0.07+/-0.03 vs. 1.00+/-0.07, RF: 1.21+/-0.20 vs. 0.92+/-0.13, BF: 1.47+/-0.47 vs. 0.84+/-0.11). It was concluded that T-V bicycling test offers the advantage to be less time and energy-consuming and to be as repeatable as IMVC tests to measure peak EMG amplitude. Furthermore, this normalization method avoids the impact of non-physiological factors on the amplitude of the EMG signals so that it allows quantifying better the activation level of lower limb muscles and the variability of the EMG patterns during submaximal bicycling exercises.

Estimating Isometric Tension of Finger Muscle Using Needle EMG Signals and the Twitch Contraction Model

NASA Astrophysics Data System (ADS)

Tachibana, Hideyuki; Suzuki, Takafumi; Mabuchi, Kunihiko

We address an estimation method of isometric muscle tension of fingers, as fundamental research for a neural signal-based prosthesis of fingers. We utilize needle electromyogram (EMG) signals, which have approximately equivalent information to peripheral neural signals. The estimating algorithm comprised two convolution operations. The first convolution is between normal distribution and a spike array, which is detected by needle EMG signals. The convolution estimates the probability density of spike-invoking time in the muscle. In this convolution, we hypothesize that each motor unit in a muscle activates spikes independently based on a same probability density function. The second convolution is between the result of the previous convolution and isometric twitch, viz., the impulse response of the motor unit. The result of the calculation is the sum of all estimated tensions of whole muscle fibers, i.e., muscle tension. We confirmed that there is good correlation between the estimated tension of the muscle and the actual tension, with >0.9 correlation coefficients at 59%, and >0.8 at 89% of all trials.

Multi-step EMG Classification Algorithm for Human-Computer Interaction

NASA Astrophysics Data System (ADS)

Ren, Peng; Barreto, Armando; Adjouadi, Malek

A three-electrode human-computer interaction system, based on digital processing of the Electromyogram (EMG) signal, is presented. This system can effectively help disabled individuals paralyzed from the neck down to interact with computers or communicate with people through computers using point-and-click graphic interfaces. The three electrodes are placed on the right frontalis, the left temporalis and the right temporalis muscles in the head, respectively. The signal processing algorithm used translates the EMG signals during five kinds of facial movements (left jaw clenching, right jaw clenching, eyebrows up, eyebrows down, simultaneous left & right jaw clenching) into five corresponding types of cursor movements (left, right, up, down and left-click), to provide basic mouse control. The classification strategy is based on three principles: the EMG energy of one channel is typically larger than the others during one specific muscle contraction; the spectral characteristics of the EMG signals produced by the frontalis and temporalis muscles during different movements are different; the EMG signals from adjacent channels typically have correlated energy profiles. The algorithm is evaluated on 20 pre-recorded EMG signal sets, using Matlab simulations. The results show that this method provides improvements and is more robust than other previous approaches.

A canonical correlation analysis based EMG classification algorithm for eliminating electrode shift effect.

PubMed

Zhe Fan; Zhong Wang; Guanglin Li; Ruomei Wang

2016-08-01

Motion classification system based on surface Electromyography (sEMG) pattern recognition has achieved good results in experimental condition. But it is still a challenge for clinical implement and practical application. Many factors contribute to the difficulty of clinical use of the EMG based dexterous control. The most obvious and important is the noise in the EMG signal caused by electrode shift, muscle fatigue, motion artifact, inherent instability of signal and biological signals such as Electrocardiogram. In this paper, a novel method based on Canonical Correlation Analysis (CCA) was developed to eliminate the reduction of classification accuracy caused by electrode shift. The average classification accuracy of our method were above 95% for the healthy subjects. In the process, we validated the influence of electrode shift on motion classification accuracy and discovered the strong correlation with correlation coefficient of >0.9 between shift position data and normal position data.

Nonlinear parameters of surface EMG in schizophrenia patients depend on kind of antipsychotic therapy.

PubMed

Meigal, Alexander Yu; Miroshnichenko, German G; Kuzmina, Anna P; Rissanen, Saara M; Georgiadis, Stefanos D; Karjalainen, Pasi A

2015-01-01

We compared a set of surface EMG (sEMG) parameters in several groups of schizophrenia (SZ, n = 74) patients and healthy controls (n = 11) and coupled them with the clinical data. sEMG records were quantified with spectral, mutual information (MI) based and recurrence quantification analysis (RQA) parameters, and with approximate and sample entropies (ApEn and SampEn). Psychotic deterioration was estimated with Positive and Negative Syndrome Scale (PANSS) and with the positive subscale of PANSS. Neuroleptic-induced parkinsonism (NIP) motor symptoms were estimated with Simpson-Angus Scale (SAS). Dyskinesia was measured with Abnormal Involuntary Movement Scale (AIMS). We found that there was no difference in values of sEMG parameters between healthy controls and drug-naïve SZ patients. The most specific group was formed of SZ patients who were administered both typical and atypical antipsychotics (AP). Their sEMG parameters were significantly different from those of SZ patients taking either typical or atypical AP or taking no AP. This may represent a kind of synergistic effect of these two classes of AP. For the clinical data we found that PANSS, SAS, and AIMS were not correlated to any of the sEMG parameters. with nonlinear parameters of sEMG it is possible to reveal NIP in SZ patients, and it may help to discriminate between different clinical groups of SZ patients. Combined typical and atypical AP therapy has stronger effect on sEMG than a therapy with AP of only one class.

[Integration of the functional signal of intraoperative EMG of the facial nerve in to navigation model for surgery of the petrous bone].

PubMed

Strauss, G; Strauss, M; Lüders, C; Stopp, S; Shi, J; Dietz, A; Lüth, T

2008-10-01

PROBLEM DEFINITION: The goal of this work is the integration of the information of the intraoperative EMG monitoring of the facial nerve into the radiological data of the petrous bone. The following hypotheses are to be examined: (I) the N. VII can be determined intraoperatively with a high reliability by the stimulation-probe. A computer program is able to discriminate true-positive EMG signals from false-positive artifacts. (II) The course of the facial nerve can be registered in a three-dimensional area by EMG signals at a nerve model in the lab test. The individual items of the nerve can be combined into a route model. The route model can be integrated into the data of digital volume tomography (DVT). (I) Intraoperative EMG signals of the facial nerve were classified at 128 measurements by an automatic software. The results were correlated with the actual intraoperative situation. (II) The nerve phantom was designed and a DVT data set was provided. Phantom was registered with a navigation system (Karl Storz NPU, Tuttlingen, Germany). The stimulation probe of the EMG-system was tracked by the navigation system. The navigation system was extended by a processing unit (MiMed, Technische Universität München, Germany). Thus the classified EMG parameters of the facial route can be received, processed and be generated to a model of the facial nerve route. The operability was examined at 120 (10 x 12) measuring points. The evaluation of the examined algorithm for classification EMG-signals of the facial nerve resulted as correct in all measuring events. In all 10 attempts it succeeded to visualize the nerve route as three-dimensional model. The different sizes of the individual measuring points reflect the appropriate values of Istim and UEMG correctly. This work proves the feasibility of an automatic classification of an intraoperative EMG signal of the facial nerve by a processing unit. Furthermore the work shows the feasibility of tracking of the position of the

Physiological modules for generating discrete and rhythmic movements: component analysis of EMG signals.

PubMed

Bengoetxea, Ana; Leurs, Françoise; Hoellinger, Thomas; Cebolla, Ana Maria; Dan, Bernard; Cheron, Guy; McIntyre, Joseph

2014-01-01

A central question in Neuroscience is that of how the nervous system generates the spatiotemporal commands needed to realize complex gestures, such as handwriting. A key postulate is that the central nervous system (CNS) builds up complex movements from a set of simpler motor primitives or control modules. In this study we examined the control modules underlying the generation of muscle activations when performing different types of movement: discrete, point-to-point movements in eight different directions and continuous figure-eight movements in both the normal, upright orientation and rotated 90°. To test for the effects of biomechanical constraints, movements were performed in the frontal-parallel or sagittal planes, corresponding to two different nominal flexion/abduction postures of the shoulder. In all cases we measured limb kinematics and surface electromyographic activity (EMG) signals for seven different muscles acting around the shoulder. We first performed principal component analysis (PCA) of the EMG signals on a movement-by-movement basis. We found a surprisingly consistent pattern of muscle groupings across movement types and movement planes, although we could detect systematic differences between the PCs derived from movements performed in each shoulder posture and between the principal components associated with the different orientations of the figure. Unexpectedly we found no systematic differences between the figure eights and the point-to-point movements. The first three principal components could be associated with a general co-contraction of all seven muscles plus two patterns of reciprocal activation. From these results, we surmise that both "discrete-rhythmic movements" such as the figure eight, and discrete point-to-point movement may be constructed from three different fundamental modules, one regulating the impedance of the limb over the time span of the movement and two others operating to generate movement, one aligned with the

Physiological modules for generating discrete and rhythmic movements: component analysis of EMG signals

PubMed Central

Bengoetxea, Ana; Leurs, Françoise; Hoellinger, Thomas; Cebolla, Ana Maria; Dan, Bernard; Cheron, Guy; McIntyre, Joseph

2015-01-01

A central question in Neuroscience is that of how the nervous system generates the spatiotemporal commands needed to realize complex gestures, such as handwriting. A key postulate is that the central nervous system (CNS) builds up complex movements from a set of simpler motor primitives or control modules. In this study we examined the control modules underlying the generation of muscle activations when performing different types of movement: discrete, point-to-point movements in eight different directions and continuous figure-eight movements in both the normal, upright orientation and rotated 90°. To test for the effects of biomechanical constraints, movements were performed in the frontal-parallel or sagittal planes, corresponding to two different nominal flexion/abduction postures of the shoulder. In all cases we measured limb kinematics and surface electromyographic activity (EMG) signals for seven different muscles acting around the shoulder. We first performed principal component analysis (PCA) of the EMG signals on a movement-by-movement basis. We found a surprisingly consistent pattern of muscle groupings across movement types and movement planes, although we could detect systematic differences between the PCs derived from movements performed in each shoulder posture and between the principal components associated with the different orientations of the figure. Unexpectedly we found no systematic differences between the figure eights and the point-to-point movements. The first three principal components could be associated with a general co-contraction of all seven muscles plus two patterns of reciprocal activation. From these results, we surmise that both “discrete-rhythmic movements” such as the figure eight, and discrete point-to-point movement may be constructed from three different fundamental modules, one regulating the impedance of the limb over the time span of the movement and two others operating to generate movement, one aligned with the

Nonlinear parameters of surface EMG in schizophrenia patients depend on kind of antipsychotic therapy

PubMed Central

Meigal, Alexander Yu.; Miroshnichenko, German G.; Kuzmina, Anna P.; Rissanen, Saara M.; Georgiadis, Stefanos D.; Karjalainen, Pasi A.

2015-01-01

We compared a set of surface EMG (sEMG) parameters in several groups of schizophrenia (SZ, n = 74) patients and healthy controls (n = 11) and coupled them with the clinical data. sEMG records were quantified with spectral, mutual information (MI) based and recurrence quantification analysis (RQA) parameters, and with approximate and sample entropies (ApEn and SampEn). Psychotic deterioration was estimated with Positive and Negative Syndrome Scale (PANSS) and with the positive subscale of PANSS. Neuroleptic-induced parkinsonism (NIP) motor symptoms were estimated with Simpson-Angus Scale (SAS). Dyskinesia was measured with Abnormal Involuntary Movement Scale (AIMS). We found that there was no difference in values of sEMG parameters between healthy controls and drug-naïve SZ patients. The most specific group was formed of SZ patients who were administered both typical and atypical antipsychotics (AP). Their sEMG parameters were significantly different from those of SZ patients taking either typical or atypical AP or taking no AP. This may represent a kind of synergistic effect of these two classes of AP. For the clinical data we found that PANSS, SAS, and AIMS were not correlated to any of the sEMG parameters. Conclusion: with nonlinear parameters of sEMG it is possible to reveal NIP in SZ patients, and it may help to discriminate between different clinical groups of SZ patients. Combined typical and atypical AP therapy has stronger effect on sEMG than a therapy with AP of only one class. PMID:26217236

A real-time, practical sensor fault-tolerant module for robust EMG pattern recognition.

PubMed

Zhang, Xiaorong; Huang, He

2015-02-19

Unreliability of surface EMG recordings over time is a challenge for applying the EMG pattern recognition (PR)-controlled prostheses in clinical practice. Our previous study proposed a sensor fault-tolerant module (SFTM) by utilizing redundant information in multiple EMG signals. The SFTM consists of multiple sensor fault detectors and a self-recovery mechanism that can identify anomaly in EMG signals and remove the recordings of the disturbed signals from the input of the pattern classifier to recover the PR performance. While the proposed SFTM has shown great promise, the previous design is impractical. A practical SFTM has to be fast enough, lightweight, automatic, and robust under different conditions with or without disturbances. This paper presented a real-time, practical SFTM towards robust EMG PR. A novel fast LDA retraining algorithm and a fully automatic sensor fault detector based on outlier detection were developed, which allowed the SFTM to promptly detect disturbances and recover the PR performance immediately. These components of SFTM were then integrated with the EMG PR module and tested on five able-bodied subjects and a transradial amputee in real-time for classifying multiple hand and wrist motions under different conditions with different disturbance types and levels. The proposed fast LDA retraining algorithm significantly shortened the retraining time from nearly 1 s to less than 4 ms when tested on the embedded system prototype, which demonstrated the feasibility of a nearly "zero-delay" SFTM that is imperceptible to the users. The results of the real-time tests suggested that the SFTM was able to handle different types of disturbances investigated in this study and significantly improve the classification performance when one or multiple EMG signals were disturbed. In addition, the SFTM could also maintain the system's classification performance when there was no disturbance. This paper presented a real-time, lightweight, and automatic

EMG-Torque correction on Human Upper extremity using Evolutionary Computation

NASA Astrophysics Data System (ADS)

JL, Veronica; Parasuraman, S.; Khan, M. K. A. Ahamed; Jeba DSingh, Kingsly

2016-09-01

There have been many studies indicating that control system of rehabilitative robot plays an important role in determining the outcome of the therapy process. Existing works have done the prediction of feedback signal in the controller based on the kinematics parameters and EMG readings of upper limb's skeletal system. Kinematics and kinetics based control signal system is developed by reading the output of the sensors such as position sensor, orientation sensor and F/T (Force/Torque) sensor and there readings are to be compared with the preceding measurement to decide on the amount of assistive force. There are also other works that incorporated the kinematics parameters to calculate the kinetics parameters via formulation and pre-defined assumptions. Nevertheless, these types of control signals analyze the movement of the upper limb only based on the movement of the upper joints. They do not anticipate the possibility of muscle plasticity. The focus of the paper is to make use of the kinematics parameters and EMG readings of skeletal system to predict the individual torque of upper extremity's joints. The surface EMG signals are fed into different mathematical models so that these data can be trained through Genetic Algorithm (GA) to find the best correlation between EMG signals and torques acting on the upper limb's joints. The estimated torque attained from the mathematical models is called simulated output. The simulated output will then be compared with the actual individual joint which is calculated based on the real time kinematics parameters of the upper movement of the skeleton when the muscle cells are activated. The findings from this contribution are extended into the development of the active control signal based controller for rehabilitation robot.

Real time estimation of generation, extinction and flow of muscle fibre action potentials in high density surface EMG.

PubMed

Mesin, Luca

2015-02-01

Developing a real time method to estimate generation, extinction and propagation of muscle fibre action potentials from bi-dimensional and high density surface electromyogram (EMG). A multi-frame generalization of an optical flow technique including a source term is considered. A model describing generation, extinction and propagation of action potentials is fit to epochs of surface EMG. The algorithm is tested on simulations of high density surface EMG (inter-electrode distance equal to 5mm) from finite length fibres generated using a multi-layer volume conductor model. The flow and source term estimated from interference EMG reflect the anatomy of the muscle, i.e. the direction of the fibres (2° of average estimation error) and the positions of innervation zone and tendons under the electrode grid (mean errors of about 1 and 2mm, respectively). The global conduction velocity of the action potentials from motor units under the detection system is also obtained from the estimated flow. The processing time is about 1 ms per channel for an epoch of EMG of duration 150 ms. A new real time image processing algorithm is proposed to investigate muscle anatomy and activity. Potential applications are proposed in prosthesis control, automatic detection of optimal channels for EMG index extraction and biofeedback. Copyright © 2014 Elsevier Ltd. All rights reserved.

An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection.

PubMed

Ergeneci, Mert; Gokcesu, Kaan; Ertan, Erhan; Kosmas, Panagiotis

2018-02-01

Wearable technology has gained increasing popularity in the applications of healthcare, sports science, and biomedical engineering in recent years. Because of its convenient nature, the wearable technology is particularly useful in the acquisition of the physiological signals. Specifically, the (surface electromyography) sEMG systems, which measure the muscle activation potentials, greatly benefit from this technology in both clinical and industrial applications. However, the current wearable sEMG systems have several drawbacks including inefficient noise cancellation, insufficient measurement quality, and difficult integration to customized applications. Additionally, none of these sEMG data acquisition systems can detect sEMG signals (i.e., contractions), which provides a valuable environment for further studies such as human machine interaction, gesture recognition, and fatigue tracking. To this end, we introduce an embedded, eight channel, noise canceling, wireless, wearable sEMG data acquisition system with adaptive muscle contraction detection. Our design consists of two stages, which are the sEMG sensors and the multichannel data acquisition unit. For the first stage, we propose a low cost, dry, and active sEMG sensor that captures the muscle activation potentials, a data acquisition unit that evaluates these captured multichannel sEMG signals and transmits them to a user interface. In the data acquisition unit, the sEMG signals are processed through embedded, adaptive methods in order to reject the power line noise and detect the muscle contractions. Through extensive experiments, we demonstrate that our sEMG sensor outperforms a widely used commercially available product and our data acquisition system achieves 4.583 dB SNR gain with accuracy in the detection of the contractions.

Muscle-tendon units localization and activation level analysis based on high-density surface EMG array and NMF algorithm

NASA Astrophysics Data System (ADS)

Huang, Chengjun; Chen, Xiang; Cao, Shuai; Zhang, Xu

2016-12-01

Objective. Some skeletal muscles can be subdivided into smaller segments called muscle-tendon units (MTUs). The purpose of this paper is to propose a framework to locate the active region of the corresponding MTUs within a single skeletal muscle and to analyze the activation level varieties of different MTUs during a dynamic motion task. Approach. Biceps brachii and gastrocnemius were selected as targeted muscles and three dynamic motion tasks were designed and studied. Eight healthy male subjects participated in the data collection experiments, and 128-channel surface electromyographic (sEMG) signals were collected with a high-density sEMG electrode grid (a grid consists of 8 rows and 16 columns). Then the sEMG envelopes matrix was factorized into a matrix of weighting vectors and a matrix of time-varying coefficients by nonnegative matrix factorization algorithm. Main results. The experimental results demonstrated that the weightings vectors, which represent invariant pattern of muscle activity across all channels, could be used to estimate the location of MTUs and the time-varying coefficients could be used to depict the variation of MTUs activation level during dynamic motion task. Significance. The proposed method provides one way to analyze in-depth the functional state of MTUs during dynamic tasks and thus can be employed on multiple noteworthy sEMG-based applications such as muscle force estimation, muscle fatigue research and the control of myoelectric prostheses. This work was supported by the National Nature Science Foundation of China under Grant 61431017 and 61271138.

Comparison of algorithms to quantify muscle fatigue in upper limb muscles based on sEMG signals.

PubMed

Kahl, Lorenz; Hofmann, Ulrich G

2016-11-01

This work compared the performance of six different fatigue detection algorithms quantifying muscle fatigue based on electromyographic signals. Surface electromyography (sEMG) was obtained by an experiment from upper arm contractions at three different load levels from twelve volunteers. Fatigue detection algorithms mean frequency (MNF), spectral moments ratio (SMR), the wavelet method WIRM1551, sample entropy (SampEn), fuzzy approximate entropy (fApEn) and recurrence quantification analysis (RQA%DET) were calculated. The resulting fatigue signals were compared considering the disturbances incorporated in fatiguing situations as well as according to the possibility to differentiate the load levels based on the fatigue signals. Furthermore we investigated the influence of the electrode locations on the fatigue detection quality and whether an optimized channel set is reasonable. The results of the MNF, SMR, WIRM1551 and fApEn algorithms fell close together. Due to the small amount of subjects in this study significant differences could not be found. In terms of disturbances the SMR algorithm showed a slight tendency to out-perform the others. Copyright © 2016 IPEM. Published by Elsevier Ltd. All rights reserved.

A two-dimensional matrix image based feature extraction method for classification of sEMG: A comparative analysis based on SVM, KNN and RBF-NN.

PubMed

Wen, Tingxi; Zhang, Zhongnan; Qiu, Ming; Zeng, Ming; Luo, Weizhen

2017-01-01

The computer mouse is an important human-computer interaction device. But patients with physical finger disability are unable to operate this device. Surface EMG (sEMG) can be monitored by electrodes on the skin surface and is a reflection of the neuromuscular activities. Therefore, we can control limbs auxiliary equipment by utilizing sEMG classification in order to help the physically disabled patients to operate the mouse. To develop a new a method to extract sEMG generated by finger motion and apply novel features to classify sEMG. A window-based data acquisition method was presented to extract signal samples from sEMG electordes. Afterwards, a two-dimensional matrix image based feature extraction method, which differs from the classical methods based on time domain or frequency domain, was employed to transform signal samples to feature maps used for classification. In the experiments, sEMG data samples produced by the index and middle fingers at the click of a mouse button were separately acquired. Then, characteristics of the samples were analyzed to generate a feature map for each sample. Finally, the machine learning classification algorithms (SVM, KNN, RBF-NN) were employed to classify these feature maps on a GPU. The study demonstrated that all classifiers can identify and classify sEMG samples effectively. In particular, the accuracy of the SVM classifier reached up to 100%. The signal separation method is a convenient, efficient and quick method, which can effectively extract the sEMG samples produced by fingers. In addition, unlike the classical methods, the new method enables to extract features by enlarging sample signals' energy appropriately. The classical machine learning classifiers all performed well by using these features.

The extraction of neural information from the surface EMG for the control of upper-limb prostheses: emerging avenues and challenges.

PubMed

Farina, Dario; Jiang, Ning; Rehbaum, Hubertus; Holobar, Aleš; Graimann, Bernhard; Dietl, Hans; Aszmann, Oskar C

2014-07-01

Despite not recording directly from neural cells, the surface electromyogram (EMG) signal contains information on the neural drive to muscles, i.e., the spike trains of motor neurons. Using this property, myoelectric control consists of the recording of EMG signals for extracting control signals to command external devices, such as hand prostheses. In commercial control systems, the intensity of muscle activity is extracted from the EMG and used for single degrees of freedom activation (direct control). Over the past 60 years, academic research has progressed to more sophisticated approaches but, surprisingly, none of these academic achievements has been implemented in commercial systems so far. We provide an overview of both commercial and academic myoelectric control systems and we analyze their performance with respect to the characteristics of the ideal myocontroller. Classic and relatively novel academic methods are described, including techniques for simultaneous and proportional control of multiple degrees of freedom and the use of individual motor neuron spike trains for direct control. The conclusion is that the gap between industry and academia is due to the relatively small functional improvement in daily situations that academic systems offer, despite the promising laboratory results, at the expense of a substantial reduction in robustness. None of the systems so far proposed in the literature fulfills all the important criteria needed for widespread acceptance by the patients, i.e. intuitive, closed-loop, adaptive, and robust real-time ( 200 ms delay) control, minimal number of recording electrodes with low sensitivity to repositioning, minimal training, limited complexity and low consumption. Nonetheless, in recent years, important efforts have been invested in matching these criteria, with relevant steps forwards.

A masked least-squares smoothing procedure for artifact reduction in scanning-EMG recordings.

PubMed

Corera, Íñigo; Eciolaza, Adrián; Rubio, Oliver; Malanda, Armando; Rodríguez-Falces, Javier; Navallas, Javier

2018-01-11

Scanning-EMG is an electrophysiological technique in which the electrical activity of the motor unit is recorded at multiple points along a corridor crossing the motor unit territory. Correct analysis of the scanning-EMG signal requires prior elimination of interference from nearby motor units. Although the traditional processing based on the median filtering is effective in removing such interference, it distorts the physiological waveform of the scanning-EMG signal. In this study, we describe a new scanning-EMG signal processing algorithm that preserves the physiological signal waveform while effectively removing interference from other motor units. To obtain a cleaned-up version of the scanning signal, the masked least-squares smoothing (MLSS) algorithm recalculates and replaces each sample value of the signal using a least-squares smoothing in the spatial dimension, taking into account the information of only those samples that are not contaminated with activity of other motor units. The performance of the new algorithm with simulated scanning-EMG signals is studied and compared with the performance of the median algorithm and tested with real scanning signals. Results show that the MLSS algorithm distorts the waveform of the scanning-EMG signal much less than the median algorithm (approximately 3.5 dB gain), being at the same time very effective at removing interference components. Graphical Abstract The raw scanning-EMG signal (left figure) is processed by the MLSS algorithm in order to remove the artifact interference. Firstly, artifacts are detected from the raw signal, obtaining a validity mask (central figure) that determines the samples that have been contaminated by artifacts. Secondly, a least-squares smoothing procedure in the spatial dimension is applied to the raw signal using the not contaminated samples according to the validity mask. The resulting MLSS-processed scanning-EMG signal (right figure) is clean of artifact interference.

Design, development and testing of a low-cost sEMG system and its use in recording muscle activity in human gait.

PubMed

Supuk, Tamara Grujic; Skelin, Ana Kuzmanic; Cic, Maja

2014-05-07

Surface electromyography (sEMG) is an important measurement technique used in biomechanical, rehabilitation and sport environments. In this article the design, development and testing of a low-cost wearable sEMG system are described. The hardware architecture consists of a two-cascade small-sized bioamplifier with a total gain of 2,000 and band-pass of 3 to 500 Hz. The sampling frequency of the system is 1,000 Hz. Since real measured EMG signals are usually corrupted by various types of noises (motion artifacts, white noise and electromagnetic noise present at 50 Hz and higher harmonics), we have tested several denoising techniques, both on artificial and measured EMG signals. Results showed that a wavelet-based technique implementing Daubechies5 wavelet and soft sqtwolog thresholding is the most appropriate for EMG signals denoising. To test the system performance, EMG activities of six dominant muscles of ten healthy subjects during gait were measured (gluteus maximus, biceps femoris, sartorius, rectus femoris, tibialis anterior and medial gastrocnemius). The obtained EMG envelopes presented against the duration of gait cycle were compared favourably with the EMG data available in the literature, suggesting that the proposed system is suitable for a wide range of applications in biomechanics.

Design, Development and Testing of a Low-Cost sEMG System and Its Use in Recording Muscle Activity in Human Gait

PubMed Central

Supuk, Tamara Grujic; Skelin, Ana Kuzmanic; Cic, Maja

2014-01-01

Surface electromyography (sEMG) is an important measurement technique used in biomechanical, rehabilitation and sport environments. In this article the design, development and testing of a low-cost wearable sEMG system are described. The hardware architecture consists of a two-cascade small-sized bioamplifier with a total gain of 2,000 and band-pass of 3 to 500 Hz. The sampling frequency of the system is 1,000 Hz. Since real measured EMG signals are usually corrupted by various types of noises (motion artifacts, white noise and electromagnetic noise present at 50 Hz and higher harmonics), we have tested several denoising techniques, both on artificial and measured EMG signals. Results showed that a wavelet—based technique implementing Daubechies5 wavelet and soft sqtwolog thresholding is the most appropriate for EMG signals denoising. To test the system performance, EMG activities of six dominant muscles of ten healthy subjects during gait were measured (gluteus maximus, biceps femoris, sartorius, rectus femoris, tibialis anterior and medial gastrocnemius). The obtained EMG envelopes presented against the duration of gait cycle were compared favourably with the EMG data available in the literature, suggesting that the proposed system is suitable for a wide range of applications in biomechanics. PMID:24811078

Prosthetic EMG control enhancement through the application of man-machine principles

NASA Technical Reports Server (NTRS)

Simcox, W. A.

1977-01-01

An area in medicine that appears suitable to man-machine principles is rehabilitation research, particularly when the motor aspects of the body are involved. If one considers the limb, whether functional or not, as the machine, the brain as the controller and the neuromuscular system as the man-machine interface, the human body is reduced to a man-machine system that can benefit from the principles behind such systems. The area of rehabilitation that this paper deals with is that of an arm amputee and his prosthetic device. Reducing this area to its man-machine basics, the problem becomes one of attaining natural multiaxis prosthetic control using Electromyographic activity (EMG) as the means of communication between man and prothesis. In order to use EMG as the communication channel it must be amplified and processed to yield a high information signal suitable for control. The most common processing scheme employed is termed Mean Value Processing. This technique for extracting the useful EMG signal consists of a differential to single ended conversion to the surface activity followed by a rectification and smoothing.

Adaptive neuron-to-EMG decoder training for FES neuroprostheses

NASA Astrophysics Data System (ADS)

Ethier, Christian; Acuna, Daniel; Solla, Sara A.; Miller, Lee E.

2016-08-01

Objective. We have previously demonstrated a brain-machine interface neuroprosthetic system that provided continuous control of functional electrical stimulation (FES) and restoration of grasp in a primate model of spinal cord injury (SCI). Predicting intended EMG directly from cortical recordings provides a flexible high-dimensional control signal for FES. However, no peripheral signal such as force or EMG is available for training EMG decoders in paralyzed individuals. Approach. Here we present a method for training an EMG decoder in the absence of muscle activity recordings; the decoder relies on mapping behaviorally relevant cortical activity to the inferred EMG activity underlying an intended action. Monkeys were trained at a 2D isometric wrist force task to control a computer cursor by applying force in the flexion, extension, ulnar, and radial directions and execute a center-out task. We used a generic muscle force-to-endpoint force model based on muscle pulling directions to relate each target force to an optimal EMG pattern that attained the target force while minimizing overall muscle activity. We trained EMG decoders during the target hold periods using a gradient descent algorithm that compared EMG predictions to optimal EMG patterns. Main results. We tested this method both offline and online. We quantified both the accuracy of offline force predictions and the ability of a monkey to use these real-time force predictions for closed-loop cursor control. We compared both offline and online results to those obtained with several other direct force decoders, including an optimal decoder computed from concurrently measured neural and force signals. Significance. This novel approach to training an adaptive EMG decoder could make a brain-control FES neuroprosthesis an effective tool to restore the hand function of paralyzed individuals. Clinical implementation would make use of individualized EMG-to-force models. Broad generalization could be achieved by

Computational Intelligence Based Data Fusion Algorithm for Dynamic sEMG and Skeletal Muscle Force Modelling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chandrasekhar Potluri,; Madhavi Anugolu; Marco P. Schoen

2013-08-01

In this work, an array of three surface Electrography (sEMG) sensors are used to acquired muscle extension and contraction signals for 18 healthy test subjects. The skeletal muscle force is estimated using the acquired sEMG signals and a Non-linear Wiener Hammerstein model, relating the two signals in a dynamic fashion. The model is obtained from using System Identification (SI) algorithm. The obtained force models for each sensor are fused using a proposed fuzzy logic concept with the intent to improve the force estimation accuracy and resilience to sensor failure or misalignment. For the fuzzy logic inference system, the sEMG entropy,more » the relative error, and the correlation of the force signals are considered for defining the membership functions. The proposed fusion algorithm yields an average of 92.49% correlation between the actual force and the overall estimated force output. In addition, the proposed fusionbased approach is implemented on a test platform. Experiments indicate an improvement in finger/hand force estimation.« less

Measurement of EMG activity with textile electrodes embedded into clothing.

PubMed

Finni, T; Hu, M; Kettunen, P; Vilavuo, T; Cheng, S

2007-11-01

Novel textile electrodes that can be embedded into sports clothing to measure averaged rectified electromyography (EMG) have been developed for easy use in field tests and in clinical settings. The purpose of this study was to evaluate the validity, reliability and feasibility of this new product to measure averaged rectified EMG. The validity was tested by comparing the signals from bipolar textile electrodes (42 cm(2)) and traditional bipolar surface electrodes (1.32 cm(2)) during bilateral isometric knee extension exercise with two electrode locations (A: both electrodes located in the same place, B: traditional electrodes placed on the individual muscles according to SENIAM, n=10 persons for each). Within-session repeatability (the coefficient of variation CV%, n=10) was calculated from five repetitions of 60% maximum voluntary contraction (MVC). The day-to-day repeatability (n=8) was assessed by measuring three different isometric force levels on five consecutive days. The feasibility of the textile electrodes in field conditions was assessed during a maximal treadmill test (n=28). Bland-Altman plots showed a good agreement within 2SD between the textile and traditional electrodes, demonstrating that the textile electrodes provide similar information on the EMG signal amplitude to the traditional electrodes. The within-session CV ranged from 13% to 21% in both the textile and traditional electrodes. The day-to-day CV was smaller, ranging from 4% to 11% for the textile electrodes. A similar relationship (r(2)=0.5) was found between muscle strength and the EMG of traditional and textile electrodes. The feasibility study showed that the textile electrode technique can potentially make EMG measurements very easy in field conditions. This study indicates that textile electrodes embedded into shorts is a valid and feasible method for assessing the average rectified value of EMG.

Epoch length to accurately estimate the amplitude of interference EMG is likely the result of unavoidable amplitude cancellation

PubMed Central

Keenan, Kevin G.; Valero-Cuevas, Francisco J.

2008-01-01

Researchers and clinicians routinely rely on interference electromyograms (EMGs) to estimate muscle forces and command signals in the neuromuscular system (e.g., amplitude, timing, and frequency content). The amplitude cancellation intrinsic to interference EMG, however, raises important questions about how to optimize these estimates. For example, what should the length of the epoch (time window) be to average an EMG signal to reliably estimate muscle forces and command signals? Shorter epochs are most practical, and significant reductions in epoch have been reported with high-pass filtering and whitening. Given that this processing attenuates power at frequencies of interest (< 250 Hz), however, it is unclear how it improves the extraction of physiologically-relevant information. We examined the influence of amplitude cancellation and high-pass filtering on the epoch necessary to accurately estimate the “true” average EMG amplitude calculated from a 28 s EMG trace (EMGref) during simulated constant isometric conditions. Monte Carlo iterations of a motor-unit model simulating 28 s of surface EMG produced 245 simulations under 2 conditions: with and without amplitude cancellation. For each simulation, we calculated the epoch necessary to generate average full-wave rectified EMG amplitudes that settled within 5% of EMGref. For the no-cancellation EMG, the necessary epochs were short (e.g., < 100 ms). For the more realistic interference EMG (i.e., cancellation condition), epochs shortened dramatically after using high-pass filter cutoffs above 250 Hz, producing epochs short enough to be practical (i.e., < 500 ms). We conclude that the need to use long epochs to accurately estimate EMG amplitude is likely the result of unavoidable amplitude cancellation, which helps to clarify why high-pass filtering (> 250 Hz) improves EMG estimates. PMID:19081815

sEMG Sensor Using Polypyrrole-Coated Nonwoven Fabric Sheet for Practical Control of Prosthetic Hand

PubMed Central

Jiang, Yinlai; Togane, Masami; Lu, Baoliang; Yokoi, Hiroshi

2017-01-01

One of the greatest challenges of using a myoelectric prosthetic hand in daily life is to conveniently measure stable myoelectric signals. This study proposes a novel surface electromyography (sEMG) sensor using polypyrrole-coated nonwoven fabric sheet as electrodes (PPy electrodes) to allow people with disabilities to control prosthetic limbs. The PPy electrodes are sewn on an elastic band to guarantee close contact with the skin and thus reduce the contact electrical impedance between the electrodes and the skin. The sensor is highly customizable to fit the size and the shape of the stump so that people with disabilities can attach the sensor by themselves. The performance of the proposed sensor was investigated experimentally by comparing measurements of Ag/AgCl electrodes with electrolytic gel and the sEMG from the same muscle fibers. The high correlation coefficient (0.87) between the two types of sensors suggests the effectiveness of the proposed sensor. Another experiment of sEMG pattern recognition to control myoelectric prosthetic hands showed that the PPy electrodes are as effective as Ag/AgCl electrodes for measuring sEMG signals for practical myoelectric control. We also investigated the relation between the myoelectric signals' signal-to-noise ratio and the source impedances by simultaneously measuring the source impedances and the myoelectric signals with a switching circuit. The results showed that differences in both the norm and the phase of the source impedance greatly affect the common mode noise in the signal. PMID:28220058

A threshold-based approach for muscle contraction detection from surface EMG signals

NASA Astrophysics Data System (ADS)

Morantes, Gaudi; Fernández, Gerardo; Altuve, Miguel

2013-11-01

Surface electromyographic (SEMG) signals are commonly used as control signals in prosthetic and orthotic devices. Super cial electrodes are placed on the skin of the subject to acquire its muscular activity through this signal. The muscle contraction episode is then in charge of activating and deactivating these devices. Nevertheless, there is no gold standard" to detect muscle contraction, leading to delayed responses and false and missed detections. This fact motivated us to propose a new approach that compares a smoothed version of the SEMG signal with a xed threshold, in order to detect muscle contraction episodes. After preprocessing the SEMG signal, the smoothed version is obtained using a moving average lter, where three di erent window lengths has been evaluated. The detector was tuned by maximizing sensitivity and speci city and evaluated using SEMG signals obtained from the anterior tibial and gastrocnemius muscles, taken during the walking of ve subjects. Compared with traditional detection methods, we obtain a reduction of 3 ms in the detection delay, an increase of 8% in sensitivity but a decrease of 15% in speci city. Future work is directed to the inclusion of a temporal threshold (a double-threshold approach) to minimize false detections and reduce detection delays.

Finite State Machine with Adaptive Electromyogram (EMG) Feature Extraction to Drive Meal Assistance Robot

NASA Astrophysics Data System (ADS)

Zhang, Xiu; Wang, Xingyu; Wang, Bei; Sugi, Takenao; Nakamura, Masatoshi

Surface electromyogram (EMG) from elbow, wrist and hand has been widely used as an input of multifunction prostheses for many years. However, for patients with high-level limb deficiencies, muscle activities in upper-limbs are not strong enough to be used as control signals. In this paper, EMG from lower-limbs is acquired and applied to drive a meal assistance robot. An onset detection method with adaptive threshold based on EMG power is proposed to recognize different muscle contractions. Predefined control commands are output by finite state machine (FSM), and applied to operate the robot. The performance of EMG control is compared with joystick control by both objective and subjective indices. The results show that FSM provides the user with an easy-performing control strategy, which successfully operates robots with complicated control commands by limited muscle motions. The high accuracy and comfortableness of the EMG-control meal assistance robot make it feasible for users with upper limbs motor disabilities.

Development of new muscle contraction sensor to replace sEMG for using in muscles analysis fields.

PubMed

Zhang, D; Matsuoka, Y; Kong, W; Imtiaz, U; Bartolomeo, L; Cosentino, S; Zecca, M; Sessa, S; Ishii, H; Takanishi, A

2014-01-01

Nowadays, the technologies for detecting, processing and interpreting bioelectrical signals have improved tremendously. In particular, surface electromyography (sEMG) has gained momentum in a wide range of applications in various fields. However, sEMG sensing has several shortcomings, the most important being: measurements are heavily sensible to individual differences, sensors are difficult to position and very expensive. In this paper, the authors will present an innovative muscle contraction sensing device (MC sensor), aiming to replace sEMG sensing in the field of muscle movement analysis. Compared with sEMG, this sensor is easier to position, setup and use, less dependent from individual differences, and less expensive. Preliminary experiments, described in this paper, confirm that MC sensing is suitable for muscle contraction analysis, and compare the results of sEMG and MC sensor for the measurement of forearm muscle contraction.

Accuracy assessment of a surface electromyogram decomposition system in human first dorsal interosseus muscle

NASA Astrophysics Data System (ADS)

Hu, Xiaogang; Rymer, William Z.; Suresh, Nina L.

2014-04-01

Objective. The aim of this study is to assess the accuracy of a surface electromyogram (sEMG) motor unit (MU) decomposition algorithm during low levels of muscle contraction. Approach. A two-source method was used to verify the accuracy of the sEMG decomposition system, by utilizing simultaneous intramuscular and surface EMG recordings from the human first dorsal interosseous muscle recorded during isometric trapezoidal force contractions. Spike trains from each recording type were decomposed independently utilizing two different algorithms, EMGlab and dEMG decomposition algorithms. The degree of agreement of the decomposed spike timings was assessed for three different segments of the EMG signals, corresponding to specified regions in the force task. A regression analysis was performed to examine whether certain properties of the sEMG and force signal can predict the decomposition accuracy. Main results. The average accuracy of successful decomposition among the 119 MUs that were common to both intramuscular and surface records was approximately 95%, and the accuracy was comparable between the different segments of the sEMG signals (i.e., force ramp-up versus steady state force versus combined). The regression function between the accuracy and properties of sEMG and force signals revealed that the signal-to-noise ratio of the action potential and stability in the action potential records were significant predictors of the surface decomposition accuracy. Significance. The outcomes of our study confirm the accuracy of the sEMG decomposition algorithm during low muscle contraction levels and provide confidence in the overall validity of the surface dEMG decomposition algorithm.

Vastus lateralis surface and single motor unit EMG following submaximal shortening and lengthening contractions.

PubMed

Altenburg, Teatske M; de Ruiter, Cornelis J; Verdijk, Peter W L; van Mechelen, Willem; de Haan, Arnold

2008-12-01

A single shortening contraction reduces the force capacity of muscle fibers, whereas force capacity is enhanced following lengthening. However, how motor unit recruitment and discharge rate (muscle activation) are adapted to such changes in force capacity during submaximal contractions remains unknown. Additionally, there is limited evidence for force enhancement in larger muscles. We therefore investigated lengthening- and shortening-induced changes in activation of the knee extensors. We hypothesized that when the same submaximal torque had to be generated following shortening, muscle activation had to be increased, whereas a lower activation would suffice to produce the same torque following lengthening. Muscle activation following shortening and lengthening (20 degrees at 10 degrees /s) was determined using rectified surface electromyography (rsEMG) in a 1st session (at 10% and 50% maximal voluntary contraction (MVC)) and additionally with EMG of 42 vastus lateralis motor units recorded in a 2nd session (at 4%-47%MVC). rsEMG and motor unit discharge rates following shortening and lengthening were normalized to isometric reference contractions. As expected, normalized rsEMG (1.15 +/- 0.19) and discharge rate (1.11 +/- 0.09) were higher following shortening (p < 0.05). Following lengthening, normalized rsEMG (0.91 +/- 0.10) was, as expected, lower than 1.0 (p < 0.05), but normalized discharge rate (0.99 +/- 0.08) was not (p > 0.05). Thus, muscle activation was increased to compensate for a reduced force capacity following shortening by increasing the discharge rate of the active motor units (rate coding). In contrast, following lengthening, rsEMG decreased while the discharge rates of active motor units remained similar, suggesting that derecruitment of units might have occurred.

An isometric muscle force estimation framework based on a high-density surface EMG array and an NMF algorithm

NASA Astrophysics Data System (ADS)

Huang, Chengjun; Chen, Xiang; Cao, Shuai; Qiu, Bensheng; Zhang, Xu

2017-08-01

Objective. To realize accurate muscle force estimation, a novel framework is proposed in this paper which can extract the input of the prediction model from the appropriate activation area of the skeletal muscle. Approach. Surface electromyographic (sEMG) signals from the biceps brachii muscle during isometric elbow flexion were collected with a high-density (HD) electrode grid (128 channels) and the external force at three contraction levels was measured at the wrist synchronously. The sEMG envelope matrix was factorized into a matrix of basis vectors with each column representing an activation pattern and a matrix of time-varying coefficients by a nonnegative matrix factorization (NMF) algorithm. The activation pattern with the highest activation intensity, which was defined as the sum of the absolute values of the time-varying coefficient curve, was considered as the major activation pattern, and its channels with high weighting factors were selected to extract the input activation signal of a force estimation model based on the polynomial fitting technique. Main results. Compared with conventional methods using the whole channels of the grid, the proposed method could significantly improve the quality of force estimation and reduce the electrode number. Significance. The proposed method provides a way to find proper electrode placement for force estimation, which can be further employed in muscle heterogeneity analysis, myoelectric prostheses and the control of exoskeleton devices.

Comparative muscle study fatigue with sEMG signals during the isotonic and isometric tasks for diagnostics purposes.

PubMed

Sarmiento, Jhon F; Benevides, Alessandro B; Moreira, Marcelo H; Elias, Arlindo; Bastos, Teodiano F; Silva, Ian V; Pelegrina, Claudinei C

2011-01-01

The study of fatigue is an important tool for diagnostics of disease, sports, ergonomics and robotics areas. This work deals with the analysis of sEMG most important fatigue muscle indicators with use of signal processing in isometric and isotonic tasks with the propose of standardizing fatigue protocol to select the data acquisition and processing with diagnostic proposes. As a result, the slope of the RMS, ARV and MNF indicators were successful to describe the fatigue behavior expected. Whereas that, MDF and AIF indicators failed in the description of fatigue. Similarly, the use of a constant load for sEMG data acquisition was the best strategy in both tasks.

Surface EMG and intra-socket force measurement to control a prosthetic device

NASA Astrophysics Data System (ADS)

Sanford, Joe; Patterson, Rita; Popa, Dan

2015-06-01

Surface electromyography (SEMG) has been shown to be a robust and reliable interaction method allowing for basic control of powered prosthetic devices. Research has shown a marked decrease in EMG-classification efficiency throughout activities of daily life due to socket shift and movement and fatigue as well as changes in degree of fit of the socket throughout the subject's lifetime. Users with the most severe levels of amputation require the most complex devices with the greatest number of degrees of freedom. Controlling complex dexterous devices with limited available inputs requires the addition of sensing and interaction modalities. However, the larger the amputation severity, the fewer viable SEMG sites are available as control inputs. Previous work reported the use of intra-socket pressure, as measured during wrist flexion and extension, and has shown that it is possible to control a powered prosthetic device with pressure sensors. In this paper, we present data correlations of SEMG data with intra-socket pressure data. Surface EMG sensors and force sensors were housed within a simulated prosthetic cuff fit to a healthy-limbed subject. EMG and intra-socket force data was collected from inside the cuff as a subject performed pre-defined grip motions with their dominant hand. Data fusion algorithms were explored and allowed a subject to use both intra-socket pressure and SEMG data as control inputs for a powered prosthetic device. This additional input modality allows for an improvement in input classification as well as information regarding socket fit through out activities of daily life.

Simulation of facial expressions using person-specific sEMG signals controlling a biomechanical face model.

PubMed

Eskes, Merijn; Balm, Alfons J M; van Alphen, Maarten J A; Smeele, Ludi E; Stavness, Ian; van der Heijden, Ferdinand

2018-01-01

Functional inoperability in advanced oral cancer is difficult to assess preoperatively. To assess functions of lips and tongue, biomechanical models are required. Apart from adjusting generic models to individual anatomy, muscle activation patterns (MAPs) driving patient-specific functional movements are necessary to predict remaining functional outcome. We aim to evaluate how volunteer-specific MAPs derived from surface electromyographic (sEMG) signals control a biomechanical face model. Muscle activity of seven facial muscles in six volunteers was measured bilaterally with sEMG. A triple camera set-up recorded 3D lip movement. The generic face model in ArtiSynth was adapted to our needs. We controlled the model using the volunteer-specific MAPs. Three activation strategies were tested: activating all muscles [Formula: see text], selecting the three muscles showing highest muscle activity bilaterally [Formula: see text]-this was calculated by taking the mean of left and right muscles and then selecting the three with highest variance-and activating the muscles considered most relevant per instruction [Formula: see text], bilaterally. The model's lip movement was compared to the actual lip movement performed by the volunteers, using 3D correlation coefficients [Formula: see text]. The correlation coefficient between simulations and measurements with [Formula: see text] resulted in a median [Formula: see text] of 0.77. [Formula: see text] had a median [Formula: see text] of 0.78, whereas with [Formula: see text] the median [Formula: see text] decreased to 0.45. We demonstrated that MAPs derived from noninvasive sEMG measurements can control movement of the lips in a generic finite element face model with a median [Formula: see text] of 0.78. Ultimately, this is important to show the patient-specific residual movement using the patient's own MAPs. When the required treatment tools and personalisation techniques for geometry and anatomy become available, this may

An evaluation of the utility and limitations of counting motor unit action potentials in the surface electromyogram

NASA Astrophysics Data System (ADS)

Zhou, Ping; Zev Rymer, William

2004-12-01

The number of motor unit action potentials (MUAPs) appearing in the surface electromyogram (EMG) signal is directly related to motor unit recruitment and firing rates and therefore offers potentially valuable information about the level of activation of the motoneuron pool. In this paper, based on morphological features of the surface MUAPs, we try to estimate the number of MUAPs present in the surface EMG by counting the negative peaks in the signal. Several signal processing procedures are applied to the surface EMG to facilitate this peak counting process. The MUAP number estimation performance by this approach is first illustrated using the surface EMG simulations. Then, by evaluating the peak counting results from the EMG records detected by a very selective surface electrode, at different contraction levels of the first dorsal interosseous (FDI) muscles, the utility and limitations of such direct peak counts for MUAP number estimation in surface EMG are further explored.

Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework.

PubMed

Baldacchino, Tara; Jacobs, William R; Anderson, Sean R; Worden, Keith; Rowson, Jennifer

2018-01-01

This contribution presents a novel methodology for myolectric-based control using surface electromyographic (sEMG) signals recorded during finger movements. A multivariate Bayesian mixture of experts (MoE) model is introduced which provides a powerful method for modeling force regression at the fingertips, while also performing finger movement classification as a by-product of the modeling algorithm. Bayesian inference of the model allows uncertainties to be naturally incorporated into the model structure. This method is tested using data from the publicly released NinaPro database which consists of sEMG recordings for 6 degree-of-freedom force activations for 40 intact subjects. The results demonstrate that the MoE model achieves similar performance compared to the benchmark set by the authors of NinaPro for finger force regression. Additionally, inherent to the Bayesian framework is the inclusion of uncertainty in the model parameters, naturally providing confidence bounds on the force regression predictions. Furthermore, the integrated clustering step allows a detailed investigation into classification of the finger movements, without incurring any extra computational effort. Subsequently, a systematic approach to assessing the importance of the number of electrodes needed for accurate control is performed via sensitivity analysis techniques. A slight degradation in regression performance is observed for a reduced number of electrodes, while classification performance is unaffected.

Comparison of sEMG processing methods during whole-body vibration exercise.

PubMed

Lienhard, Karin; Cabasson, Aline; Meste, Olivier; Colson, Serge S

2015-12-01

The objective was to investigate the influence of surface electromyography (sEMG) processing methods on the quantification of muscle activity during whole-body vibration (WBV) exercises. sEMG activity was recorded while the participants performed squats on the platform with and without WBV. The spikes observed in the sEMG spectrum at the vibration frequency and its harmonics were deleted using state-of-the-art methods, i.e. (1) a band-stop filter, (2) a band-pass filter, and (3) spectral linear interpolation. The same filtering methods were applied on the sEMG during the no-vibration trial. The linear interpolation method showed the highest intraclass correlation coefficients (no vibration: 0.999, WBV: 0.757-0.979) with the comparison measure (unfiltered sEMG during the no-vibration trial), followed by the band-stop filter (no vibration: 0.929-0.975, WBV: 0.661-0.938). While both methods introduced a systematic bias (P < 0.001), the error increased with increasing mean values to a higher degree for the band-stop filter. After adjusting the sEMG(RMS) during WBV for the bias, the performance of the interpolation method and the band-stop filter was comparable. The band-pass filter was in poor agreement with the other methods (ICC: 0.207-0.697), unless the sEMG(RMS) was corrected for the bias (ICC ⩾ 0.931, %LOA ⩽ 32.3). In conclusion, spectral linear interpolation or a band-stop filter centered at the vibration frequency and its multiple harmonics should be applied to delete the artifacts in the sEMG signals during WBV. With the use of a band-stop filter it is recommended to correct the sEMG(RMS) for the bias as this procedure improved its performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

Oscillations in motor unit discharge are reflected in the low-frequency component of rectified surface EMG and the rate of change in force.

PubMed

Yoshitake, Yasuhide; Shinohara, Minoru

2013-11-01

Common drive to a motor unit (MU) pool manifests as low-frequency oscillations in MU discharge rate, producing fluctuations in muscle force. The aim of the study was to examine the temporal correlation between instantaneous MU discharge rate and rectified EMG in low frequencies. Additionally, we attempted to examine whether there is a temporal correlation between the low-frequency oscillations in MU discharge rate and the first derivative of force (dF/dt). Healthy young subjects produced steady submaximal force with their right finger as a single task or while maintaining a pinch-grip force with the left hand as a dual task. Surface EMG and fine-wire MU potentials were recorded from the first dorsal interosseous muscle in the right hand. Surface EMG was band-pass filtered (5-1,000 Hz) and full-wave rectified. Rectified surface EMG and the instantaneous discharge rate of MUs were smoothed by a Hann-window of 400 ms duration (equivalent to 2 Hz low-pass filtering). In each of the identified MUs, the smoothed MU discharge rate was positively correlated with the rectified-and-smoothed EMG as confirmed by the distinct peak in cross-correlation function with greater values in the dual task compared with the single task. Additionally, the smoothed MU discharge rate was temporally correlated with dF/dt more than with force and with rectified-and-smoothed EMG. The results indicated that the low-frequency component of rectified surface EMG and the first derivative of force provide temporal information on the low-frequency oscillations in the MU discharge rate.

The Analysis of Surface EMG Signals with the Wavelet-Based Correlation Dimension Method

PubMed Central

Zhang, Yanyan; Wang, Jue

2014-01-01

Many attempts have been made to effectively improve a prosthetic system controlled by the classification of surface electromyographic (SEMG) signals. Recently, the development of methodologies to extract the effective features still remains a primary challenge. Previous studies have demonstrated that the SEMG signals have nonlinear characteristics. In this study, by combining the nonlinear time series analysis and the time-frequency domain methods, we proposed the wavelet-based correlation dimension method to extract the effective features of SEMG signals. The SEMG signals were firstly analyzed by the wavelet transform and the correlation dimension was calculated to obtain the features of the SEMG signals. Then, these features were used as the input vectors of a Gustafson-Kessel clustering classifier to discriminate four types of forearm movements. Our results showed that there are four separate clusters corresponding to different forearm movements at the third resolution level and the resulting classification accuracy was 100%, when two channels of SEMG signals were used. This indicates that the proposed approach can provide important insight into the nonlinear characteristics and the time-frequency domain features of SEMG signals and is suitable for classifying different types of forearm movements. By comparing with other existing methods, the proposed method exhibited more robustness and higher classification accuracy. PMID:24868240

The effect of 630-nm light stimulation on the sEMG signal of forearm muscle

NASA Astrophysics Data System (ADS)

Yang, Dan D.; Hou, W. Sheng; Wu, Xiao Y.; Zheng, Xiao L.; Zheng, Jun; Jiang, Ying T.

2010-11-01

This study aimed to explore if the red light irradiation can affect the electrophysiology performance of flexor digitorum superficialis (FDS) and fatigue recovery. Four healthy volunteers were randomly divided into two groups. In the designed force-tracking tasks, all subjects performed the four fingertip isometric force production except thumb with a load of 30% of the maximum voluntary contraction (MVC) force until exhaustion. Subsequently, for the red light group, red light irradiation (640 nm wavelength, 0.23J/cm2, 20 min) was used on the right forearm; for the control group, the subjects relaxed without red light irradiation. Then subjects were required to perform fatigue trail again, and sEMG signal was collected simultaneously from FDS during finger force production. Average rectified value (ARV) and median frequency (MF) of sEMG were calculated. Compared to the control group, the red light irradiation induced more smoother value of ARV between 30% and 40%, and the value of MF was obviously large and smooth. The above electrophysiological markers indicated that recovery from muscle fatigue may be positively affected by the red light irradiation, suggesting that sEMG would become a power tool for exploring the effect of red light irradiation on local muscle fatigue.

EMG-based pattern recognition approach in post stroke robot-aided rehabilitation: a feasibility study

PubMed Central

2013-01-01

Background Several studies investigating the use of electromyographic (EMG) signals in robot-based stroke neuro-rehabilitation to enhance functional recovery. Here we explored whether a classical EMG-based patterns recognition approach could be employed to predict patients’ intentions while attempting to generate goal-directed movements in the horizontal plane. Methods Nine right-handed healthy subjects and seven right-handed stroke survivors performed reaching movements in the horizontal plane. EMG signals were recorded and used to identify the intended motion direction of the subjects. To this aim, a standard pattern recognition algorithm (i.e., Support Vector Machine, SVM) was used. Different tests were carried out to understand the role of the inter- and intra-subjects’ variability in affecting classifier accuracy. Abnormal muscular spatial patterns generating misclassification were evaluated by means of an assessment index calculated from the results achieved with the PCA, i.e., the so-called Coefficient of Expressiveness (CoE). Results Processing the EMG signals of the healthy subjects, in most of the cases we were able to build a static functional map of the EMG activation patterns for point-to-point reaching movements on the horizontal plane. On the contrary, when processing the EMG signals of the pathological subjects a good classification was not possible. In particular, patients’ aimed movement direction was not predictable with sufficient accuracy either when using the general map extracted from data of normal subjects and when tuning the classifier on the EMG signals recorded from each patient. Conclusions The experimental findings herein reported show that the use of EMG patterns recognition approach might not be practical to decode movement intention in subjects with neurological injury such as stroke. Rather than estimate motion from EMGs, future scenarios should encourage the utilization of these signals to detect and interpret the normal and

EMG based FES for post-stroke rehabilitation

NASA Astrophysics Data System (ADS)

Piyus, Ceethal K.; Anjaly Cherian, V.; Nageswaran, Sharmila

2017-11-01

Annually, 15 million in world population experiences stroke. Nearly 9 million stroke survivors every year experience mild to severe disability. The loss of upper extremity function in stroke survivors still remains a major rehabilitation challenge. The proposed EMG Abstract—Annually, 15 million in world population experiences stroke. Nearly 9 million stroke survivors every year experience mild to severe disability. The loss of upper extremity function in stroke survivors still remains a major rehabilitation challenge. The proposed EMG based FES system can be used for effective upper limb motor re-education in post stroke upper limb rehabilitation. The governing feature of the designed system is its synchronous activation, in which the FES stimulation is dependent on the amplitude of the EMG signal acquired from the unaffected upper limb muscle of the hemiplegic patient. This proportionate operation eliminates the undesirable damage to the patient’s skin by generating stimulus in proportion to voluntary EMG signals. This feature overcomes the disadvantages of currently available manual motor re-education systems. This model can be used in home-based post stroke rehabilitation, to effectively improve the upper limb functions.

Customized Interactive Robotic Treatment for Stroke: EMG-Triggered Therapy

PubMed Central

Dipietro, Laura; Ferraro, Mark; Palazzolo, Jerome Joseph; Krebs, Hermano Igo; Volpe, Bruce T.; Hogan, Neville

2009-01-01

A system for electromyographic (EMG) triggering of robot-assisted therapy (dubbed the EMG game) for stroke patients is presented. The onset of a patient’s attempt to move is detected by monitoring EMG in selected muscles, whereupon the robot assists her or him to perform point-to-point movements in a horizontal plane. Besides delivering customized robot-assisted therapy, the system can record signals that may be useful to better understand the process of recovery from stroke. Preliminary experiments aimed at testing the proposed system and gaining insight into the potential of EMG-triggered, robot-assisted therapy are reported. PMID:16200756

Filter design for cancellation of baseline-fluctuation in needle EMG recordings.

PubMed

Rodríguez-Carreño, I; Malanda-Trigueros, A; Gila-Useros, L; Navallas-Irujo, J; Rodríguez-Falces, J

2006-01-01

Appropriate cancellation of the baseline fluctuation (BLF) is an important issue when recording EMG signals as it may degrade signal quality and distort qualitative and quantitative analysis. We present a novel filter-design approach for automatic cancellation of the BLF based on several signal processing techniques used sequentially. The methodology is to estimate the spectral content of the BLF, and then to use this estimation to design a high-pass FIR filter that cancel the BLF present in the signal. Two merit figures are devised for measuring the degree of BLF present in an EMG record. These figures are used to compare our method with the conventional approach, which naively considers the baseline course to be of constant (without any fluctuation) potential shift. Applications of the technique on real and simulated EMG signals show the superior performance of our approach in terms of both visual inspection and the merit figures.

A frequency and pulse-width co-modulation strategy for transcutaneous neuromuscular electrical stimulation based on sEMG time-domain features

NASA Astrophysics Data System (ADS)

Zhou, Yu-Xuan; Wang, Hai-Peng; Bao, Xue-Liang; Lü, Xiao-Ying; Wang, Zhi-Gong

2016-02-01

Objective. Surface electromyography (sEMG) is often used as a control signal in neuromuscular electrical stimulation (NMES) systems to enhance the voluntary control and proprioceptive sensory feedback of paralyzed patients. Most sEMG-controlled NMES systems use the envelope of the sEMG signal to modulate the stimulation intensity (current amplitude or pulse width) with a constant frequency. The aims of this study were to develop a strategy that co-modulates frequency and pulse width based on features of the sEMG signal and to investigate the torque-reproduction performance and the level of fatigue resistance achieved with our strategy. Approach. We examined the relationships between wrist torque and two stimulation parameters (frequency and pulse width) and between wrist torque and two sEMG time-domain features (mean absolute value (MAV) and number of slope sign changes (NSS)) in eight healthy volunteers. By using wrist torque as an intermediate variable, customized and generalized transfer functions were constructed to convert the two features of the sEMG signal into the two stimulation parameters, thereby establishing a MAV/NSS dual-coding (MNDC) algorithm. Wrist torque reproduction performance was assessed by comparing the torque generated by the algorithms with that originally recorded during voluntary contractions. Muscle fatigue was assessed by measuring the decline percentage of the peak torque and by comparing the torque time integral of the response to test stimulation trains before and after fatigue sessions. Main Results. The MNDC approach could produce a wrist torque that closely matched the voluntary wrist torque. In addition, a smaller decay in the wrist torque was observed after the MNDC-coded fatigue stimulation was applied than after stimulation using pulse-width modulation alone. Significance. Compared with pulse-width modulation stimulation strategies that are based on sEMG detection, the MNDC strategy is more effective for both voluntary muscle

Compressed sensing system considerations for ECG and EMG wireless biosensors.

PubMed

Dixon, Anna M R; Allstot, Emily G; Gangopadhyay, Daibashish; Allstot, David J

2012-04-01

Compressed sensing (CS) is an emerging signal processing paradigm that enables sub-Nyquist processing of sparse signals such as electrocardiogram (ECG) and electromyogram (EMG) biosignals. Consequently, it can be applied to biosignal acquisition systems to reduce the data rate to realize ultra-low-power performance. CS is compared to conventional and adaptive sampling techniques and several system-level design considerations are presented for CS acquisition systems including sparsity and compression limits, thresholding techniques, encoder bit-precision requirements, and signal recovery algorithms. Simulation studies show that compression factors greater than 16X are achievable for ECG and EMG signals with signal-to-quantization noise ratios greater than 60 dB.

Modeling Nonlinear Errors in Surface Electromyography Due To Baseline Noise: A New Methodology

PubMed Central

Law, Laura Frey; Krishnan, Chandramouli; Avin, Keith

2010-01-01

The surface electromyographic (EMG) signal is often contaminated by some degree of baseline noise. It is customary for scientists to subtract baseline noise from the measured EMG signal prior to further analyses based on the assumption that baseline noise adds linearly to the observed EMG signal. The stochastic nature of both the baseline and EMG signal, however, may invalidate this assumption. Alternately, “true” EMG signals may be either minimally or nonlinearly affected by baseline noise. This information is particularly relevant at low contraction intensities when signal-to-noise ratios (SNR) may be lowest. Thus, the purpose of this simulation study was to investigate the influence of varying levels of baseline noise (approximately 2 – 40 % maximum EMG amplitude) on mean EMG burst amplitude and to assess the best means to account for signal noise. The simulations indicated baseline noise had minimal effects on mean EMG activity for maximum contractions, but increased nonlinearly with increasing noise levels and decreasing signal amplitudes. Thus, the simple baseline noise subtraction resulted in substantial error when estimating mean activity during low intensity EMG bursts. Conversely, correcting EMG signal as a nonlinear function of both baseline and measured signal amplitude provided highly accurate estimates of EMG amplitude. This novel nonlinear error modeling approach has potential implications for EMG signal processing, particularly when assessing co-activation of antagonist muscles or small amplitude contractions where the SNR can be low. PMID:20869716

EMG amplifier with wireless data transmission

NASA Astrophysics Data System (ADS)

Kowalski, Grzegorz; Wildner, Krzysztof

2017-08-01

Wireless medical diagnostics is a trend in modern technology used in medicine. This paper presents a concept of realization, architecture of hardware and software implementation of an elecromyography signal (EMG) amplifier with wireless data transmission. This amplifier consists of three components: analogue processing of bioelectric signal module, micro-controller circuit and an application enabling data acquisition via a personal computer. The analogue bioelectric signal processing circuit receives electromyography signals from the skin surface, followed by initial analogue processing and preparation of the signals for further digital processing. The second module is a micro-controller circuit designed to wirelessly transmit the electromyography signals from the analogue signal converter to a personal computer. Its purpose is to eliminate the need for wired connections between the patient and the data logging device. The third block is a computer application designed to display the transmitted electromyography signals, as well as data capture and analysis. Its purpose is to provide a graphical representation of the collected data. The entire device has been thoroughly tested to ensure proper functioning. In use, the device displayed the captured electromyography signal from the arm of the patient. Amplitude- frequency characteristics were set in order to investigate the bandwidth and the overall gain of the device.

Surface EMG of shoulder and back muscles and posture analysis in secretaries typing at visual display units.

PubMed

Kleine, B U; Schumann, N P; Bradl, I; Grieshaber, R; Scholle, H C

1999-09-01

A study was carried out to investigate temporal changes of activation of shoulder and back muscles in workers at visual display units by means of surface EMG. Moreover, postural parameters were recorded to distinguish fatigue-related from posture-related changes of the myoelectrical activity. Nine healthy female office workers typed texts spoken from tape during three 1-h-long sessions. After the first and again after the second hour there was a break of 15 min. Sixteen-channel surface EMG was bipolarly recorded from the erector spinae, trapezius, deltoid and sternocleidomastoid muscles. Root mean square (RMS) and power spectrum median frequency of the EMG were calculated. Sitting posture was assessed using an eight-channel movement analysis system with ultrasound markers. The position of the seventh cervical spinous process and the left and the right acromion were analysed synchronously with the EMG characteristics using regression analysis. The normalised RMS of the left and right trapezius muscle increased, while the median frequency did not change. The increase of the normalised RMS was significantly lower when the linear influence of posture was excluded. On average, the distance between C7 and the left and right acromion decreased within each working an hour. C7 became lower on average by 5.5 mm within an hour, whereas the acromions became lower by only 1.7 mm (left) and 3.3 mm (right). The increase in trapezius muscle activity was partly related to a lifting of the shoulders to compensate a slight slumping of the back. Another part of the EMG activity increase has to be attributed to fatigue, to attention-related activity or to the combination of both. Therefore, training of the back muscles and a varied organisation of work might have a preventive effect with respect to musculoskeletal complaints in VDU workers.

Knowledge of electromyography (EMG) in patients undergoing EMG examinations

PubMed Central

Mondelli, Mauro; Aretini, Alessandro; Greco, Giuseppe

2014-01-01

Summary The aim of this study was to evaluate knowledge of electromyography (EMG) in patients undergoing the procedure. In one year, 1,586 consecutive patients (mean age 56 years; 58.8% women) were admitted to two EMG labs to undergo EMG for the first time. The patients found to be “informed” about the how an EMG examination is performed and about the purpose of EMG numbered 448 (28.2%), while those found to be “informed” only about the manner of its execution or only about its purpose numbered 161 (10.2%) and 151 (9.5%), respectively. The remaining 826 (52.1%) patients had either no information, or the information they had was very poor or incorrect (this was particularly true if they had been consulting websites). Being “informed” was associated with level of education (high), type of referring physician (specialist) and with an appropriate referral diagnosis specified in the EMG request. The quality of patient information on EMG was found to be very poor and could be improved. Physicians referring patients for EMG examinations, especially general practitioners, should assume primary responsibility for patient education and counseling in this field. PMID:25473740

Changes in force, surface and motor unit EMG during post-exercise development of low frequency fatigue in vastus lateralis muscle.

PubMed

de Ruiter, C J; Elzinga, M J H; Verdijk, P W L; van Mechelen, W; de Haan, A

2005-08-01

We investigated the effects of low frequency fatigue (LFF) on post-exercise changes in rectified surface EMG (rsEMG) and single motor unit EMG (smuEMG) in vastus lateralis muscle (n = 9). On two experimental days the knee extensors were fatigued with a 60-s-isometric contraction (exercise) at 50% maximal force capacity (MFC). On the first day post-exercise (15 s, 3, 9, 15, 21 and 27 min) rsEMG and electrically-induced (surface stimulation) forces were investigated. SmuEMG was obtained on day two. During short ramp and hold (5 s) contractions at 50% MFC, motor unit discharges of the same units were followed over time. Post-exercise MFC and tetanic force (100 Hz stimulation) recovered to about 90% of the pre-exercise values, but recovery with 20 Hz stimulation was less complete: the 20-100 Hz force ratio (mean +/- SD) decreased from 0.65+/-0.06 (pre-exercise) to 0.56+/-0.04 at 27 min post-exercise (P<0.05), indicative of LFF. At 50% MFC, pre-exercise rsEMG (% pre-exercise maximum) and motor unit discharge rate were 51.1 +/- 12.7% and 14.1 +/- 3.7 (pulses per second; pps) respectively, 15 s post-exercise the respective values were 61.4 +/- 15.4% (P<0.05) and 13.2 +/- 5.6 pps (P>0.05). Thereafter, rsEMG (at 50% MFC) remained stable but motor unit discharge rate significantly increased to 17.7 +/- 3.9 pps 27 min post-exercise. The recruitment threshold decreased (P<0.05) from 27.7 +/- 6.6% MFC before exercise to 25.2 +/- 6.7% 27 min post-exercise. The increase in discharge rate was significantly greater than could be expected from the decrease in recruitment threshold. Thus, post-exercise LFF was compensated by increased motor unit discharge rates which could only partly be accounted for by the small decrease in motor unit recruitment threshold.

Influence on muscle oxygenation to EMG parameters at different skeletal muscle contraction

NASA Astrophysics Data System (ADS)

Zhang, Li; Song, Gaoqing

2010-02-01

The purpose of this study is to investigate the influence of muscle oxygenation on EMG parameters during isometric and incremental exercises and to observe the relationship between EMG parameters and muscle oxygenation. Twelve rowers took part in the tests. Near infrared spectrometer was utilized for measurements of muscle oxygenation on lateral quadriceps. sEMG measurement is performed for EMG parameters during isometric and incremental exercises. Results indicated that Oxy-Hb decrease significantly correlated with IEMG, E/T ratio and frequency of impulse signal during 1/3 MVC and 2/3 MVC isometric exercise, and it is also correlated with IEMG, E/T ratio and frequency of impulse signal. Increase of IEMG occurred at the time after Oxy-Hb decrease during incremental exercise and highly correlated with BLa. It is concluded that no matter how heavy the intensity is, Oxy-Hb dissociation may play an important role in affecting EMG parameters of muscle fatigue during isometric exercise. 2) EMG parameters may be influenced by Oxy-Hb dissociation and blood lactate concentration during dynamic exercise.

Influence on muscle oxygenation to EMG parameters at different skeletal muscle contraction

NASA Astrophysics Data System (ADS)

Zhang, Li; Song, Gaoqing

2009-10-01

The purpose of this study is to investigate the influence of muscle oxygenation on EMG parameters during isometric and incremental exercises and to observe the relationship between EMG parameters and muscle oxygenation. Twelve rowers took part in the tests. Near infrared spectrometer was utilized for measurements of muscle oxygenation on lateral quadriceps. sEMG measurement is performed for EMG parameters during isometric and incremental exercises. Results indicated that Oxy-Hb decrease significantly correlated with IEMG, E/T ratio and frequency of impulse signal during 1/3 MVC and 2/3 MVC isometric exercise, and it is also correlated with IEMG, E/T ratio and frequency of impulse signal. Increase of IEMG occurred at the time after Oxy-Hb decrease during incremental exercise and highly correlated with BLa. It is concluded that no matter how heavy the intensity is, Oxy-Hb dissociation may play an important role in affecting EMG parameters of muscle fatigue during isometric exercise. 2) EMG parameters may be influenced by Oxy-Hb dissociation and blood lactate concentration during dynamic exercise.

Test-retest reliability of muscle fiber conduction velocity and fractal dimension of surface EMG during isometric contractions.

PubMed

Beretta-Piccoli, Matteo; D'Antona, Giuseppe; Zampella, Cristian; Barbero, Marco; Clijsen, Ron; Cescon, Corrado

2017-04-01

The aim of this study was to determine the test-retest reliability of muscle fiber conduction velocity (CV) and fractal dimension (FD) obtained from multichannel surface electromyographic (sEMG) recordings. Forty healthy recreationally active subjects (20 men and 20 women) performed two elbow flexions on two trials with a 1 week interval. The first was a 20% maximal voluntary contraction (MVC) of 120 s, and the second at 60% MVC held until exhaustion. sEMG signals were detected from the biceps brachii, using bi-dimensional arrays. Initial values and slope of CV and FD were used for the reliability analysis. The intraclass correlation coefficient (ICC) values for the isometric contraction at 20% MVC were (-0.09) and 0.67 for CV and FD respectively; whereas the ICC values at 60% MVC were 0.78 and 0.82 for CV and FD respectively. The Bland Altman plots for the two isometric contractions showed a mean difference close to zero, with no evident outliers between the repeated measurements: at 20% MVC 0.001 53 for FD and  -0.0277 for CV, and at 60% MVC 0.006 66 for FD and 0.009 07 for CV. Overall, our findings suggest that during isometric fatiguing contractions, CV and FD slopes are reliable variables, with potential application in clinical populations.

EMG prediction from Motor Cortical Recordings via a Non-Negative Point Process Filter

PubMed Central

Nazarpour, Kianoush; Ethier, Christian; Paninski, Liam; Rebesco, James M.; Miall, R. Chris; Miller, Lee E.

2012-01-01

A constrained point process filtering mechanism for prediction of electromyogram (EMG) signals from multi-channel neural spike recordings is proposed here. Filters from the Kalman family are inherently sub-optimal in dealing with non-Gaussian observations, or a state evolution that deviates from the Gaussianity assumption. To address these limitations, we modeled the non-Gaussian neural spike train observations by using a generalized linear model (GLM) that encapsulates covariates of neural activity, including the neurons’ own spiking history, concurrent ensemble activity, and extrinsic covariates (EMG signals). In order to predict the envelopes of EMGs, we reformulated the Kalman filter (KF) in an optimization framework and utilized a non-negativity constraint. This structure characterizes the non-linear correspondence between neural activity and EMG signals reasonably. The EMGs were recorded from twelve forearm and hand muscles of a behaving monkey during a grip-force task. For the case of limited training data, the constrained point process filter improved the prediction accuracy when compared to a conventional Wiener cascade filter (a linear causal filter followed by a static non-linearity) for different bin sizes and delays between input spikes and EMG output. For longer training data sets, results of the proposed filter and that of the Wiener cascade filter were comparable. PMID:21659018

Relationship between oxygen uptake slow component and surface EMG during heavy exercise in humans: influence of pedal rate.

PubMed

Vercruyssen, Fabrice; Missenard, Olivier; Brisswalter, Jeanick

2009-08-01

The aim of this study was to test the hypothesis that extreme pedal rates contributed to the slow component of oxygen uptake (VO(2) SC) in association with changes in surface electromyographic (sEMG) during heavy-cycle exercise. Eight male trained cyclists performed two square-wave transitions at 50 and 110 rpm at a work rate that would elicit a VO(2) corresponding to 50% of the difference between peak VO(2) and the ventilatory threshold. Pulmonary gas exchange was measured breath-by-breath and sEMG was obtained from the vastus lateralis and medialis muscles. Integrated EMG flow (QiEMG) and mean power frequency (MPF) were computed. The relative amplitude of the VO(2) SC was significantly higher during the 110-rpm bout (556+/-186 ml min(-1), P<0.05) with compared to the 50-rpm bout (372+/-227 ml min(-1)). QiEMG values increased throughout exercise only during the 110-rpm bout and were associated with the greater amplitude of the VO(2) SC observed for this condition (P<0.05). MPF values remained relatively constant whatever the cycle bout. These findings indicated a VO(2) SC at the two pedal rates but the association with sEMG responses was observed only at high pedal rate. Possible changes in motor units recruitment pattern, muscle energy turnover and muscle temperature have been suggested to explain the different VO(2) SC to heavy pedal rate bouts.

Spatial distribution of surface EMG on trapezius and lumbar muscles of violin and cello players in single note playing.

PubMed

Afsharipour, Babak; Petracca, Francesco; Gasparini, Mauro; Merletti, Roberto

2016-12-01

Musicians activate their muscles in different patterns, depending on their posture, the instrument being played, and their experience level. Bipolar surface electrodes have been used in the past to monitor such activity, but this method is highly sensitive to the location of the electrode pair. In this work, the spatial distribution of surface EMG (sEMG) of the right trapezius and right and left erector spinae muscles were studied in 16 violin players and 11 cello players. Musicians played their instrument one string at a time in sitting position with/without backrest support. A 64 sEMG electrode (16×4) grid, 10mm inter-electrode distance (IED), was placed over the middle and lower trapezius (MT and LT) of the bowing arm. Two 16×2 electrode grids (IED=10mm) were placed on the left and right erector spinae muscles. Subjects played each of the four strings of the instrument either in large (1bow/s) or detaché tip/tail (8bows/s) bowing in two sessions (two days). In each of two days, measurements were repeated after half an hour of exercise to see the effect of exercise on the muscle activity and signal stability. A "muscle activity index" (MAI) was defined as the spatial average of the segmented active region of the RMS map. Spatial maps were automatically segmented using the watershed algorithm and thresholding. Results showed that, for violin players, sliding the bow upward from the tip toward the tail results in a higher MAI for the trapezius muscle than a downward bow. On the contrary, in cello players, higher MAI is produced in the tail to tip movement. For both instruments, an increasing MAI in the trapezius was observed as the string position became increasingly lateral, from string 1 (most medial) toward string 4 (most lateral). Half an hour of performance did not cause significant differences between the signal quality and the MAI values measured before and after the exercise. The MAI of the left and right erector spinae was smaller in the case of

EOG and EMG: two important switches in automatic sleep stage classification.

PubMed

Estrada, E; Nazeran, H; Barragan, J; Burk, J R; Lucas, E A; Behbehani, K

2006-01-01

Sleep is a natural periodic state of rest for the body, in which the eyes are usually closed and consciousness is completely or partially lost. In this investigation we used the EOG and EMG signals acquired from 10 patients undergoing overnight polysomnography with their sleep stages determined by expert sleep specialists based on RK rules. Differentiation between Stage 1, Awake and REM stages challenged a well trained neural network classifier to distinguish between classes when only EEG-derived signal features were used. To meet this challenge and improve the classification rate, extra features extracted from EOG and EMG signals were fed to the classifier. In this study, two simple feature extraction algorithms were applied to EOG and EMG signals. The statistics of the results were calculated and displayed in an easy to visualize fashion to observe tendencies for each sleep stage. Inclusion of these features show a great promise to improve the classification rate towards the target rate of 100%

Human joint motion estimation for electromyography (EMG)-based dynamic motion control.

PubMed

Zhang, Qin; Hosoda, Ryo; Venture, Gentiane

2013-01-01

This study aims to investigate a joint motion estimation method from Electromyography (EMG) signals during dynamic movement. In most EMG-based humanoid or prosthetics control systems, EMG features were directly or indirectly used to trigger intended motions. However, both physiological and nonphysiological factors can influence EMG characteristics during dynamic movements, resulting in subject-specific, non-stationary and crosstalk problems. Particularly, when motion velocity and/or joint torque are not constrained, joint motion estimation from EMG signals are more challenging. In this paper, we propose a joint motion estimation method based on muscle activation recorded from a pair of agonist and antagonist muscles of the joint. A linear state-space model with multi input single output is proposed to map the muscle activity to joint motion. An adaptive estimation method is proposed to train the model. The estimation performance is evaluated in performing a single elbow flexion-extension movement in two subjects. All the results in two subjects at two load levels indicate the feasibility and suitability of the proposed method in joint motion estimation. The estimation root-mean-square error is within 8.3% ∼ 10.6%, which is lower than that being reported in several previous studies. Moreover, this method is able to overcome subject-specific problem and compensate non-stationary EMG properties.

Stationary Wavelet-based Two-directional Two-dimensional Principal Component Analysis for EMG Signal Classification

NASA Astrophysics Data System (ADS)

Ji, Yi; Sun, Shanlin; Xie, Hong-Bo

2017-06-01

Discrete wavelet transform (WT) followed by principal component analysis (PCA) has been a powerful approach for the analysis of biomedical signals. Wavelet coefficients at various scales and channels were usually transformed into a one-dimensional array, causing issues such as the curse of dimensionality dilemma and small sample size problem. In addition, lack of time-shift invariance of WT coefficients can be modeled as noise and degrades the classifier performance. In this study, we present a stationary wavelet-based two-directional two-dimensional principal component analysis (SW2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify eight hand motions using 4-channel electromyographic (EMG) signals recorded in healthy subjects and amputees, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis.

Use of sEMG in identification of low level muscle activities: features based on ICA and fractal dimension.

PubMed

Naik, Ganesh R; Kumar, Dinesh K; Arjunan, Sridhar

2009-01-01

This paper has experimentally verified and compared features of sEMG (Surface Electromyogram) such as ICA (Independent Component Analysis) and Fractal Dimension (FD) for identification of low level forearm muscle activities. The fractal dimension was used as a feature as reported in the literature. The normalized feature values were used as training and testing vectors for an Artificial neural network (ANN), in order to reduce inter-experimental variations. The identification accuracy using FD of four channels sEMG was 58%, and increased to 96% when the signals are separated to their independent components using ICA.

Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network

PubMed Central

Zhai, Xiaolong; Jelfs, Beth; Chan, Rosa H. M.; Tin, Chung

2017-01-01

Hand movement classification based on surface electromyography (sEMG) pattern recognition is a promising approach for upper limb neuroprosthetic control. However, maintaining day-to-day performance is challenged by the non-stationary nature of sEMG in real-life operation. In this study, we propose a self-recalibrating classifier that can be automatically updated to maintain a stable performance over time without the need for user retraining. Our classifier is based on convolutional neural network (CNN) using short latency dimension-reduced sEMG spectrograms as inputs. The pretrained classifier is recalibrated routinely using a corrected version of the prediction results from recent testing sessions. Our proposed system was evaluated with the NinaPro database comprising of hand movement data of 40 intact and 11 amputee subjects. Our system was able to achieve ~10.18% (intact, 50 movement types) and ~2.99% (amputee, 10 movement types) increase in classification accuracy averaged over five testing sessions with respect to the unrecalibrated classifier. When compared with a support vector machine (SVM) classifier, our CNN-based system consistently showed higher absolute performance and larger improvement as well as more efficient training. These results suggest that the proposed system can be a useful tool to facilitate long-term adoption of prosthetics for amputees in real-life applications. PMID:28744189

Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network.

PubMed

Zhai, Xiaolong; Jelfs, Beth; Chan, Rosa H M; Tin, Chung

2017-01-01

Hand movement classification based on surface electromyography (sEMG) pattern recognition is a promising approach for upper limb neuroprosthetic control. However, maintaining day-to-day performance is challenged by the non-stationary nature of sEMG in real-life operation. In this study, we propose a self-recalibrating classifier that can be automatically updated to maintain a stable performance over time without the need for user retraining. Our classifier is based on convolutional neural network (CNN) using short latency dimension-reduced sEMG spectrograms as inputs. The pretrained classifier is recalibrated routinely using a corrected version of the prediction results from recent testing sessions. Our proposed system was evaluated with the NinaPro database comprising of hand movement data of 40 intact and 11 amputee subjects. Our system was able to achieve ~10.18% (intact, 50 movement types) and ~2.99% (amputee, 10 movement types) increase in classification accuracy averaged over five testing sessions with respect to the unrecalibrated classifier. When compared with a support vector machine (SVM) classifier, our CNN-based system consistently showed higher absolute performance and larger improvement as well as more efficient training. These results suggest that the proposed system can be a useful tool to facilitate long-term adoption of prosthetics for amputees in real-life applications.

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.

A new algorithm for ECG interference removal from single channel EMG recording.

PubMed

Yazdani, Shayan; Azghani, Mahmood Reza; Sedaaghi, Mohammad Hossein

2017-09-01

This paper presents a new method to remove electrocardiogram (ECG) interference from electromyogram (EMG). This interference occurs during the EMG acquisition from trunk muscles. The proposed algorithm employs progressive image denoising (PID) algorithm and ensembles empirical mode decomposition (EEMD) to remove this type of interference. PID is a very recent method that is being used for denoising digital images mixed with white Gaussian noise. It detects white Gaussian noise by deterministic annealing. To the best of our knowledge, PID has never been used before, in the case of EMG and ECG separation or in other 1D signal denoising applications. We have used it according to this fact that amplitude of the EMG signal can be modeled as white Gaussian noise using a filter with time-variant properties. The proposed algorithm has been compared to the other well-known methods such as HPF, EEMD-ICA, Wavelet-ICA and PID. The results show that the proposed algorithm outperforms the others, on the basis of three evaluation criteria used in this paper: Normalized mean square error, Signal to noise ratio and Pearson correlation.

The impact of shoulder abduction loading on EMG-based intention detection of hand opening and closing after stroke.

PubMed

Lan, Yiyun; Yao, Jun; Dewald, Julius P A

2011-01-01

Many stroke patients are subject to limited hand functions in the paretic arm due to a significant loss of Corticospinal Tract (CST) fibers. A possible solution for this problem is to classify surface Electromyography (EMG) signals generated by hand movements and uses that to implement Functional Electrical Stimulation (FES). However, EMG usually presents an abnormal muscle coactivation pattern shown as increased coupling between muscles within and/or across joints after stroke. The resulting Abnormal Muscle Synergies (AMS) could make the classification more difficult in individuals with stroke, especially when attempting to use the hand together with other joints in the paretic arm. Therefore, this study is aimed at identifying the impact of AMS following stroke on EMG pattern recognition between two hand movements. In an effort to achieve this goal, 7 chronic hemiparetic chronic stroke subjects were recruited and asked to perform hand opening and closing movements at their paretic arm while being either fully supported by a virtual table or loaded with 25% of subject's maximum shoulder abduction force. During the execution of motor tasks EMG signals from the wrist flexors and extensors were simultaneously acquired. Our results showed that increased synergy-induced activity at elbow flexors, induced by increasing shoulder abduction loading, deteriorated the performance of EMG pattern recognition for hand opening for those with a weak grasp strength and EMG activity. However, no such impact on hand closing has yet been observed possibly because finger/wrist flexion is facilitated by the shoulder abduction-induced flexion synergy.

A hybrid BMI-based exoskeleton for paresis: EMG control for assisting arm movements

NASA Astrophysics Data System (ADS)

Kawase, Toshihiro; Sakurada, Takeshi; Koike, Yasuharu; Kansaku, Kenji

2017-02-01

Objective. Brain-machine interface (BMI) technologies have succeeded in controlling robotic exoskeletons, enabling some paralyzed people to control their own arms and hands. We have developed an exoskeleton asynchronously controlled by EEG signals. In this study, to enable real-time control of the exoskeleton for paresis, we developed a hybrid system with EEG and EMG signals, and the EMG signals were used to estimate its joint angles. Approach. Eleven able-bodied subjects and two patients with upper cervical spinal cord injuries (SCIs) performed hand and arm movements, and the angles of the metacarpophalangeal (MP) joint of the index finger, wrist, and elbow were estimated from EMG signals using a formula that we derived to calculate joint angles from EMG signals, based on a musculoskeletal model. The formula was exploited to control the elbow of the exoskeleton after automatic adjustments. Four able-bodied subjects and a patient with upper cervical SCI wore an exoskeleton controlled using EMG signals and were required to perform hand and arm movements to carry and release a ball. Main results. Estimated angles of the MP joints of index fingers, wrists, and elbows were correlated well with the measured angles in 11 able-bodied subjects (correlation coefficients were 0.81  ±  0.09, 0.85  ±  0.09, and 0.76  ±  0.13, respectively) and the patients (e.g. 0.91  ±  0.01 in the elbow of a patient). Four able-bodied subjects successfully positioned their arms to adequate angles by extending their elbows and a joint of the exoskeleton, with root-mean-square errors  <6°. An upper cervical SCI patient, empowered by the exoskeleton, successfully carried a ball to a goal in all 10 trials. Significance. A BMI-based exoskeleton for paralyzed arms and hands using real-time control was realized by designing a new method to estimate joint angles based on EMG signals, and these may be useful for practical rehabilitation and the support of

A hybrid BMI-based exoskeleton for paresis: EMG control for assisting arm movements.

PubMed

Kawase, Toshihiro; Sakurada, Takeshi; Koike, Yasuharu; Kansaku, Kenji

2017-02-01

Brain-machine interface (BMI) technologies have succeeded in controlling robotic exoskeletons, enabling some paralyzed people to control their own arms and hands. We have developed an exoskeleton asynchronously controlled by EEG signals. In this study, to enable real-time control of the exoskeleton for paresis, we developed a hybrid system with EEG and EMG signals, and the EMG signals were used to estimate its joint angles. Eleven able-bodied subjects and two patients with upper cervical spinal cord injuries (SCIs) performed hand and arm movements, and the angles of the metacarpophalangeal (MP) joint of the index finger, wrist, and elbow were estimated from EMG signals using a formula that we derived to calculate joint angles from EMG signals, based on a musculoskeletal model. The formula was exploited to control the elbow of the exoskeleton after automatic adjustments. Four able-bodied subjects and a patient with upper cervical SCI wore an exoskeleton controlled using EMG signals and were required to perform hand and arm movements to carry and release a ball. Estimated angles of the MP joints of index fingers, wrists, and elbows were correlated well with the measured angles in 11 able-bodied subjects (correlation coefficients were 0.81  ±  0.09, 0.85  ±  0.09, and 0.76  ±  0.13, respectively) and the patients (e.g. 0.91  ±  0.01 in the elbow of a patient). Four able-bodied subjects successfully positioned their arms to adequate angles by extending their elbows and a joint of the exoskeleton, with root-mean-square errors  <6°. An upper cervical SCI patient, empowered by the exoskeleton, successfully carried a ball to a goal in all 10 trials. A BMI-based exoskeleton for paralyzed arms and hands using real-time control was realized by designing a new method to estimate joint angles based on EMG signals, and these may be useful for practical rehabilitation and the support of daily actions.

Comparison of sEMG-Based Feature Extraction and Motion Classification Methods for Upper-Limb Movement

PubMed Central

Guo, Shuxiang; Pang, Muye; Gao, Baofeng; Hirata, Hideyuki; Ishihara, Hidenori

2015-01-01

The surface electromyography (sEMG) technique is proposed for muscle activation detection and intuitive control of prostheses or robot arms. Motion recognition is widely used to map sEMG signals to the target motions. One of the main factors preventing the implementation of this kind of method for real-time applications is the unsatisfactory motion recognition rate and time consumption. The purpose of this paper is to compare eight combinations of four feature extraction methods (Root Mean Square (RMS), Detrended Fluctuation Analysis (DFA), Weight Peaks (WP), and Muscular Model (MM)) and two classifiers (Neural Networks (NN) and Support Vector Machine (SVM)), for the task of mapping sEMG signals to eight upper-limb motions, to find out the relation between these methods and propose a proper combination to solve this issue. Seven subjects participated in the experiment and six muscles of the upper-limb were selected to record sEMG signals. The experimental results showed that NN classifier obtained the highest recognition accuracy rate (88.7%) during the training process while SVM performed better in real-time experiments (85.9%). For time consumption, SVM took less time than NN during the training process but needed more time for real-time computation. Among the four feature extraction methods, WP had the highest recognition rate for the training process (97.7%) while MM performed the best during real-time tests (94.3%). The combination of MM and NN is recommended for strict real-time applications while a combination of MM and SVM will be more suitable when time consumption is not a key requirement. PMID:25894941

The importance of the orientation of the electrode plates in recording the external anal sphincter EMG by non-invasive anal plug electrodes.

PubMed

Binnie, N R; Kawimbe, B M; Papachrysostomou, M; Clare, N; Smith, A N

1991-02-01

Two non-invasive anal plug electrodes of similar size have been compared, one with the electrode plates orientated circularly in the anal canal and the other with the plates in the long axis of the anal canal. There was a significant increase in the amplitude in the EMG signals recorded at rest and during squeeze from the external anal sphincter with a longitudinally placed electrode in 117 patients. Inappropriate contraction of the external anal sphincter when straining at stool was more readily detected using the longitudinal electrode in 52 patients investigated for intractable constipation. The longitudinal electrode detected the amplitude of the response to the elicitation of a pudeno-anal reflex more readily than the circular electrode. When in 12 of the 117 the pudeno-anal reflex EMG signal was either absent or not detected with the circumferential plug electrode, the longitudinal electrode detected the presence of a low amplitude response in 11 of these. When the non-invasive longitudinal electrode was compared to invasive fine wire stainless steel electrodes, a correlation was found for external anal sphincter resting EMG (r = 0.99, p less than 0.01), voluntary squeeze EMG (r = 0.99, p less than 0.001) and strain EMG (r = 0.91, p less than 0.01). The longitudinal anal plug electrode thus facilitates surface acquisition of EMG activity.

Multichannel noninvasive human-machine interface via stretchable µm thick sEMG patches for robot manipulation

NASA Astrophysics Data System (ADS)

Zhou, Ying; Wang, Youhua; Liu, Runfeng; Xiao, Lin; Zhang, Qin; Huang, YongAn

2018-01-01

Epidermal electronics (e-skin) emerging in recent years offer the opportunity to noninvasively and wearably extract biosignals from human bodies. The conventional processes of e-skin based on standard microelectronic fabrication processes and a variety of transfer printing methods, nevertheless, unquestionably constrains the size of the devices, posing a serious challenge to collecting signals via skin, the largest organ in the human body. Herein we propose a multichannel noninvasive human-machine interface (HMI) using stretchable surface electromyography (sEMG) patches to realize a robot hand mimicking human gestures. Time-efficient processes are first developed to manufacture µm thick large-scale stretchable devices. With micron thickness, the stretchable µm thick sEMG patches show excellent conformability with human skin and consequently comparable electrical performance with conventional gel electrodes. Combined with the large-scale size, the multichannel noninvasive HMI via stretchable µm thick sEMG patches successfully manipulates the robot hand with eight different gestures, whose precision is as high as conventional gel electrodes array.

A new biomechanical hand prosthesis controlled by surface electromyographic signals.

PubMed

Andrade, Nei A; Borges, Geovany A; de O Nascimento, Francisco A; Romariz, Alexandre R S; da Rocha, Adson F

2007-01-01

This paper describes the development of a low-cost hand prosthesis for use in patients with an amputated hand due to congenital problems or to trauma wound, who possess a part or the forearm endowed with muscular activity. The paper covers the constructive aspects of both mechanical and electronic designs. The prototype is controlled by electromyographic signals measured at the remaining part of the injured limb of the patient. The EMG signals are measured at the surface of the skin, at a point that is close to a working muscle of the amputated arm. The prosthesis allows the patient to hold objects by means of a three finger clamp. The prosthesis presented an excellent performance in preliminary tests with an amputated patient. These tests showed that the prosthesis had a very good performance regarding force and speed.

Feature Extraction and Selection for Myoelectric Control Based on Wearable EMG Sensors.

PubMed

Phinyomark, Angkoon; N Khushaba, Rami; Scheme, Erik

2018-05-18

Specialized myoelectric sensors have been used in prosthetics for decades, but, with recent advancements in wearable sensors, wireless communication and embedded technologies, wearable electromyographic (EMG) armbands are now commercially available for the general public. Due to physical, processing, and cost constraints, however, these armbands typically sample EMG signals at a lower frequency (e.g., 200 Hz for the Myo armband) than their clinical counterparts. It remains unclear whether existing EMG feature extraction methods, which largely evolved based on EMG signals sampled at 1000 Hz or above, are still effective for use with these emerging lower-bandwidth systems. In this study, the effects of sampling rate (low: 200 Hz vs. high: 1000 Hz) on the classification of hand and finger movements were evaluated for twenty-six different individual features and eight sets of multiple features using a variety of datasets comprised of both able-bodied and amputee subjects. The results show that, on average, classification accuracies drop significantly ( p.

Steering a Tractor by Means of an EMG-Based Human-Machine Interface

PubMed Central

Gomez-Gil, Jaime; San-Jose-Gonzalez, Israel; Nicolas-Alonso, Luis Fernando; Alonso-Garcia, Sergio

2011-01-01

An electromiographic (EMG)-based human-machine interface (HMI) is a communication pathway between a human and a machine that operates by means of the acquisition and processing of EMG signals. This article explores the use of EMG-based HMIs in the steering of farm tractors. An EPOC, a low-cost human-computer interface (HCI) from the Emotiv Company, was employed. This device, by means of 14 saline sensors, measures and processes EMG and electroencephalographic (EEG) signals from the scalp of the driver. In our tests, the HMI took into account only the detection of four trained muscular events on the driver’s scalp: eyes looking to the right and jaw opened, eyes looking to the right and jaw closed, eyes looking to the left and jaw opened, and eyes looking to the left and jaw closed. The EMG-based HMI guidance was compared with manual guidance and with autonomous GPS guidance. A driver tested these three guidance systems along three different trajectories: a straight line, a step, and a circumference. The accuracy of the EMG-based HMI guidance was lower than the accuracy obtained by manual guidance, which was lower in turn than the accuracy obtained by the autonomous GPS guidance; the computed standard deviations of error to the desired trajectory in the straight line were 16 cm, 9 cm, and 4 cm, respectively. Since the standard deviation between the manual guidance and the EMG-based HMI guidance differed only 7 cm, and this difference is not relevant in agricultural steering, it can be concluded that it is possible to steer a tractor by an EMG-based HMI with almost the same accuracy as with manual steering. PMID:22164006

Steering a tractor by means of an EMG-based human-machine interface.

PubMed

Gomez-Gil, Jaime; San-Jose-Gonzalez, Israel; Nicolas-Alonso, Luis Fernando; Alonso-Garcia, Sergio

2011-01-01

An electromiographic (EMG)-based human-machine interface (HMI) is a communication pathway between a human and a machine that operates by means of the acquisition and processing of EMG signals. This article explores the use of EMG-based HMIs in the steering of farm tractors. An EPOC, a low-cost human-computer interface (HCI) from the Emotiv Company, was employed. This device, by means of 14 saline sensors, measures and processes EMG and electroencephalographic (EEG) signals from the scalp of the driver. In our tests, the HMI took into account only the detection of four trained muscular events on the driver's scalp: eyes looking to the right and jaw opened, eyes looking to the right and jaw closed, eyes looking to the left and jaw opened, and eyes looking to the left and jaw closed. The EMG-based HMI guidance was compared with manual guidance and with autonomous GPS guidance. A driver tested these three guidance systems along three different trajectories: a straight line, a step, and a circumference. The accuracy of the EMG-based HMI guidance was lower than the accuracy obtained by manual guidance, which was lower in turn than the accuracy obtained by the autonomous GPS guidance; the computed standard deviations of error to the desired trajectory in the straight line were 16 cm, 9 cm, and 4 cm, respectively. Since the standard deviation between the manual guidance and the EMG-based HMI guidance differed only 7 cm, and this difference is not relevant in agricultural steering, it can be concluded that it is possible to steer a tractor by an EMG-based HMI with almost the same accuracy as with manual steering.

Motor unit recruitment and bursts of activity in the surface electromyogram during a sustained contraction.

PubMed

Riley, Zachary A; Terry, Mary E; Mendez-Villanueva, Alberto; Litsey, Jane C; Enoka, Roger M

2008-06-01

Bursts of activity in the surface electromyogram (EMG) during a sustained contraction have been interpreted as corresponding to the transient recruitment of motor units, but this association has never been confirmed. The current study compared the timing of trains of action potentials discharged by single motor units during a sustained contraction with the bursts of activity detected in the surface EMG signal. The 20 motor units from 6 subjects [recruitment threshold, 35.3 +/- 11.3% maximal voluntary contraction (MVC) force] that were detected with fine wire electrodes discharged 2-9 trains of action potentials (7.2 +/- 5.6 s in duration) when recruited during a contraction that was sustained at a force below its recruitment threshold (target force, 25.4 +/- 10.6% MVC force). High-pass filtering the bipolar surface EMG signal improved its correlation with the single motor unit signal. An algorithm applied to the surface EMG was able to detect 75% of the trains of motor unit action potentials. The results indicate that bursts of activity in the surface EMG during a constant-force contraction correspond to the transient recruitment of higher-threshold motor units in healthy individuals, and these results could assist in the diagnosis and design of treatment in individuals who demonstrate deficits in motor unit activation.

Surface EMG characteristics of people with multiple sclerosis during static contractions of the knee extensors.

PubMed

Scott, Sasha M; Hughes, Adrienne R; Galloway, Stuart D R; Hunter, Angus M

2011-01-01

This study was designed to determine whether any alterations existed in surface electromyography (sEMG) in people with multiple sclerosis (MS) during isometric contractions of the knee extensors. Fifteen people with MS and 14 matched controls (mean ± SD age and body mass index 53·7 ± 10·5 versus 54·6 ± 9·6 years and 27·7 ± 6·1 versus 26·5 ± 4, respectively) completed 20%, 40%, 60% and 80% of their maximal voluntary contraction (MVC) of the knee extensors. sEMG was recorded from the vastus lateralis where muscle fibre conduction velocity (MFCV) and sEMG amplitude (RMS) were assessed. Body composition was determined using dual-energy X-ray absorptiometry and physical activity with the use of accelerometry. People with MS showed significantly (P<0·05) faster MFCV during MVC (6·6 ± 2·7 versus 4·7 ± 1·4 m s(-1) ) and all submaximal contractions, while RMS was significantly (P<0·05) less (0·11 ± 0·03 versus 0·24 ± 0·06 mV) in comparison with the controls. MVC along with specific thigh lean mass to torque, rate of force development and mean physical activity were significantly (P<0·01) less in PwMS. People with MS have elevated MFCV alongside reduced RMS during isometric contraction. This elevation in MFCV should be accounted for when interpreting sEMG from people with MS. © 2010 University of Stirling. Clinical physiology and Functional Imaging © 2010 Scandinavian Society of Clinical Physiology and Nuclear Medicine.

Local Wavelet-Based Filtering of Electromyographic Signals to Eliminate the Electrocardiographic-Induced Artifacts in Patients with Spinal Cord Injury

PubMed Central

Nitzken, Matthew; Bajaj, Nihit; Aslan, Sevda; Gimel’farb, Georgy; Ovechkin, Alexander

2013-01-01

Surface Electromyography (EMG) is a standard method used in clinical practice and research to assess motor function in order to help with the diagnosis of neuromuscular pathology in human and animal models. EMG recorded from trunk muscles involved in the activity of breathing can be used as a direct measure of respiratory motor function in patients with spinal cord injury (SCI) or other disorders associated with motor control deficits. However, EMG potentials recorded from these muscles are often contaminated with heart-induced electrocardiographic (ECG) signals. Elimination of these artifacts plays a critical role in the precise measure of the respiratory muscle electrical activity. This study was undertaken to find an optimal approach to eliminate the ECG artifacts from EMG recordings. Conventional global filtering can be used to decrease the ECG-induced artifact. However, this method can alter the EMG signal and changes physiologically relevant information. We hypothesize that, unlike global filtering, localized removal of ECG artifacts will not change the original EMG signals. We develop an approach to remove the ECG artifacts without altering the amplitude and frequency components of the EMG signal by using an externally recorded ECG signal as a mask to locate areas of the ECG spikes within EMG data. These segments containing ECG spikes were decomposed into 128 sub-wavelets by a custom-scaled Morlet Wavelet Transform. The ECG-related sub-wavelets at the ECG spike location were removed and a de-noised EMG signal was reconstructed. Validity of the proposed method was proven using mathematical simulated synthetic signals and EMG obtained from SCI patients. We compare the Root-mean Square Error and the Relative Change in Variance between this method, global, notch and adaptive filters. The results show that the localized wavelet-based filtering has the benefit of not introducing error in the native EMG signal and accurately removing ECG artifacts from EMG signals

Local Wavelet-Based Filtering of Electromyographic Signals to Eliminate the Electrocardiographic-Induced Artifacts in Patients with Spinal Cord Injury.

PubMed

Nitzken, Matthew; Bajaj, Nihit; Aslan, Sevda; Gimel'farb, Georgy; El-Baz, Ayman; Ovechkin, Alexander

2013-07-18

Surface Electromyography (EMG) is a standard method used in clinical practice and research to assess motor function in order to help with the diagnosis of neuromuscular pathology in human and animal models. EMG recorded from trunk muscles involved in the activity of breathing can be used as a direct measure of respiratory motor function in patients with spinal cord injury (SCI) or other disorders associated with motor control deficits. However, EMG potentials recorded from these muscles are often contaminated with heart-induced electrocardiographic (ECG) signals. Elimination of these artifacts plays a critical role in the precise measure of the respiratory muscle electrical activity. This study was undertaken to find an optimal approach to eliminate the ECG artifacts from EMG recordings. Conventional global filtering can be used to decrease the ECG-induced artifact. However, this method can alter the EMG signal and changes physiologically relevant information. We hypothesize that, unlike global filtering, localized removal of ECG artifacts will not change the original EMG signals. We develop an approach to remove the ECG artifacts without altering the amplitude and frequency components of the EMG signal by using an externally recorded ECG signal as a mask to locate areas of the ECG spikes within EMG data. These segments containing ECG spikes were decomposed into 128 sub-wavelets by a custom-scaled Morlet Wavelet Transform. The ECG-related sub-wavelets at the ECG spike location were removed and a de-noised EMG signal was reconstructed. Validity of the proposed method was proven using mathematical simulated synthetic signals and EMG obtained from SCI patients. We compare the Root-mean Square Error and the Relative Change in Variance between this method, global, notch and adaptive filters. The results show that the localized wavelet-based filtering has the benefit of not introducing error in the native EMG signal and accurately removing ECG artifacts from EMG signals.

Robust Features Of Surface Electromyography Signal

NASA Astrophysics Data System (ADS)

Sabri, M. I.; Miskon, M. F.; Yaacob, M. R.

2013-12-01

Nowadays, application of robotics in human life has been explored widely. Robotics exoskeleton system are one of drastically areas in recent robotic research that shows mimic impact in human life. These system have been developed significantly to be used for human power augmentation, robotics rehabilitation, human power assist, and haptic interaction in virtual reality. This paper focus on solving challenges in problem using neural signals and extracting human intent. Commonly, surface electromyography signal (sEMG) are used in order to control human intent for application exoskeleton robot. But the problem lies on difficulty of pattern recognition of the sEMG features due to high noises which are electrode and cable motion artifact, electrode noise, dermic noise, alternating current power line interface, and other noise came from electronic instrument. The main objective in this paper is to study the best features of electromyography in term of time domain (statistical analysis) and frequency domain (Fast Fourier Transform).The secondary objectives is to map the relationship between torque and best features of muscle unit activation potential (MaxPS and RMS) of biceps brachii. This project scope use primary data of 2 male sample subject which using same dominant hand (right handed), age between 20-27 years old, muscle diameter 32cm to 35cm and using single channel muscle (biceps brachii muscle). The experiment conduct 2 times repeated task of contraction and relaxation of biceps brachii when lifting different load from no load to 3kg with ascending 1kg The result shows that Fast Fourier Transform maximum power spectrum (MaxPS) has less error than mean value of reading compare to root mean square (RMS) value. Thus, Fast Fourier Transform maximum power spectrum (MaxPS) show the linear relationship against torque experience by elbow joint to lift different load. As the conclusion, the best features is MaxPS because it has the lowest error than other features and show

Objectivity and validity of EMG method in estimating anaerobic threshold.

PubMed

Kang, S-K; Kim, J; Kwon, M; Eom, H

2014-08-01

The purposes of this study were to verify and compare the performances of anaerobic threshold (AT) point estimates among different filtering intervals (9, 15, 20, 25, 30 s) and to investigate the interrelationships of AT point estimates obtained by ventilatory threshold (VT) and muscle fatigue thresholds using electromyographic (EMG) activity during incremental exercise on a cycle ergometer. 69 untrained male university students, yet pursuing regular exercise voluntarily participated in this study. The incremental exercise protocol was applied with a consistent stepwise increase in power output of 20 watts per minute until exhaustion. AT point was also estimated in the same manner using V-slope program with gas exchange parameters. In general, the estimated values of AT point-time computed by EMG method were more consistent across 5 filtering intervals and demonstrated higher correlations among themselves when compared with those values obtained by VT method. The results found in the present study suggest that the EMG signals could be used as an alternative or a new option in estimating AT point. Also the proposed computing procedure implemented in Matlab for the analysis of EMG signals appeared to be valid and reliable as it produced nearly identical values and high correlations with VT estimates. © Georg Thieme Verlag KG Stuttgart · New York.

Young, Healthy Subjects Can Reduce the Activity of Calf Muscles When Provided with EMG Biofeedback in Upright Stance

PubMed Central

Vieira, Taian M.; Baudry, Stéphane; Botter, Alberto

2016-01-01

Recent evidence suggests the minimization of muscular effort rather than of the size of bodily sway may be the primary, nervous system goal when regulating the human, standing posture. Different programs have been proposed for balance training; none however has been focused on the activation of postural muscles during standing. In this study we investigated the possibility of minimizing the activation of the calf muscles during standing through biofeedback. By providing subjects with an audio signal that varied in amplitude and frequency with the amplitude of surface electromyograms (EMG) recorded from different regions of the gastrocnemius and soleus muscles, we expected them to be able to minimize the level of muscle activation during standing without increasing the excursion of the center of pressure (CoP). CoP data and surface EMG from gastrocnemii, soleus and tibialis anterior muscles were obtained from 10 healthy participants while standing at ease and while standing with EMG biofeedback. Four sensitivities were used to test subjects' responsiveness to the EMG biofeedback. Compared with standing at ease, the two most sensitive feedback conditions induced a decrease in plantar flexor activity (~15%; P < 0.05) and an increase in tibialis anterior EMG (~10%; P < 0.05). Furthermore, CoP mean position significantly shifted backward (~30 mm). In contrast, the use of less sensitive EMG biofeedback resulted in a significant decrease in EMG activity of ankle plantar flexors with a marginal increase in TA activity compared with standing at ease. These changes were not accompanied by greater CoP displacements or significant changes in mean CoP position. Key results revealed subjects were able to keep standing stability while reducing the activity of gastrocnemius and soleus without loading their tibialis anterior muscle when standing with EMG biofeedback. These results may therefore posit the basis for the development of training protocols aimed at assisting subjects in

Validity and feasibility of the EMG direct observation tool (EMG-DOT).

PubMed

Leep Hunderfund, Andrea N; Rubin, Devon I; Laughlin, Ruple S; Sorenson, Eric J; Watson, James C; Jones, Lyell K; Juul, Dorthea; Park, Yoon Soo

2016-04-26

To develop a new workplace-based EMG direct observation tool (EMG-DOT) and gather validity evidence supporting its use for assessing electrodiagnostic skills among postgraduate medical trainees. The EMG-DOT was developed by experts using an iterative process. Validity evidence from content, response process, internal structure, relations to other variables, and consequences of testing was collected during the 2013-2014 academic year. Of 3,412 studies performed by trainees during the study period, 299 (9%) were assessed using the EMG-DOT. Of these, 203 (68%) involved a physician rater and 96 (32%) involved a technician rater. The 14-item EMG-DOT had excellent internal-consistency reliability (Cronbach α 0.94). Correlations between individual items and criterion-referenced global ratings of performance ranged from 0.36 to 0.72 (all p < 0.001). Mean total scores increased from 70% to 80% over 4 months of the EMG rotation (p < 0.001) despite a corresponding significant increase in case complexity (0.21-0.74 on a 3-point rating scale; p < 0.001). Trainees reported that the observational assessment exercise improved their knowledge or skills in 82% of encounters (188/230) and that feedback generated by the EMG-DOT improved the quality of care provided to patients in 58% (133/230). Trainees were "satisfied" or "very satisfied" with the observational assessment exercise in 96% of encounters (234/243). This study provides validity evidence supporting the use of EMG-DOT scores to assess electrodiagnostic skills of residents and fellows. The EMG-DOT can be used to inform milestone-based assessments of trainee performance in neurology, child neurology, physical medicine and rehabilitation, neuromuscular, and clinical neurophysiology training programs. © 2016 American Academy of Neurology.

The relationship between EMG activity and extensor moment generation in the erector spinae muscles during bending and lifting activities.

PubMed

Dolan, P; Adams, M A

1993-01-01

The relationship between EMG activity and extensor moment generation in the erector spinae muscles was investigated under isometric and concentric conditions. The full-wave rectified and averaged EMG signal was recorded from skin-surface electrodes located over the belly of the erector spinae at the levels of T10 and L3, and compared with measurements of extensor moment. The effects of muscle length and contraction velocity were studied by measuring the overall curvature (theta) and rate of change of curvature (d theta/dt) of the lumbar spine in the sagittal plane, using the '3-Space Isotrak' system. Isometric contractions were investigated with the subjects pulling up on a load cell attached to the floor. Hand height was varied to produce different amounts of lumbar flexion, as indicated by changes in lumbar curvature. The extensor moment was found to be linearly related to EMG activity, and the 'gradient' and 'intercept' of the relationship were themselves dependent upon the lumbar curvature at the time of testing. Concentric contractions were investigated with the subjects extending from a seated toe-touching position, at various speeds, while the torque exerted on the arm of a Cybex dynamometer was continuously measured. Under these conditions the EMG signal (E) was higher than the isometric signal (E0) associated with the same torque. E and E0 were related as follows: E0 = E/(1 + A d theta/dt), where A = 0.0014 exp (0.045P) and P = percentage lumbar flexion. This equation was used to correct the EMG data for the effect of contraction velocity. The corrected data were then used, in conjunction with the results of the isometric calibrations, to calculate the extensor moment generated by the erector spinae muscles during bending and lifting activities. The extensor moment can itself be used to calculate the compressive force acting on the lumbar spine.

sEMG-based joint force control for an upper-limb power-assist exoskeleton robot.

PubMed

Li, Zhijun; Wang, Baocheng; Sun, Fuchun; Yang, Chenguang; Xie, Qing; Zhang, Weidong

2014-05-01

This paper investigates two surface electromyogram (sEMG)-based control strategies developed for a power-assist exoskeleton arm. Different from most of the existing position control approaches, this paper develops force control methods to make the exoskeleton robot behave like humans in order to provide better assistance. The exoskeleton robot is directly attached to a user's body and activated by the sEMG signals of the user's muscles, which reflect the user's motion intention. In the first proposed control method, the forces of agonist and antagonist muscles pair are estimated, and their difference is used to produce the torque of the corresponding joints. In the second method, linear discriminant analysis-based classifiers are introduced as the indicator of the motion type of the joints. Then, the classifier's outputs together with the estimated force of corresponding active muscle determine the torque control signals. Different from the conventional approaches, one classifier is assigned to each joint, which decreases the training time and largely simplifies the recognition process. Finally, the extensive experiments are conducted to illustrate the effectiveness of the proposed approaches.

Calibration of EMG to force for knee muscles is applicable with submaximal voluntary contractions.

PubMed

Doorenbosch, Caroline A M; Joosten, Annemiek; Harlaar, Jaap

2005-08-01

In this study, the influence of using submaximal isokinetic contractions about the knee compared to maximal voluntary contractions as input to obtain the calibration of an EMG-force model for knee muscles is investigated. Isokinetic knee flexion and extension contractions were performed by healthy subjects at five different velocities and at three contraction levels (100%, 75% and 50% of MVC). Joint angle, angular velocity, joint moment and surface EMG of five knee muscles were recorded. Individual calibration values were calculated according to [C.A.M. Doorenbosch, J. Harlaar, A clinically applicable EMG-force model to quantify active stabilization of the knee after a lesion of the anterior cruciate ligament, Clinical Biomechanics 18 (2003) 142-149] for each contraction level. First, the output of the model, calibrated with the 100% MVC was compared to the actually exerted net knee moment at the dynamometer. Normalized root mean square errors were calculated [A.L. Hof, C.A.N. Pronk, J.A. van Best, Comparison between EMG to force processing and kinetic analysis for the calf muscle moment in walking and stepping, Journal of Biomechanics 20 (1987) 167-187] to compare the estimated moments with the actually exerted moments. Mean RMSD errors ranged from 0.06 to 0.21 for extension and from 0.12 to 0.29 for flexion at the 100% trials. Subsequently, the calibration results of the 50% and 75% MVC calibration procedures were used. A standard signal, representing a random EMG level was used as input in the EMG force model, to compare the three models. Paired samples t-tests between the 100% MVC and the 75% MVC and 50% MVC, respectively, showed no significant differences (p>0.05). The application of submaximal contractions of larger than 50% MVC is suitable to calibrate a simple EMG to force model for knee extension and flexion. This means that in clinical practice, the EMG to force model can be applied by patients who cannot exert maximal force.

EMG-Based Estimation of Limb Movement Using Deep Learning With Recurrent Convolutional Neural Networks.

PubMed

Xia, Peng; Hu, Jie; Peng, Yinghong

2017-10-25

A novel model based on deep learning is proposed to estimate kinematic information for myoelectric control from multi-channel electromyogram (EMG) signals. The neural information of limb movement is embedded in EMG signals that are influenced by all kinds of factors. In order to overcome the negative effects of variability in signals, the proposed model employs the deep architecture combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The EMG signals are transformed to time-frequency frames as the input to the model. The limb movement is estimated by the model that is trained with the gradient descent and backpropagation procedure. We tested the model for simultaneous and proportional estimation of limb movement in eight healthy subjects and compared it with support vector regression (SVR) and CNNs on the same data set. The experimental studies show that the proposed model has higher estimation accuracy and better robustness with respect to time. The combination of CNNs and RNNs can improve the model performance compared with using CNNs alone. The model of deep architecture is promising in EMG decoding and optimization of network structures can increase the accuracy and robustness. © 2017 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.

An ICA-EBM-Based sEMG Classifier for Recognizing Lower Limb Movements in Individuals With and Without Knee Pathology.

PubMed

Naik, Ganesh R; Selvan, S Easter; Arjunan, Sridhar P; Acharyya, Amit; Kumar, Dinesh K; Ramanujam, Arvind; Nguyen, Hung T

2018-03-01

Surface electromyography (sEMG) data acquired during lower limb movements has the potential for investigating knee pathology. Nevertheless, a major challenge encountered with sEMG signals generated by lower limb movements is the intersubject variability, because the signals recorded from the leg or thigh muscles are contingent on the characteristics of a subject such as gait activity and muscle structure. In order to cope with this difficulty, we have designed a three-step classification scheme. First, the multichannel sEMG is decomposed into activities of the underlying sources by means of independent component analysis via entropy bound minimization. Next, a set of time-domain features, which would best discriminate various movements, are extracted from the source estimates. Finally, the feature selection is performed with the help of the Fisher score and a scree-plot-based statistical technique, prior to feeding the dimension-reduced features to the linear discriminant analysis. The investigation involves 11 healthy subjects and 11 individuals with knee pathology performing three different lower limb movements, namely, walking, sitting, and standing, which yielded an average classification accuracy of 96.1% and 86.2%, respectively. While the outcome of this study per se is very encouraging, with suitable improvement, the clinical application of such an sEMG-based pattern recognition system that distinguishes healthy and knee pathological subjects would be an attractive consequence.

Real-time simultaneous and proportional myoelectric control using intramuscular EMG

NASA Astrophysics Data System (ADS)

Smith, Lauren H.; Kuiken, Todd A.; Hargrove, Levi J.

2014-12-01

Objective. Myoelectric prostheses use electromyographic (EMG) signals to control movement of prosthetic joints. Clinically available myoelectric control strategies do not allow simultaneous movement of multiple degrees of freedom (DOFs); however, the use of implantable devices that record intramuscular EMG signals could overcome this constraint. The objective of this study was to evaluate the real-time simultaneous control of three DOFs (wrist rotation, wrist flexion/extension, and hand open/close) using intramuscular EMG. Approach. We evaluated task performance of five able-bodied subjects in a virtual environment using two control strategies with fine-wire EMG: (i) parallel dual-site differential control, which enabled simultaneous control of three DOFs and (ii) pattern recognition control, which required sequential control of DOFs. Main results. Over the course of the experiment, subjects using parallel dual-site control demonstrated increased use of simultaneous control and improved performance in a Fitts’ Law test. By the end of the experiment, performance using parallel dual-site control was significantly better (up to a 25% increase in throughput) than when using sequential pattern recognition control for tasks requiring multiple DOFs. The learning trends with parallel dual-site control suggested that further improvements in performance metrics were possible. Subjects occasionally experienced difficulty in performing isolated single-DOF movements with parallel dual-site control but were able to accomplish related Fitts’ Law tasks with high levels of path efficiency. Significance. These results suggest that intramuscular EMG, used in a parallel dual-site configuration, can provide simultaneous control of a multi-DOF prosthetic wrist and hand and may outperform current methods that enforce sequential control.

Learning an EMG Controlled Game: Task-Specific Adaptations and Transfer

PubMed Central

van Dijk, Ludger; van der Sluis, Corry K.; van Dijk, Hylke W.; Bongers, Raoul M.

2016-01-01

Video games that aim to improve myoelectric control (myogames) are gaining popularity and are often part of the rehabilitation process following an upper limb amputation. However, direct evidence for their effect on prosthetic skill is limited. This study aimed to determine whether and how myogaming improves EMG control and whether performance improvements transfer to a prosthesis-simulator task. Able-bodied right-handed participants (N = 28) were randomly assigned to 1 of 2 groups. The intervention group was trained to control a video game (Breakout-EMG) using the myosignals of wrist flexors and extensors. Controls played a regular Mario computer game. Both groups trained 20 minutes a day for 4 consecutive days. Before and after training, two tests were conducted: one level of the Breakout-EMG game, and grasping objects with a prosthesis-simulator. Results showed a larger increase of in-game accuracy for the Breakout-EMG group than for controls. The Breakout-EMG group moreover showed increased adaptation of the EMG signal to the game. No differences were found in using a prosthesis-simulator. This study demonstrated that myogames lead to task-specific myocontrol skills. Transfer to a prosthesis task is therefore far from easy. We discuss several implications for future myogame designs. PMID:27556154

Learning an EMG Controlled Game: Task-Specific Adaptations and Transfer.

PubMed

van Dijk, Ludger; van der Sluis, Corry K; van Dijk, Hylke W; Bongers, Raoul M

2016-01-01

Video games that aim to improve myoelectric control (myogames) are gaining popularity and are often part of the rehabilitation process following an upper limb amputation. However, direct evidence for their effect on prosthetic skill is limited. This study aimed to determine whether and how myogaming improves EMG control and whether performance improvements transfer to a prosthesis-simulator task. Able-bodied right-handed participants (N = 28) were randomly assigned to 1 of 2 groups. The intervention group was trained to control a video game (Breakout-EMG) using the myosignals of wrist flexors and extensors. Controls played a regular Mario computer game. Both groups trained 20 minutes a day for 4 consecutive days. Before and after training, two tests were conducted: one level of the Breakout-EMG game, and grasping objects with a prosthesis-simulator. Results showed a larger increase of in-game accuracy for the Breakout-EMG group than for controls. The Breakout-EMG group moreover showed increased adaptation of the EMG signal to the game. No differences were found in using a prosthesis-simulator. This study demonstrated that myogames lead to task-specific myocontrol skills. Transfer to a prosthesis task is therefore far from easy. We discuss several implications for future myogame designs.

An Investigative Redesign of the ECG and EMG Signal Conditioning Circuits for Two-fault Tolerance and Circuit Improvement

NASA Technical Reports Server (NTRS)

Obrien, Edward M.

1991-01-01

An investigation was undertaken to make the elctrocardiography (ECG) and the electromyography (EMG) signal conditioning circuits two-fault tolerant and to update the circuitry. The present signal conditioning circuits provide at least one level of subject protection against electrical shock hazard but at a level of 100 micro-A (for voltages of up to 200 V). However, it is necessary to provide catastrophic fault tolerance protection for the astronauts and to provide protection at a current level of less that 100 micro-A. For this study, protection at the 10 micro-A level was sought. This is the generally accepted value below which no possibility of microshock exists. Only the possibility of macroshock exists in the case of the signal conditioners. However, this extra amount of protection is desirable. The initial part deals with current limiter circuits followed by an investigation into the signal conditioner specifications and circuit design.

Elbow joint angle and elbow movement velocity estimation using NARX-multiple layer perceptron neural network model with surface EMG time domain parameters.

PubMed

Raj, Retheep; Sivanandan, K S

2017-01-01

Estimation of elbow dynamics has been the object of numerous investigations. In this work a solution is proposed for estimating elbow movement velocity and elbow joint angle from Surface Electromyography (SEMG) signals. Here the Surface Electromyography signals are acquired from the biceps brachii muscle of human hand. Two time-domain parameters, Integrated EMG (IEMG) and Zero Crossing (ZC), are extracted from the Surface Electromyography signal. The relationship between the time domain parameters, IEMG and ZC with elbow angular displacement and elbow angular velocity during extension and flexion of the elbow are studied. A multiple input-multiple output model is derived for identifying the kinematics of elbow. A Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural network (MLPNN) model is proposed for the estimation of elbow joint angle and elbow angular velocity. The proposed NARX MLPNN model is trained using Levenberg-marquardt based algorithm. The proposed model is estimating the elbow joint angle and elbow movement angular velocity with appreciable accuracy. The model is validated using regression coefficient value (R). The average regression coefficient value (R) obtained for elbow angular displacement prediction is 0.9641 and for the elbow anglular velocity prediction is 0.9347. The Nonlinear Auto Regressive with eXogenous inputs (NARX) structure based multiple layer perceptron neural networks (MLPNN) model can be used for the estimation of angular displacement and movement angular velocity of the elbow with good accuracy.

EMGD-FE: an open source graphical user interface for estimating isometric muscle forces in the lower limb using an EMG-driven model.

PubMed

Menegaldo, Luciano Luporini; de Oliveira, Liliam Fernandes; Minato, Kin K

2014-04-04

This paper describes the "EMG Driven Force Estimator (EMGD-FE)", a Matlab® graphical user interface (GUI) application that estimates skeletal muscle forces from electromyography (EMG) signals. Muscle forces are obtained by numerically integrating a system of ordinary differential equations (ODEs) that simulates Hill-type muscle dynamics and that utilises EMG signals as input. In the current version, the GUI can estimate the forces of lower limb muscles executing isometric contractions. Muscles from other parts of the body can be tested as well, although no default values for model parameters are provided. To achieve accurate evaluations, EMG collection is performed simultaneously with torque measurement from a dynamometer. The computer application guides the user, step-by-step, to pre-process the raw EMG signals, create inputs for the muscle model, numerically integrate the ODEs and analyse the results. An example of the application's functions is presented using the quadriceps femoris muscle. Individual muscle force estimations for the four components as well the knee isometric torque are shown. The proposed GUI can estimate individual muscle forces from EMG signals of skeletal muscles. The estimation accuracy depends on several factors, including signal collection and modelling hypothesis issues.

Separation of electrocardiographic from electromyographic signals using dynamic filtration.

PubMed

Christov, Ivaylo; Raikova, Rositsa; Angelova, Silvija

2018-07-01

Trunk muscle electromyographic (EMG) signals are often contaminated by the electrical activity of the heart. During low or moderate muscle force, these electrocardiographic (ECG) signals disturb the estimation of muscle activity. Butterworth high-pass filters with cut-off frequency of up to 60 Hz are often used to suppress the ECG signal. Such filters disturb the EMG signal in both frequency and time domain. A new method based on the dynamic application of Savitzky-Golay filter is proposed. EMG signals of three left trunk muscles and pure ECG signal were recorded during different motor tasks. The efficiency of the method was tested and verified both with the experimental EMG signals and with modeled signals obtained by summing the pure ECG signal with EMG signals at different levels of signal-to-noise ratio. The results were compared with those obtained by application of high-pass, 4th order Butterworth filter with cut-off frequency of 30 Hz. The suggested method is separating the EMG signal from the ECG signal without EMG signal distortion across its entire frequency range regardless of amplitudes. Butterworth filter suppresses the signals in the 0-30 Hz range thus preventing the low-frequency analysis of the EMG signal. An additional disadvantage is that it passes high-frequency ECG signal components which is apparent at equal and higher amplitudes of the ECG signal as compared to the EMG signal. The new method was also successfully verified with abnormal ECG signals. Copyright © 2018. Published by Elsevier Ltd.

Real-time simultaneous and proportional myoelectric control using intramuscular EMG

PubMed Central

Kuiken, Todd A; Hargrove, Levi J

2014-01-01

Objective Myoelectric prostheses use electromyographic (EMG) signals to control movement of prosthetic joints. Clinically available myoelectric control strategies do not allow simultaneous movement of multiple degrees of freedom (DOFs); however, the use of implantable devices that record intramuscular EMG signals could overcome this constraint. The objective of this study was to evaluate the real-time simultaneous control of three DOFs (wrist rotation, wrist flexion/extension, and hand open/close) using intramuscular EMG. Approach We evaluated task performance of five able-bodied subjects in a virtual environment using two control strategies with fine-wire EMG: (i) parallel dual-site differential control, which enabled simultaneous control of three DOFs and (ii) pattern recognition control, which required sequential control of DOFs. Main Results Over the course of the experiment, subjects using parallel dual-site control demonstrated increased use of simultaneous control and improved performance in a Fitts' Law test. By the end of the experiment, performance using parallel dual-site control was significantly better (up to a 25% increase in throughput) than when using sequential pattern recognition control for tasks requiring multiple DOFs. The learning trends with parallel dual-site control suggested that further improvements in performance metrics were possible. Subjects occasionally experienced difficulty in performing isolated single-DOF movements with parallel dual-site control but were able to accomplish related Fitts' Law tasks with high levels of path efficiency. Significance These results suggest that intramuscular EMG, used in a parallel dual-site configuration, can provide simultaneous control of a multi-DOF prosthetic wrist and hand and may outperform current methods that enforce sequential control. PMID:25394366

EMGD-FE: an open source graphical user interface for estimating isometric muscle forces in the lower limb using an EMG-driven model

PubMed Central

2014-01-01

Background This paper describes the “EMG Driven Force Estimator (EMGD-FE)”, a Matlab® graphical user interface (GUI) application that estimates skeletal muscle forces from electromyography (EMG) signals. Muscle forces are obtained by numerically integrating a system of ordinary differential equations (ODEs) that simulates Hill-type muscle dynamics and that utilises EMG signals as input. In the current version, the GUI can estimate the forces of lower limb muscles executing isometric contractions. Muscles from other parts of the body can be tested as well, although no default values for model parameters are provided. To achieve accurate evaluations, EMG collection is performed simultaneously with torque measurement from a dynamometer. The computer application guides the user, step-by-step, to pre-process the raw EMG signals, create inputs for the muscle model, numerically integrate the ODEs and analyse the results. Results An example of the application’s functions is presented using the quadriceps femoris muscle. Individual muscle force estimations for the four components as well the knee isometric torque are shown. Conclusions The proposed GUI can estimate individual muscle forces from EMG signals of skeletal muscles. The estimation accuracy depends on several factors, including signal collection and modelling hypothesis issues. PMID:24708668

Neuromuscular interfacing: establishing an EMG-driven model for the human elbow joint.

PubMed

Pau, James W L; Xie, Shane S Q; Pullan, Andrew J

2012-09-01

Assistive devices aim to mitigate the effects of physical disability by aiding users to move their limbs or by rehabilitating through therapy. These devices are commonly embodied by robotic or exoskeletal systems that are still in development and use the electromyographic (EMG) signal to determine user intent. Not much focus has been placed on developing a neuromuscular interface (NI) that solely relies on the EMG signal, and does not require modifications to the end user's state to enhance the signal (such as adding weights). This paper presents the development of a flexible, physiological model for the elbow joint that is leading toward the implementation of an NI, which predicts joint motion from EMG signals for both able-bodied and less-abled users. The approach uses musculotendon models to determine muscle contraction forces, a proposed musculoskeletal model to determine total joint torque, and a kinematic model to determine joint rotational kinematics. After a sensitivity analysis and tuning using genetic algorithms, subject trials yielded an average root-mean-square error of 6.53° and 22.4° for a single cycle and random cycles of movement of the elbow joint, respectively. This helps us to validate the elbow model and paves the way toward the development of an NI.

Characteristics of power spectrum density function of EMG during muscle contraction below 30%MVC.

PubMed

Roman-Liu, Danuta; Konarska, Maria

2009-10-01

The aim of the study was to quantify changes in PSDF frequency bands of the EMG signal and EMG parameters such as MF, MPF and zero crossing, with an increase in the level of muscle contractions in the range from 0.5% to 30% RMS(max) and to determine the frequency bands with the lowest dependency on RMS level so that this could be used in investigating muscle fatigue. Sixteen men, aged from 23 to 33 years old (mean 26.1), who participated in the study performed two force exertion tests. Fragments of EMG which corresponded to the levels of muscle contraction of 0.5%, 1%, 2.5%, 5%, 10%, 15%, 20%, 25%, 30% RMS(max) registered from left and right trapezius pars descendents (TP) and left and right extensor digitorum superficialis (ED) muscles were selected for analysis. The analysis included changes in standard parameters of the EMG signal and changes in PSDF frequency bands, which occurred across muscle contraction levels. To analyze changes in PSDF across the level of muscle contraction, the spectrum was divided into six frequency bandwidths. The analysis of parameters focused on the differences in those parameters between the analyzed muscles, at different levels of muscle contraction. The study revealed that, at muscle contraction levels below 5% RMSmax, contraction level influences standard parameters of the EMG signal and that at such levels of muscle contraction every change in muscle contraction level (recruitment of additional MUs) is reflected in PSDF. The frequency band with the lowest dependency on contraction level was 76-140 Hz for which in both muscles no contraction level effect was detected for contraction levels above 5% RMS(max). The reproducibility of the results was very high, since the observations in of the left and right muscles were almost equal. The other factor, which strongly influences PSDF of the EMG signal, is probably the examined muscle structure (muscle morphology, size, function, subcutaneous layer, cross talk). It seems that low

Design of sEMG assembly to detect external anal sphincter activity: a proof of concept.

PubMed

Shiraz, Arsam; Leaker, Brian; Mosse, Charles Alexander; Solomon, Eskinder; Craggs, Michael; Demosthenous, Andreas

2017-10-31

Conditional trans-rectal stimulation of the pudendal nerve could provide a viable solution to treat hyperreflexive bladder in spinal cord injury. A set threshold of the amplitude estimate of the external anal sphincter surface electromyography (sEMG) may be used as the trigger signal. The efficacy of such a device should be tested in a large scale clinical trial. As such, a probe should remain in situ for several hours while patients attend to their daily routine; the recording electrodes should be designed to be large enough to maintain good contact while observing design constraints. The objective of this study was to arrive at a design for intra-anal sEMG recording electrodes for the subsequent clinical trials while deriving the possible recording and processing parameters. Having in mind existing solutions and based on theoretical and anatomical considerations, a set of four multi-electrode probes were designed and developed. These were tested in a healthy subject and the measured sEMG traces were recorded and appropriately processed. It was shown that while comparatively large electrodes record sEMG traces that are not sufficiently correlated with the external anal sphincter contractions, smaller electrodes may not maintain a stable electrode tissue contact. It was shown that 3 mm wide and 1 cm long electrodes with 5 mm inter-electrode spacing, in agreement with Nyquist sampling, placed 1 cm from the orifice may intra-anally record a sEMG trace sufficiently correlated with external anal sphincter activity. The outcome of this study can be used in any biofeedback, treatment or diagnostic application where the activity of the external anal sphincter sEMG should be detected for an extended period of time.

Electromyographic signal and force comparisons during maximal voluntary isometric contraction in water and on dry land.

PubMed

Pinto, Stephanie Santana; Liedtke, Giane Veiga; Alberton, Cristine Lima; da Silva, Eduardo Marczwski; Cadore, Eduardo Lusa; Kruel, Luiz Fernando Martins

2010-11-01

This study was designed to compare surface electromyographic (sEMG) signal and force production during maximal voluntary isometric contractions (MVCs) in water and on dry land. The reproducibility of sEMG and isometric force measurements between water and dry land environments was also assessed. Nine women performed MVC for elbow flexion and extension, hip flexion, and extension against identical fixed resistance in both environments. The sEMG signal from biceps brachii, triceps brachii, rectus femoris, and biceps femoris was recorded with waterproof adhesives placed over each electrode. The sEMG and force production showed no significant difference between water and dry land, except for HEX (p = 0.035). In addition, intraclass correlation coefficient values were significant and ranged from moderate to high (0.66-0.96) for sEMG and force production between environments. These results showed that the environment did not influence the sEMG and force in MVC.

A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition.

PubMed

Benatti, Simone; Casamassima, Filippo; Milosevic, Bojan; Farella, Elisabetta; Schönle, Philipp; Fateh, Schekeb; Burger, Thomas; Huang, Qiuting; Benini, Luca

2015-10-01

Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery.

Heart rate variability (HRV) and muscular system activity (EMG) in cases of crash threat during simulated driving of a passenger car.

PubMed

Zużewicz, Krystyna; Roman-Liu, Danuta; Konarska, Maria; Bartuzi, Paweł; Matusiak, Krzysztof; Korczak, Dariusz; Lozia, Zbigniew; Guzek, Marek

2013-10-01

The aim of the study was to verify whether simultaneous responses from the muscular and circulatory system occur in the driver's body under simulated conditions of a crash threat. The study was carried out in a passenger car driving simulator. The crash was included in the driving test scenario developed in an urban setting. In the group of 22 young male subjects, two physiological signals - ECG and EMG were continuously recorded. The length of the RR interval in the ECG signal was assessed. A HRV analysis was performed in the time and frequency domains for 1-minute record segments at rest (seated position), during undisturbed driving as well as during and several minutes after the crash. For the left and right side muscles: m. trapezius (TR) and m. flexor digitorum superficialis (FDS), the EMG signal amplitude was determined. The percentage of maximal voluntary contraction (MVC) was compared during driving and during the crash. As for the ECG signal, it was found that in most of the drivers changes occurred in the parameter values reflecting HRV in the time domain. Significant changes were noted in the mean length of RR intervals (mRR). As for the EMG signal, the changes in the amplitude concerned the signal recorded from the FDS muscle. The changes in ECG and EMG were simultaneous in half of the cases. Such parameters as mRR (ECG signal) and FDS-L amplitude (EMG signal) were the responses to accident risk. Under simulated conditions, responses from the circulatory and musculoskeletal systems are not always simultaneous. The results indicate that a more complete driver's response to a crash in road traffic is obtained based on parallel recording of two physiological signals (ECG and EMG).

EMG analysis tuned for determining the timing and level of activation in different motor units

PubMed Central

Lee, Sabrina S.M.; de Boef Miara, Maria; Arnold, Allison S.; Biewener, Andrew A.; Wakeling, James M.

2011-01-01

Recruitment patterns and activation dynamics of different motor units greatly influence the temporal pattern and magnitude of muscle force development, yet these features are not often considered in muscle models. The purpose of this study was to characterize the recruitment and activation dynamics of slow and fast motor units from electromyographic (EMG) recordings and twitch force profiles recorded directly from animal muscles. EMG and force data from the gastrocnemius muscles of seven goats were recorded during in vivo tendon-tap reflex and in situ nerve stimulation experiments. These experiments elicited EMG signals with significant differences in frequency content (p<0.001). The frequency content was characterized using wavelet and principal components analysis, and optimized wavelets with centre frequencies, 149.94Hz and 323.13Hz, were obtained. The optimized wavelets were used to calculate the EMG intensities and, with the reconstructed twitch force profiles, to derive transfer functions for slow and fast motor units that estimate the activation state of the muscle from the EMG signal. The resulting activation-deactivation time constants gave r values of 0.98 to 0.99 between the activation state and the force profiles. This work establishes a framework for developing improved muscle models that consider the intrinsic properties of slow and fast fibres within a mixed muscle, and that can more accurately predict muscle force output from EMG. PMID:21570317

EMG analysis tuned for determining the timing and level of activation in different motor units.

PubMed

Lee, Sabrina S M; Miara, Maria de Boef; Arnold, Allison S; Biewener, Andrew A; Wakeling, James M

2011-08-01

Recruitment patterns and activation dynamics of different motor units greatly influence the temporal pattern and magnitude of muscle force development, yet these features are not often considered in muscle models. The purpose of this study was to characterize the recruitment and activation dynamics of slow and fast motor units from electromyographic (EMG) recordings and twitch force profiles recorded directly from animal muscles. EMG and force data from the gastrocnemius muscles of seven goats were recorded during in vivo tendon-tap reflex and in situ nerve stimulation experiments. These experiments elicited EMG signals with significant differences in frequency content (p<0.001). The frequency content was characterized using wavelet and principal components analysis, and optimized wavelets with centre frequencies, 149.94 Hz and 323.13 Hz, were obtained. The optimized wavelets were used to calculate the EMG intensities and, with the reconstructed twitch force profiles, to derive transfer functions for slow and fast motor units that estimate the activation state of the muscle from the EMG signal. The resulting activation-deactivation time constants gave r values of 0.98-0.99 between the activation state and the force profiles. This work establishes a framework for developing improved muscle models that consider the intrinsic properties of slow and fast fibres within a mixed muscle, and that can more accurately predict muscle force output from EMG. Copyright © 2011 Elsevier Ltd. All rights reserved.

EMG Processing Based Measures of Fatigue Assessment during Manual Lifting.

PubMed

Shair, E F; Ahmad, S A; Marhaban, M H; Mohd Tamrin, S B; Abdullah, A R

2017-01-01

Manual lifting is one of the common practices used in the industries to transport or move objects to a desired place. Nowadays, even though mechanized equipment is widely available, manual lifting is still considered as an essential way to perform material handling task. Improper lifting strategies may contribute to musculoskeletal disorders (MSDs), where overexertion contributes as the highest factor. To overcome this problem, electromyography (EMG) signal is used to monitor the workers' muscle condition and to find maximum lifting load, lifting height and number of repetitions that the workers are able to handle before experiencing fatigue to avoid overexertion. Past researchers have introduced several EMG processing techniques and different EMG features that represent fatigue indices in time, frequency, and time-frequency domain. The impact of EMG processing based measures in fatigue assessment during manual lifting are reviewed in this paper. It is believed that this paper will greatly benefit researchers who need a bird's eye view of the biosignal processing which are currently available, thus determining the best possible techniques for lifting applications.

EMG Processing Based Measures of Fatigue Assessment during Manual Lifting

PubMed Central

Marhaban, M. H.; Abdullah, A. R.

2017-01-01

Manual lifting is one of the common practices used in the industries to transport or move objects to a desired place. Nowadays, even though mechanized equipment is widely available, manual lifting is still considered as an essential way to perform material handling task. Improper lifting strategies may contribute to musculoskeletal disorders (MSDs), where overexertion contributes as the highest factor. To overcome this problem, electromyography (EMG) signal is used to monitor the workers' muscle condition and to find maximum lifting load, lifting height and number of repetitions that the workers are able to handle before experiencing fatigue to avoid overexertion. Past researchers have introduced several EMG processing techniques and different EMG features that represent fatigue indices in time, frequency, and time-frequency domain. The impact of EMG processing based measures in fatigue assessment during manual lifting are reviewed in this paper. It is believed that this paper will greatly benefit researchers who need a bird's eye view of the biosignal processing which are currently available, thus determining the best possible techniques for lifting applications. PMID:28303251

Is child walking conditioned by gender? Surface EMG patterns in female and male children.

PubMed

Di Nardo, Francesco; Laureati, Giulio; Strazza, Annachiara; Mengarelli, Alessandro; Burattini, Laura; Agostini, Valentina; Nascimbeni, Alberto; Knaflitz, Marco; Fioretti, Sandro

2017-03-01

EMG-based differences between females and males during walking are generally acknowledged in adults. Aim of the study was the quantification of possible gender differences in myoelectric activity of gastrocnemius lateralis (GL) and tibialis anterior (TA) during walking in school-age children. Gender-related comparison with adults was also provided to get possible novel insight in maturation of gait. To this aim, Statistical gait analysis, a recent methodology performing a statistical characterization of gait by averaging spatial-temporal and surface-EMG-based parameters over hundreds of strides, was performed in100 healthy school-age children (C-group) and in 33 healthy young adults (YA-group). On average, 301±110 consecutive strides were analyzed for each subject. In C-group, no significant differences (p>0.05) were observed between females and males in GL and TA, considering mean onset/offset instants of activation and occurrence frequency. Stratifying the C-group for age, small differences between females and males in occurrence frequency of GL arose in oldest children. In YA-group, females showed a significant propensity for a more complex recruitment of TA and GL (higher number of activations during gait cycle, quantified by occurrence frequency) compared to males. These outcomes suggest that gender-related differences in sEMG parameters do not characterize the recruitment of GL and TA during child walking in early years (6-8 years), start occurring when adolescence is approaching (10-12 years), and are acknowledged in both ankle muscles only in adults. Present findings seem to support previous studies on maturation of gait which indicate adolescence as the time-range where gait is completing its maturation path. Copyright © 2017 Elsevier B.V. All rights reserved.

EMG of the hip adductor muscles in six clinical examination tests.

PubMed

Lovell, Gregory A; Blanch, Peter D; Barnes, Christopher J

2012-08-01

To assess activation of muscles of hip adduction using EMG and force analysis during standard clinical tests, and compare athletes with and without a prior history of groin pain. Controlled laboratory study. 21 male athletes from an elite junior soccer program. Bilateral surface EMG recordings of the adductor magnus, adductor longus, gracilis and pectineus as well as a unilateral fine-wire EMG of the pectineus were made during isometric holds in six clinical examination tests. A load cell was used to measure force data. Test type was a significant factor in the EMG output for all four muscles (all muscles p < 0.01). EMG activation was highest in Hips 0 or Hips 45 for adductor magnus, adductor longus and gracilis. EMG activation for pectineus was highest in Hips 90. Injury history was a significant factor in the EMG output for the adductor longus (p < 0.05), pectineus (p < 0.01) and gracilis (p < 0.01) but not adductor magnus. For force data, clinical test type was a significant factor (p < 0.01) with Hips 0 being significantly stronger than Hips 45, Hips 90 and Side lay. BMI (body mass index) was a significant factor (p < 0.01) for producing a higher force. All other factors had no significant effect on the force outputs. Hip adduction strength assessment is best measured at hips 0 (which produced most force) or 45° flexion (which generally gave the highest EMG output). Muscle EMG varied significantly with clinical test position. Athletes with previous groin injury had a significant fall in some EMG outputs. Copyright © 2011 Elsevier Ltd. All rights reserved.

Paralyzed subject controls telepresence mobile robot using novel sEMG brain-computer interface: case study.

PubMed

Lyons, Kenneth R; Joshi, Sanjay S

2013-06-01

Here we demonstrate the use of a new singlesignal surface electromyography (sEMG) brain-computer interface (BCI) to control a mobile robot in a remote location. Previous work on this BCI has shown that users are able to perform cursor-to-target tasks in two-dimensional space using only a single sEMG signal by continuously modulating the signal power in two frequency bands. Using the cursor-to-target paradigm, targets are shown on the screen of a tablet computer so that the user can select them, commanding the robot to move in different directions for a fixed distance/angle. A Wifi-enabled camera transmits video from the robot's perspective, giving the user feedback about robot motion. Current results show a case study with a C3-C4 spinal cord injury (SCI) subject using a single auricularis posterior muscle site to navigate a simple obstacle course. Performance metrics for operation of the BCI as well as completion of the telerobotic command task are developed. It is anticipated that this noninvasive and mobile system will open communication opportunities for the severely paralyzed, possibly using only a single sensor.

[Multi-channel motion signal acquisition system and experimental results].

PubMed

Zhong, Sheng; Yi, Wanguan; Deng, Ke; Zhan, Kai; Wen, Huiying; Chen, Xin

2014-09-01

For the study of muscle function and features during exercise, a multi-channel data acquisition system was developed, the overall design of the system, hardware composition, the function of system and so on have made a detail implements. The synchronous acquisition and storage of the surface EMG signal, joint angle signal, plantar pressure signal, ultrasonic image and initial results have been achieved.

Characterization of Volitional Electromyographic Signals in the Lower Extremity After Motor Complete Spinal Cord Injury.

PubMed

Heald, Elizabeth; Hart, Ronald; Kilgore, Kevin; Peckham, P Hunter

2017-06-01

Previous studies have demonstrated the presence of intact axons across a spinal cord lesion, even in those clinically diagnosed with complete spinal cord injury (SCI). These axons may allow volitional motor signals to be transmitted through the injury, even in the absence of visible muscle contraction. To demonstrate the presence of volitional electromyographic (EMG) activity below the lesion in motor complete SCI and to characterize this activity to determine its value for potential use as a neuroprosthetic command source. Twenty-four subjects with complete (AIS A or B), chronic, cervical SCI were tested for the presence of volitional below-injury EMG activity. Surface electrodes recorded from 8 to 12 locations of each lower limb, while participants were asked to attempt specific movements of the lower extremity in response to visual and audio cues. EMG trials were ranked through visual inspection, and were scored using an amplitude threshold algorithm to identify channels of interest with volitional motor unit activity. Significant below-injury muscle activity was identified through visual inspection in 16 of 24 participants, and visual inspection rankings were well correlated to the algorithm scoring. The surface EMG protocol utilized here is relatively simple and noninvasive, ideal for a clinical screening tool. The majority of subjects tested were able to produce a volitional EMG signal below their injury level, and the algorithm developed allows automatic identification of signals of interest. The presence of this volitional activity in the lower extremity could provide an innovative new command signal source for implanted neuroprostheses or other assistive technology.

The Effectiveness of FES-Evoked EMG Potentials to Assess Muscle Force and Fatigue in Individuals with Spinal Cord Injury

PubMed Central

Ibitoye, Morufu Olusola; Estigoni, Eduardo H.; Hamzaid, Nur Azah; Wahab, Ahmad Khairi Abdul; Davis, Glen M.

2014-01-01

The evoked electromyographic signal (eEMG) potential is the standard index used to monitor both electrical changes within the motor unit during muscular activity and the electrical patterns during evoked contraction. However, technical and physiological limitations often preclude the acquisition and analysis of the signal especially during functional electrical stimulation (FES)-evoked contractions. Hence, an accurate quantification of the relationship between the eEMG potential and FES-evoked muscle response remains elusive and continues to attract the attention of researchers due to its potential application in the fields of biomechanics, muscle physiology, and rehabilitation science. We conducted a systematic review to examine the effectiveness of eEMG potentials to assess muscle force and fatigue, particularly as a biofeedback descriptor of FES-evoked contractions in individuals with spinal cord injury. At the outset, 2867 citations were identified and, finally, fifty-nine trials met the inclusion criteria. Four hypotheses were proposed and evaluated to inform this review. The results showed that eEMG is effective at quantifying muscle force and fatigue during isometric contraction, but may not be effective during dynamic contractions including cycling and stepping. Positive correlation of up to r = 0.90 (p < 0.05) between the decline in the peak-to-peak amplitude of the eEMG and the decline in the force output during fatiguing isometric contractions has been reported. In the available prediction models, the performance index of the eEMG signal to estimate the generated muscle force ranged from 3.8% to 34% for 18 s to 70 s ahead of the actual muscle force generation. The strength and inherent limitations of the eEMG signal to assess muscle force and fatigue were evident from our findings with implications in clinical management of spinal cord injury (SCI) population. PMID:25025551

Analysis and Simple Circuit Design of Double Differential EMG Active Electrode.

PubMed

Guerrero, Federico Nicolás; Spinelli, Enrique Mario; Haberman, Marcelo Alejandro

2016-06-01

In this paper we present an analysis of the voltage amplifier needed for double differential (DD) sEMG measurements and a novel, very simple circuit for implementing DD active electrodes. The three-input amplifier that standalone DD active electrodes require is inherently different from a differential amplifier, and general knowledge about its design is scarce in the literature. First, the figures of merit of the amplifier are defined through a decomposition of its input signal into three orthogonal modes. This analysis reveals a mode containing EMG crosstalk components that the DD electrode should reject. Then, the effect of finite input impedance is analyzed. Because there are three terminals, minimum bounds for interference rejection ratios due to electrode and input impedance unbalances with two degrees of freedom are obtained. Finally, a novel circuit design is presented, including only a quadruple operational amplifier and a few passive components. This design is nearly as simple as the branched electrode and much simpler than the three instrumentation amplifier design, while providing robust EMG crosstalk rejection and better input impedance using unity gain buffers for each electrode input. The interference rejection limits of this input stage are analyzed. An easily replicable implementation of the proposed circuit is described, together with a parameter design guideline to adjust it to specific needs. The electrode is compared with the established alternatives, and sample sEMG signals are obtained, acquired on different body locations with dry contacts, successfully rejecting interference sources.

Acoustic (loudspeaker) facial EMG monitoring: II. Use of evoked EMG activity during acoustic neuroma resection.

PubMed

Prass, R L; Kinney, S E; Hardy, R W; Hahn, J F; Lüders, H

1987-12-01

Facial electromyographic (EMG) activity was continuously monitored via loudspeaker during eleven translabyrinthine and nine suboccipital consecutive unselected acoustic neuroma resections. Ipsilateral facial EMG activity was synchronously recorded on the audio channels of operative videotapes, which were retrospectively reviewed in order to allow detailed evaluation of the potential benefit of various acoustic EMG patterns in the performance of specific aspects of acoustic neuroma resection. The use of evoked facial EMG activity was classified and described. Direct local mechanical (surgical) stimulation and direct electrical stimulation were of benefit in the localization and/or delineation of the facial nerve contour. Burst and train acoustic patterns of EMG activity appeared to indicate surgical trauma to the facial nerve that would not have been appreciated otherwise. Early results of postoperative facial function of monitored patients are presented, and the possible value of burst and train acoustic EMG activity patterns in the intraoperative assessment of facial nerve function is discussed. Acoustic facial EMG monitoring appears to provide a potentially powerful surgical tool for delineation of the facial nerve contour, the ongoing use of which may lead to continued improvement in facial nerve function preservation through modification of dissection strategy.

Surface electromyographic amplitude does not identify differences in neural drive to synergistic muscles.

PubMed

Martinez-Valdes, Eduardo; Negro, Francesco; Falla, Deborah; De Nunzio, Alessandro Marco; Farina, Dario

2018-04-01

Surface electromyographic (EMG) signal amplitude is typically used to compare the neural drive to muscles. We experimentally investigated this association by studying the motor unit (MU) behavior and action potentials in the vastus medialis (VM) and vastus lateralis (VL) muscles. Eighteen participants performed isometric knee extensions at four target torques [10, 30, 50, and 70% of the maximum torque (MVC)] while high-density EMG signals were recorded from the VM and VL. The absolute EMG amplitude was greater for VM than VL ( P < 0.001), whereas the EMG amplitude normalized with respect to MVC was greater for VL than VM ( P < 0.04). Because differences in EMG amplitude can be due to both differences in the neural drive and in the size of the MU action potentials, we indirectly inferred the neural drives received by the two muscles by estimating the synaptic inputs received by the corresponding motor neuron pools. For this purpose, we analyzed the increase in discharge rate from recruitment to target torque for motor units matched by recruitment threshold in the two muscles. This analysis indicated that the two muscles received similar levels of neural drive. Nonetheless, the size of the MU action potentials was greater for VM than VL ( P < 0.001), and this difference explained most of the differences in EMG amplitude between the two muscles (~63% of explained variance). These results indicate that EMG amplitude, even following normalization, does not reflect the neural drive to synergistic muscles. Moreover, absolute EMG amplitude is mainly explained by the size of MU action potentials. NEW & NOTEWORTHY Electromyographic (EMG) amplitude is widely used to compare indirectly the strength of neural drive received by synergistic muscles. However, there are no studies validating this approach with motor unit data. Here, we compared between-muscles differences in surface EMG amplitude and motor unit behavior. The results clarify the limitations of surface EMG to

Hybrid fusion of linear, non-linear and spectral models for the dynamic modeling of sEMG and skeletal muscle force: an application to upper extremity amputation.

PubMed

Potluri, Chandrasekhar; Anugolu, Madhavi; Schoen, Marco P; Subbaram Naidu, D; Urfer, Alex; Chiu, Steve

2013-11-01

Estimating skeletal muscle (finger) forces using surface Electromyography (sEMG) signals poses many challenges. In general, the sEMG measurements are based on single sensor data. In this paper, two novel hybrid fusion techniques for estimating the skeletal muscle force from the sEMG array sensors are proposed. The sEMG signals are pre-processed using five different filters: Butterworth, Chebychev Type II, Exponential, Half-Gaussian and Wavelet transforms. Dynamic models are extracted from the acquired data using Nonlinear Wiener Hammerstein (NLWH) models and Spectral Analysis Frequency Dependent Resolution (SPAFDR) models based system identification techniques. A detailed comparison is provided for the proposed filters and models using 18 healthy subjects. Wavelet transforms give higher mean correlation of 72.6 ± 1.7 (mean ± SD) and 70.4 ± 1.5 (mean ± SD) for NLWH and SPAFDR models, respectively, when compared to the other filters used in this work. Experimental verification of the fusion based hybrid models with wavelet transform shows a 96% mean correlation and 3.9% mean relative error with a standard deviation of ± 1.3 and ± 0.9 respectively between the overall hybrid fusion algorithm estimated and the actual force for 18 test subjects' k-fold cross validation data. © 2013 Elsevier Ltd. All rights reserved.

An open and configurable embedded system for EMG pattern recognition implementation for artificial arms.

PubMed

Jun Liu; Fan Zhang; Huang, He Helen

2014-01-01

Pattern recognition (PR) based on electromyographic (EMG) signals has been developed for multifunctional artificial arms for decades. However, assessment of EMG PR control for daily prosthesis use is still limited. One of the major barriers is the lack of a portable and configurable embedded system to implement the EMG PR control. This paper aimed to design an open and configurable embedded system for EMG PR implementation so that researchers can easily modify and optimize the control algorithms upon our designed platform and test the EMG PR control outside of the lab environments. The open platform was built on an open source embedded Linux Operating System running a high-performance Gumstix board. Both the hardware and software system framework were openly designed. The system was highly flexible in terms of number of inputs/outputs and calibration interfaces used. Such flexibility enabled easy integration of our embedded system with different types of commercialized or prototypic artificial arms. Thus far, our system was portable for take-home use. Additionally, compared with previously reported embedded systems for EMG PR implementation, our system demonstrated improved processing efficiency and high system precision. Our long-term goals are (1) to develop a wearable and practical EMG PR-based control for multifunctional artificial arms, and (2) to quantify the benefits of EMG PR-based control over conventional myoelectric prosthesis control in a home setting.

Electrotactile EMG feedback improves the control of prosthesis grasping force

NASA Astrophysics Data System (ADS)

Schweisfurth, Meike A.; Markovic, Marko; Dosen, Strahinja; Teich, Florian; Graimann, Bernhard; Farina, Dario

2016-10-01

Objective. A drawback of active prostheses is that they detach the subject from the produced forces, thereby preventing direct mechanical feedback. This can be compensated by providing somatosensory feedback to the user through mechanical or electrical stimulation, which in turn may improve the utility, sense of embodiment, and thereby increase the acceptance rate. Approach. In this study, we compared a novel approach to closing the loop, namely EMG feedback (emgFB), to classic force feedback (forceFB), using electrotactile interface in a realistic task setup. Eleven intact-bodied subjects and one transradial amputee performed a routine grasping task while receiving emgFB or forceFB. The two feedback types were delivered through the same electrotactile interface, using a mixed spatial/frequency coding to transmit 8 discrete levels of the feedback variable. In emgFB, the stimulation transmitted the amplitude of the processed myoelectric signal generated by the subject (prosthesis input), and in forceFB the generated grasping force (prosthesis output). The task comprised 150 trials of routine grasping at six forces, randomly presented in blocks of five trials (same force). Interquartile range and changes in the absolute error (AE) distribution (magnitude and dispersion) with respect to the target level were used to assess precision and overall performance, respectively. Main results. Relative to forceFB, emgFB significantly improved the precision of myoelectric commands (min/max of the significant levels) for 23%/36% as well as the precision of force control for 12%/32%, in intact-bodied subjects. Also, the magnitude and dispersion of the AE distribution were reduced. The results were similar in the amputee, showing considerable improvements. Significance. Using emgFB, the subjects therefore decreased the uncertainty of the forward pathway. Since there is a correspondence between the EMG and force, where the former anticipates the latter, the emgFB allowed for

Effects of innovative virtual reality game and EMG biofeedback on neuromotor control in cerebral palsy.

PubMed

Yoo, Ji Won; Lee, Dong Ryul; Sim, Yon Ju; You, Joshua H; Kim, Cheol J

2014-01-01

Sensorimotor control dysfunction or dyskinesia is a hallmark of neuromuscular impairment in children with cerebral palsy (CP), and is often implicated in reaching and grasping deficiencies due to a neuromuscular imbalance between the triceps and biceps. To mitigate such muscle imbalances, an innovative electromyography (EMG)-virtual reality (VR) biofeedback system were designed to provide accurate information about muscle activation and motivation. However, the clinical efficacy of this approach has not yet been determined in children with CP. The purpose of this study was to investigate the effectiveness of a combined EMG biofeedback and VR (EMG-VR biofeedback) intervention system to improve muscle imbalance between triceps and biceps during reaching movements in children with spastic CP. Raw EMG signals were recorded at a sampling rate of 1,000 Hz, band-pass filtered between 20-450 Hz, and notch-filtered at 60 Hz during elbow flexion and extension movements. EMG data were then processed using MyoResearch Master Edition 1.08 XP software. All participants underwent both interventions consisting of the EMG-VR biofeedback combination and EMG biofeedback alone. EMG analysis resulted in improved muscle activation in the underactive triceps while decreasing overactive or hypertonic biceps in the EMG-VR biofeedback compared with EMG biofeedback. The muscle imbalance ratio between the triceps and biceps was consistently improved. The present study is the first clinical trial to provide evidence for the additive benefits of VR intervention for enhancing the upper limb function of children with spastic CP.

Evaluation of head orientation and neck muscle EMG signals as three-dimensional command sources.

PubMed

Williams, Matthew R; Kirsch, Robert F

2015-03-05

High cervical spinal cord injuries result in significant functional impairments and affect both the injured individual as well as their family and care givers. To help restore function to these individuals, multiple user interfaces are available to enable command and control of external devices. However, little work has been performed to assess the 3D performance of these interfaces. We investigated the performance of eight human subjects in using three user interfaces (head orientation, EMG from muscles of the head and neck, and a three-axis joystick) to command the endpoint position of a multi-axis robotic arm within a 3D workspace to perform a novel out-to-center 3D Fitts' Law style task. Two of these interfaces (head orientation, EMG from muscles of the head and neck) could realistically be used by individuals with high tetraplegia, while the joystick was evaluated as a standard of high performance. Performance metrics were developed to assess the aspects of command source performance. Data were analyzed using a mixed model design ANOVA. Fixed effects were investigated between sources as well as for interactions between index of difficulty, command source, and the five performance measures used. A 5% threshold for statistical significance was used in the analysis. The performances of the three command interfaces were rather similar, though significant differences between command sources were observed. The apparent similarity is due in large part to the sequential command strategy (i.e., one dimension of movement at a time) typically adopted by the subjects. EMG-based commands were particularly pulsatile in nature. The use of sequential commands had a significant impact on each command source's performance for movements in two or three dimensions. While the sequential nature of the commands produced by the user did not fit with Fitts' Law, the other performance measures used were able to illustrate the properties of each command source. Though pulsatile, given

Influence of Joint Angle on EMG-Torque Model During Constant-Posture, Torque-Varying Contractions.

PubMed

Liu, Pu; Liu, Lukai; Clancy, Edward A

2015-11-01

Relating the electromyogram (EMG) to joint torque is useful in various application areas, including prosthesis control, ergonomics and clinical biomechanics. Limited study has related EMG to torque across varied joint angles, particularly when subjects performed force-varying contractions or when optimized modeling methods were utilized. We related the biceps-triceps surface EMG of 22 subjects to elbow torque at six joint angles (spanning 60° to 135°) during constant-posture, torque-varying contractions. Three nonlinear EMG σ -torque models, advanced EMG amplitude (EMG σ ) estimation processors (i.e., whitened, multiple-channel) and the duration of data used to train models were investigated. When EMG-torque models were formed separately for each of the six distinct joint angles, a minimum "gold standard" error of 4.01±1.2% MVC(F90) resulted (i.e., error relative to maximum voluntary contraction at 90° flexion). This model structure, however, did not directly facilitate interpolation across angles. The best model which did so achieved a statistically equivalent error of 4.06±1.2% MVC(F90). Results demonstrated that advanced EMG σ processors lead to improved joint torque estimation as do longer model training durations.

[The effect of EMG level by EMG biofeedback with progressive muscle relaxation training on tension headache].

PubMed

Ro, U J; Kim, N C; Kim, H S

1990-08-01

The purpose of this study is to assess if EMG biofeedback training with progressive muscle relaxation training is effective in reducing the EMG level in patients with tension headaches. This study which lasted from 23 October to 30 December 1989, was conducted on 10 females who were diagnosed as patients with tension headaches and selected from among volunteers at C. University in Seoul. The process of the study was as follows: First, before the treatment, the baseline was measured for two weeks and the level of EMG was measured five times in five minutes. And then EMG biofeedback training was used for six weeks, 12 sessions in all, and progressive muscle relaxation was done at home by audio tape over eight weeks. Each session was composed of a 5-minute baseline, two 5-minute EMG biofeedback training periods and a 5-minute self-control stage. Each stage was followed by a five minute rest period. So each session took a total of 40 minutes. The EMG level was measured by EMG biofeedback (Autogenic-Cyborg: M 130 EMG module). The results were as follows: 1. The average age of the subjects was 44.1 years and the average history of headache was 10.6 years (range: 6 months-20 years). 2. The level of EMG was lowest between the third and the fourth week of the training except in Cases I and IV. 3. The patients began to show a nonconciliatory attitude at the first session of the fifth week of the training.

Interpreting Signal Amplitudes in Surface Electromyography Studies in Sport and Rehabilitation Sciences

PubMed Central

Vigotsky, Andrew D.; Halperin, Israel; Lehman, Gregory J.; Trajano, Gabriel S.; Vieira, Taian M.

2018-01-01

Surface electromyography (sEMG) is a popular research tool in sport and rehabilitation sciences. Common study designs include the comparison of sEMG amplitudes collected from different muscles as participants perform various exercises and techniques under different loads. Based on such comparisons, researchers attempt to draw conclusions concerning the neuro- and electrophysiological underpinning of force production and hypothesize about possible longitudinal adaptations, such as strength and hypertrophy. However, such conclusions are frequently unsubstantiated and unwarranted. Hence, the goal of this review is to discuss what can and cannot be inferred from comparative research designs as it pertains to both the acute and longitudinal outcomes. General methodological recommendations are made, gaps in the literature are identified, and lines for future research to help improve the applicability of sEMG are suggested. PMID:29354060

Surface EMG electrodes do not accurately record from lumbar multifidus muscles.

PubMed

Stokes, Ian A F; Henry, Sharon M; Single, Richard M

2003-01-01

This study investigated whether electromyographic signals recorded from the skin surface overlying the multifidus muscles could be used to quantify their activity. Comparison of electromyography signals recorded from electrodes on the back surface and from wire electrodes within four different slips of multifidus muscles of three human subjects performing isometric tasks that loaded the trunk from three different directions. It has been suggested that suitably placed surface electrodes can be used to record activity in the deep multifidus muscles. We tested whether there was a stronger correlation and more consistent regression relationship between signals from electrodes overlying multifidus and longissimus muscles respectively than between signals from within multifidus and from the skin surface electrodes over multifidus. The findings provided consistent evidence that the surface electrodes placed over multifidus muscles were more sensitive to the adjacent longissimus muscles than to the underlying multifidus muscles. The R(2) for surface versus intra-muscular comparisons was 0.64, while the average R(2) for surface-multifidus versus surface-longissimus comparisons was 0.80. Also, the magnitude of the regression coefficients was less variable between different tasks for the longissimus versus surface multifidus comparisons. Accurate measurement of multifidus muscle activity requires intra-muscular electrodes. Electromyography is the accepted technique to document the level of muscular activation, but its specificity to particular muscles depends on correct electrode placement. For multifidus, intra-muscular electrodes are required.

Effects of the innervation zone on the time and frequency domain parameters of the surface electromyographic signal.

PubMed

Smith, Cory M; Housh, Terry J; Herda, Trent J; Zuniga, Jorge M; Ryan, Eric D; Camic, Clayton L; Bergstrom, Haley C; Smith, Doug B; Weir, Joseph P; Cramer, Joel T; Hill, Ethan C; Cochrane, Kristen C; Jenkins, Nathaniel D M; Schmidt, Richard J; Johnson, Glen O

2015-08-01

The purposes of the present study were to examine the effects of electrode placements over, proximal, and distal to the innervation zone (IZ) on electromyographic (EMG) amplitude (RMS) and frequency (MPF) responses during: (1) a maximal voluntary isometric contraction (MVIC), and; (2) a sustained, submaximal isometric muscle action. A linear array was used to record EMG signals from the vastus lateralis over the IZ, 30mm proximal, and 30mm distal to the IZ during an MVIC and a sustained isometric muscle action of the leg extensors at 50% MVIC. During the MVIC, lower EMG RMS (p>0.05) and greater EMG MPF (p<0.05) values were recorded over the IZ compared to away from the IZ, however, no differences in slope coefficients for the EMG RMS and MPF versus time relationships over, proximal, and distal to the IZ occurred. Thus, the results of the present study indicated that during an MVIC, EMG RMS and MPF values recorded over the IZ are not comparable to those away from the IZ. However, the rates of fatigue-induced changes in EMG RMS and MPF during sustained, submaximal isometric muscle actions of the leg extensors were the same regardless of the electrode placement locations relative to the IZ. Copyright © 2015 Elsevier Ltd. All rights reserved.

Anomaly Detection of Electromyographic Signals.

PubMed

Ijaz, Ahsan; Choi, Jongeun

2018-04-01

In this paper, we provide a robust framework to detect anomalous electromyographic (EMG) signals and identify contamination types. As a first step for feature selection, optimally selected Lawton wavelets transform is applied. Robust principal component analysis (rPCA) is then performed on these wavelet coefficients to obtain features in a lower dimension. The rPCA based features are used for constructing a self-organizing map (SOM). Finally, hierarchical clustering is applied on the SOM that separates anomalous signals residing in the smaller clusters and breaks them into logical units for contamination identification. The proposed methodology is tested using synthetic and real world EMG signals. The synthetic EMG signals are generated using a heteroscedastic process mimicking desired experimental setups. A sub-part of these synthetic signals is introduced with anomalies. These results are followed with real EMG signals introduced with synthetic anomalies. Finally, a heterogeneous real world data set is used with known quality issues under an unsupervised setting. The framework provides recall of 90% (± 3.3) and precision of 99%(±0.4).

Detection of convulsive seizures using surface electromyography.

PubMed

Beniczky, Sándor; Conradsen, Isa; Wolf, Peter

2018-06-01

Bilateral (generalized) tonic-clonic seizures (TCS) increase the risk of sudden unexpected death in epilepsy (SUDEP), especially when patients are unattended. In sleep, TCS often remain unnoticed, which can result in suboptimal treatment decisions. There is a need for automated detection of these major epileptic seizures, using wearable devices. Quantitative surface electromyography (EMG) changes are specific for TCS and characterized by a dynamic evolution of low- and high-frequency signal components. Algorithms targeting increase in high-frequency EMG signals constitute biomarkers of TCS; they can be used both for seizure detection and for differentiating TCS from convulsive nonepileptic seizures. Two large-scale, blinded, prospective studies demonstrated the accuracy of wearable EMG devices for detecting TCS with high sensitivity (76%-100%). The rate of false alarms (0.7-2.5/24 h) needs further improvement. This article summarizes the pathophysiology of muscle activation during convulsive seizures and reviews the published evidence on the accuracy of EMG-based seizure detection. Wiley Periodicals, Inc. © 2018 International League Against Epilepsy.

[Surface electromyography signal classification using gray system theory].

PubMed

Xie, Hongbo; Ma, Congbin; Wang, Zhizhong; Huang, Hai

2004-12-01

A new method based on gray correlation was introduced to improve the identification rate in artificial limb. The electromyography (EMG) signal was first transformed into time-frequency domain by wavelet transform. Singular value decomposition (SVD) was then used to extract feature vector from the wavelet coefficient for pattern recognition. The decision was made according to the maximum gray correlation coefficient. Compared with neural network recognition, this robust method has an almost equivalent recognition rate but much lower computation costs and less training samples.

Pathological tremor prediction using surface EMG and acceleration: potential use in “ON-OFF” demand driven deep brain stimulator design

PubMed Central

Basu, Ishita; Graupe, Daniel; Tuninetti, Daniela; Shukla, Pitamber; Slavin, Konstantin V.; Metman, Leo Verhagen; Corcos, Daniel M.

2013-01-01

Objective We present a proof of concept for a novel method of predicting the onset of pathological tremor using non-invasively measured surface electromyogram (sEMG) and acceleration from tremor-affected extremities of patients with Parkinson’s disease (PD) and Essential tremor (ET). Approach The tremor prediction algorithm uses a set of spectral (fourier and wavelet) and non-linear time series (entropy and recurrence rate) parameters extracted from the non-invasively recorded sEMG and acceleration signals. Main results The resulting algorithm is shown to successfully predict tremor onset for all 91 trials recorded in 4 PD patients and for all 91 trials recorded in 4 ET patients. The predictor achieves a 100% sensitivity for all trials considered, along with an overall accuracy of 85.7% for all ET trials and 80.2% for all PD trials. By using a Pearson’s chi-square test, the prediction results are shown to significantly differ from a random prediction outcome. Significance The tremor prediction algorithm can be potentially used for designing the next generation of non-invasive closed-loop predictive ON-OFF controllers for deep brain stimulation (DBS), used for suppressing pathological tremor in such patients. Such a system is based on alternating ON and OFF DBS periods, an incoming tremor being predicted during the time intervals when DBS is OFF, so as to turn DBS back ON. The prediction should be a few seconds before tremor re-appears so that the patient is tremor-free for the entire DBS ON-OFF cycle as well as the tremor-free DBS OFF interval should be maximized in order to minimize the current injected in the brain and battery usage. PMID:23658233

Knee joint angle affects EMG-force relationship in the vastus intermedius muscle.

PubMed

Saito, Akira; Akima, Hiroshi

2013-12-01

It is not understood how the knee joint angle affects the relationship between electromyography (EMG) and force of four individual quadriceps femoris (QF) muscles. The purpose of this study was to examine the effect of the knee joint angle on the EMG-force relationship of the four individual QF muscles, particularly the vastus intermedius (VI), during isometric knee extensions. Eleven healthy men performed 20-100% of maximal voluntary contraction (MVC) at knee joint angles of 90°, 120° and 150°. Surface EMG of the four QF synergists was recorded and normalized by the root mean square during MVC. The normalized EMG of the four QF synergists at a knee joint angle of 150° was significantly lower than that at 90° and 120° (P < 0.05). Comparing the normalized EMG among the four QF synergists, a significantly lower normalized EMG was observed in the VI at 150° as compared with the other three QF muscles (P < 0.05). These results suggest that the EMG-force relationship of the four QF synergists shifted downward at an extended knee joint angle of 150°. Furthermore, the neuromuscular activation of the VI was the most sensitive to change in muscle length among the four QF synergistic muscles. Copyright © 2013 Elsevier Ltd. All rights reserved.

Wideband EMG telemetry system

NASA Technical Reports Server (NTRS)

Rosatino, S. A.; Westbrook, R. M.

1979-01-01

Miniature, individual crystal-controlled RF transmitters located in EMG pressure sensors simplifies multichannel EMG telemetry for electronic gait monitoring. Transmitters which are assigned operating frequencies within 174 - 216 MHz band have linear frequency response from 20 - 2000 Hz and operate over range of 15 m.

Non-Stationarity and Power Spectral Shifts in EMG Activity Reflect Motor Unit Recruitment in Rat Diaphragm Muscle

PubMed Central

Seven, Yasin B.; Mantilla, Carlos B.; Zhan, Wen-Zhi; Sieck, Gary C.

2012-01-01

We hypothesized that diaphragm muscle (DIAm) by a shift in the EMG power spectral density (PSD) to higher frequencies reflects recruitment of more fatigable fast-twitch motor units and motor unit recruitment is reflected by EMG non-stationarity. DIAm EMG was recorded in anesthetized rats during eupnea, hypoxia-hypercapnia (10% O2-5% CO2), airway occlusion, and sneezing (maximal DIAm force). Although power in all frequency bands increased progressively across motor behaviors, PSD centroid frequency increased only during sneezing (p<0.05). The non-stationary period at the onset of EMG activity ranged from ~70 ms during airway occlusion to ~150 ms during eupnea. Within the initial non-stationary period of EMG activity 80–95% of motor units were recruited during different motor behaviors. Motor units augmented their discharge frequencies progressively beyond the non-stationary period; yet, EMG signal became stationary. In conclusion, non-stationarity of DIAm EMG reflects the period of motor unit recruitment, while a shift in the PSD towards higher frequencies reflects recruitment of more fatigable fast-twitch motor units. PMID:22986086

Non-stationarity and power spectral shifts in EMG activity reflect motor unit recruitment in rat diaphragm muscle.

PubMed

Seven, Yasin B; Mantilla, Carlos B; Zhan, Wen-Zhi; Sieck, Gary C

2013-01-15

We hypothesized that a shift in diaphragm muscle (DIAm) EMG power spectral density (PSD) to higher frequencies reflects recruitment of more fatigable fast-twitch motor units and motor unit recruitment is reflected by EMG non-stationarity. DIAm EMG was recorded in anesthetized rats during eupnea, hypoxia-hypercapnia (10% O(2)-5% CO(2)), airway occlusion, and sneezing (maximal DIAm force). Although power in all frequency bands increased progressively across motor behaviors, PSD centroid frequency increased only during sneezing (p<0.05). The non-stationary period at the onset of EMG activity ranged from ∼80 ms during airway occlusion to ∼150 ms during eupnea. Within the initial non-stationary period of EMG activity 80-95% of motor units were recruited during different motor behaviors. Motor units augmented their discharge frequencies progressively beyond the non-stationary period; yet, EMG signal became stationary. In conclusion, non-stationarity of DIAm EMG reflects the period of motor unit recruitment, while a shift in the PSD towards higher frequencies reflects recruitment of more fatigable fast-twitch motor units. Copyright © 2012 Elsevier B.V. All rights reserved.

Predicting Blood Lactate Concentration and Oxygen Uptake from sEMG Data during Fatiguing Cycling Exercise.

PubMed

Ražanskas, Petras; Verikas, Antanas; Olsson, Charlotte; Viberg, Per-Arne

2015-08-19

This article presents a study of the relationship between electromyographic (EMG) signals from vastus lateralis, rectus femoris, biceps femoris and semitendinosus muscles, collected during fatiguing cycling exercises, and other physiological measurements, such as blood lactate concentration and oxygen consumption. In contrast to the usual practice of picking one particular characteristic of the signal, e.g., the median or mean frequency, multiple variables were used to obtain a thorough characterization of EMG signals in the spectral domain. Based on these variables, linear and non-linear (random forest) models were built to predict blood lactate concentration and oxygen consumption. The results showed that mean and median frequencies are sub-optimal choices for predicting these physiological quantities in dynamic exercises, as they did not exhibit significant changes over the course of our protocol and only weakly correlated with blood lactate concentration or oxygen uptake. Instead, the root mean square of the original signal and backward difference, as well as parameters describing the tails of the EMG power distribution were the most important variables for these models. Coefficients of determination ranging from R(2) = 0:77 to R(2) = 0:98 (for blood lactate) and from R(2) = 0:81 to R(2) = 0:97 (for oxygen uptake) were obtained when using random forest regressors.

EMG monitoring during functional non-surgical therapy of Achilles tendon rupture.

PubMed

Hüfner, Tobias; Wohifarth, Kai; Fink, Matthias; Thermann, H; Rollnik, Jens D

2002-07-01

After surgical therapy of Achilles tendon rupture, neuromuscular changes may persist, even one year after surgery. We were interested whether these changes are also evident following a non-surgical functional therapy (Variostabil therapy boot/Adidas). Twenty-one patients with complete Achilles tendon rupture were enrolled in the study (mean age 38.5 years, range 24 to 60; 18 men, three women) and followed-up clinically and with surface EMG of the gastrocnemius muscles after four, eight, 12 weeks, and one year after rupture. EMG differences between the affected and non-affected side could only be observed at baseline and after four weeks following Achilles tendon rupture. The results from our study show that EMG changes are not found following non-surgical functional therapy.

The reliability of a maximal isometric hip strength and simultaneous surface EMG screening protocol in elite, junior rugby league athletes.

PubMed

Charlton, Paula C; Mentiplay, Benjamin F; Grimaldi, Alison; Pua, Yong-Hao; Clark, Ross A

2017-02-01

Firstly to describe the reliability of assessing maximal isometric strength of the hip abductor and adductor musculature using a hand held dynamometry (HHD) protocol with simultaneous wireless surface electromyographic (sEMG) evaluation of the gluteus medius (GM) and adductor longus (AL). Secondly, to describe the correlation between isometric strength recorded with the HHD protocol and a laboratory standard isokinetic device. Reliability and correlational study. A sample of 24 elite, male, junior, rugby league athletes, age 16-20 years participated in repeated HHD and isometric Kin-Com (KC) strength testing with simultaneous sEMG assessment, on average (range) 6 (5-7) days apart by a single assessor. Strength tests included; unilateral hip abduction (ABD) and adduction (ADD) and bilateral ADD assessed with squeeze (SQ) tests in 0 and 45° of hip flexion. HHD demonstrated good to excellent inter-session reliability for all outcome measures (ICC (2,1) =0.76-0.91) and good to excellent association with the laboratory reference KC (ICC (2,1) =0.80-0.88). Whilst intra-session, inter-trial reliability of EMG activation and co-activation outcome measures ranged from moderate to excellent (ICC (2,1) =0.70-0.94), inter-session reliability was poor (all ICC (2,1) <0.50). Isometric strength testing of the hip ABD and ADD musculature using HHD may be measured reliably in elite, junior rugby league athletes. Due to the poor inter-session reliability of sEMG measures, it is not recommended for athlete screening purposes if using the techniques implemented in this study. Copyright © 2016 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

An EMG-Controlled Robotic Hand Exoskeleton for Bilateral Rehabilitation.

PubMed

Leonardis, Daniele; Barsotti, Michele; Loconsole, Claudio; Solazzi, Massimiliano; Troncossi, Marco; Mazzotti, Claudio; Castelli, Vincenzo Parenti; Procopio, Caterina; Lamola, Giuseppe; Chisari, Carmelo; Bergamasco, Massimo; Frisoli, Antonio

2015-01-01

This paper presents a novel electromyography (EMG)-driven hand exoskeleton for bilateral rehabilitation of grasping in stroke. The developed hand exoskeleton was designed with two distinctive features: (a) kinematics with intrinsic adaptability to patient's hand size, and (b) free-palm and free-fingertip design, preserving the residual sensory perceptual capability of touch during assistance in grasping of real objects. In the envisaged bilateral training strategy, the patient's non paretic hand acted as guidance for the paretic hand in grasping tasks. Grasping force exerted by the non paretic hand was estimated in real-time from EMG signals, and then replicated as robotic assistance for the paretic hand by means of the hand-exoskeleton. Estimation of the grasping force through EMG allowed to perform rehabilitation exercises with any, non sensorized, graspable objects. This paper presents the system design, development, and experimental evaluation. Experiments were performed within a group of six healthy subjects and two chronic stroke patients, executing robotic-assisted grasping tasks. Results related to performance in estimation and modulation of the robotic assistance, and to the outcomes of the pilot rehabilitation sessions with stroke patients, positively support validity of the proposed approach for application in stroke rehabilitation.

The Location of Peak Upper Trapezius Muscle Activity During Submaximal Contractions is not Associated With the Location of Myofascial Trigger Points: New Insights Revealed by High-density Surface EMG.

PubMed

Barbero, Marco; Falla, Deborah; Mafodda, Luca; Cescon, Corrado; Gatti, Roberto

2016-12-01

To apply topographical mapping of the electromyography (EMG) amplitude recorded from the upper trapezius muscle to evaluate the distribution of activity and the location of peak activity during a shoulder elevation task in participants with and without myofascial pain and myofascial trigger points (MTrP) and compare this location with the site of the MTrP. Thirteen participants with myofascial pain and MTrP in the upper trapezius muscle and 12 asymptomatic individuals participated. High-density surface EMG was recorded from the upper trapezius muscle using a matrix of 64 surface electrodes aligned with an anatomic landmark system (ALS). Each participant performed a shoulder elevation task consisting of a series of 30 s ramped contractions to 15% or 60% of their maximal voluntary contraction (MVC) force. Topographical maps of the EMG average rectified value were computed and the peak EMG amplitude during the ramped contractions was identified and its location determined with respect to the ALS. The location of the MTrP was also determined relative to the ALS and Spearman correlation coefficients were used to examine the relationship between MTrP and peak EMG amplitude location. The location of the peak EMG amplitude was significantly (P<0.05) different between groups (participants with pain/MTrP: -0.32±1.2 cm at 15% MVC and -0.35±0.9 cm at 60% MVC relative to the ALS; asymptomatic participants: 1.0±1.3 cm at 15% MVC and 1.3±1.1 cm relative to the ALS). However, no correlation was observed between the position of the MTrP and peak EMG amplitude during the ramped contractions at either force level (15%: rs=0.039, P=0.9; 60%: rs=-0.087, P=0.778). People with myofascial pain and MTrP displayed a caudal shift of the distribution of upper trapezius muscle activity compared with asymptomatic individuals during a submaximal shoulder elevation task. For the first time, we show that the location of peak muscle activity is not associated with the location of the MTrP.

Improving robustness against electrode shift of high density EMG for myoelectric control through common spatial patterns.

PubMed

Pan, Lizhi; Zhang, Dingguo; Jiang, Ning; Sheng, Xinjun; Zhu, Xiangyang

2015-12-02

Most prosthetic myoelectric control studies have concentrated on low density (less than 16 electrodes, LD) electromyography (EMG) signals, due to its better clinical applicability and low computation complexity compared with high density (more than 16 electrodes, HD) EMG signals. Since HD EMG electrodes have been developed more conveniently to wear with respect to the previous versions recently, HD EMG signals become an alternative for myoelectric prostheses. The electrode shift, which may occur during repositioning or donning/doffing of the prosthetic socket, is one of the main reasons for degradation in classification accuracy (CA). HD EMG signals acquired from the forearm of the subjects were used for pattern recognition-based myoelectric control in this study. Multiclass common spatial patterns (CSP) with two types of schemes, namely one versus one (CSP-OvO) and one versus rest (CSP-OvR), were used for feature extraction to improve the robustness against electrode shift for myoelectric control. Shift transversal (ST1 and ST2) and longitudinal (SL1 and SL2) to the direction of the muscle fibers were taken into consideration. We tested nine intact-limb subjects for eleven hand and wrist motions. The CSP features (CSP-OvO and CSP-OvR) were compared with three commonly used features, namely time-domain (TD) features, time-domain autoregressive (TDAR) features and variogram (Variog) features. Compared with the TD features, the CSP features significantly improved the CA over 10 % in all shift configurations (ST1, ST2, SL1 and SL2). Compared with the TDAR features, a. the CSP-OvO feature significantly improved the average CA over 5 % in all shift configurations; b. the CSP-OvR feature significantly improved the average CA in shift configurations ST1, SL1 and SL2. Compared with the Variog features, the CSP features significantly improved the average CA in longitudinal shift configurations (SL1 and SL2). The results demonstrated that the CSP features significantly

Cortico-muscular coherence on artifact corrected EEG-EMG data recorded with a MRI scanner.

PubMed

Muthuraman, M; Galka, A; Hong, V N; Heute, U; Deuschl, G; Raethjen, J

2013-01-01

Simultaneous recording of electroencephalogram (EEG) and electromyogram (EMG) with magnetic resonance imaging (MRI) provides great potential for studying human brain activity with high temporal and spatial resolution. But, due to the MRI, the recorded signals are contaminated with artifacts. The correction of these artifacts is important to use these signals for further spectral analysis. The coherence can reveal the cortical representation of peripheral muscle signal in particular motor tasks, e.g. finger movements. The artifact correction of these signals was done by two different algorithms the Brain vision analyzer (BVA) and the Matlab FMRIB plug-in for EEGLAB. The Welch periodogram method was used for estimating the cortico-muscular coherence. Our analysis revealed coherence with a frequency of 5Hz in the contralateral side of the brain. The entropy is estimated for the calculated coherence to get the distribution of coherence in the scalp. The significance of the paper is to identify the optimal algorithm to rectify the MR artifacts and as a first step to use both these signals EEG and EMG in conjunction with MRI for further studies.

Power independent EMG based gesture recognition for robotics.

PubMed

Li, Ling; Looney, David; Park, Cheolsoo; Rehman, Naveed U; Mandic, Danilo P

2011-01-01

A novel method for detecting muscle contraction is presented. This method is further developed for identifying four different gestures to facilitate a hand gesture controlled robot system. It is achieved based on surface Electromyograph (EMG) measurements of groups of arm muscles. The cross-information is preserved through a simultaneous processing of EMG channels using a recent multivariate extension of Empirical Mode Decomposition (EMD). Next, phase synchrony measures are employed to make the system robust to different power levels due to electrode placements and impedances. The multiple pairwise muscle synchronies are used as features of a discrete gesture space comprising four gestures (flexion, extension, pronation, supination). Simulations on real-time robot control illustrate the enhanced accuracy and robustness of the proposed methodology.

Linear methods for reducing EMG contamination in peripheral nerve motor decodes.

PubMed

Kagan, Zachary B; Wendelken, Suzanne; Page, David M; Davis, Tyler; Hutchinson, Douglas T; Clark, Gregory A; Warren, David J

2016-08-01

Signals recorded from the peripheral nervous system (PNS) with high channel count penetrating microelectrode arrays, such as the Utah Slanted Electrode Array (USEA), often have electromyographic (EMG) signals contaminating the neural signal. This common-mode signal source may prevent single neural units from successfully being detected, thus hindering motor decode algorithms. Reducing this EMG contamination may lead to more accurate motor decode performance. A virtual reference (VR), created by a weighted linear combination of signals from a subset of all available channels, can be used to reduce this EMG contamination. Four methods of determining individual channel weights and six different methods of selecting subsets of channels were investigated (24 different VR types in total). The methods of determining individual channel weights were equal weighting, regression-based weighting, and two different proximity-based weightings. The subsets of channels were selected by a radius-based criteria, such that a channel was included if it was within a particular radius of inclusion from the target channel. These six radii of inclusion were 1.5, 2.9, 3.2, 5, 8.4, and 12.8 electrode-distances; the 12.8 electrode radius includes all USEA electrodes. We found that application of a VR improves the detectability of neural events via increasing the SNR, but we found no statistically meaningful difference amongst the VR types we examined. The computational complexity of implementation varies with respect to the method of determining channel weights and the number of channels in a subset, but does not correlate with VR performance. Hence, we examined the computational costs of calculating and applying the VR and based on these criteria, we recommend an equal weighting method of assigning weights with a 3.2 electrode-distance radius of inclusion. Further, we found empirically that application of the recommended VR will require less than 1 ms for 33.3 ms of data from one USEA.

Shoulder muscles recruitment during a power backward giant swing on high bar: a wavelet-EMG-analysis.

PubMed

Frère, Julien; Göpfert, Beat; Slawinski, Jean; Tourny-Chollet, Claire

2012-04-01

This study aimed at determining the upper limb muscles coordination during a power backward giant swing (PBGS) and the recruitment pattern of motor units (MU) of co-activated muscles. The wavelet transformation (WT) was applied to the surface electromyographic (EMG) signal of eight shoulder muscles. Total gymnast's body energy and wavelet synergies extracted from the WT-EMG by using a non-negative matrix factorization were analyzed as a function of the body position angle of the gymnast. A cross-correlation analysis of the EMG patterns allowed determining two main groups of co-activated muscles. Two wavelet synergies representing the main spectral features (82% of the variance accounted for) discriminated the recruitment of MU. Although no task-group of MU was found among the muscles, it appeared that a higher proportion of fast MU was recruited within the muscles of the first group during the upper part of the PBGS. The last increase of total body energy before bar release was induced by the recruitment of the muscles of the second group but did not necessitate the recruitment of a higher proportion of fast MU. Such muscle coordination agreed with previous simulations of elements on high bar as well as the findings related to the recruitment of MU. Copyright © 2012 Elsevier B.V. All rights reserved.

Are external knee load and EMG measures accurate indicators of internal knee contact forces during gait?

PubMed

Meyer, Andrew J; D'Lima, Darryl D; Besier, Thor F; Lloyd, David G; Colwell, Clifford W; Fregly, Benjamin J

2013-06-01

Mechanical loading is believed to be a critical factor in the development and treatment of knee osteoarthritis. However, the contact forces to which the knee articular surfaces are subjected during daily activities cannot be measured clinically. Thus, the ability to predict internal knee contact forces accurately using external measures (i.e., external knee loads and muscle electromyographic [EMG] signals) would be clinically valuable. We quantified how well external knee load and EMG measures predict internal knee contact forces during gait. A single subject with a force-measuring tibial prosthesis and post-operative valgus alignment performed four gait patterns (normal, medial thrust, walking pole, and trunk sway) to induce a wide range of external and internal knee joint loads. Linear regression analyses were performed to assess how much of the variability in internal contact forces was accounted for by variability in the external measures. Though the different gait patterns successfully induced significant changes in the external and internal quantities, changes in external measures were generally weak indicators of changes in total, medial, and lateral contact force. Our results suggest that when total contact force may be changing, caution should be exercised when inferring changes in knee contact forces based on observed changes in external knee load and EMG measures. Advances in musculoskeletal modeling methods may be needed for accurate estimation of in vivo knee contact forces. Copyright © 2012 Orthopaedic Research Society.

A stretchable electrode array for non-invasive, skin-mounted measurement of electrocardiography (ECG), electromyography (EMG) and electroencephalography (EEG).

PubMed

Ma, Rui; Kim, Dae-Hyeong; McCormick, Martin; Coleman, Todd; Rogers, John

2010-01-01

This paper reports a class of stretchable electrode array capable of intimate, conformal integration onto the curvilinear surfaces of skin on the human body. The designs employ conventional metallic conductors but in optimized mechanical layouts, on soft, thin elastomeric substrates. These devices exhibit an ability to record spontaneous EEG activity even without conductive electrolyte gels, with recorded alpha rhythm responses that are 40% stronger than those collected using conventional tin electrodes and gels under otherwise similar conditions. The same type of device can also measure high quality ECG and EMG signals. The results suggest broad utility for skin-mounted measurements of electrical activity in the body, with advantages in signal levels, wearability and modes of integration compared to alternatives.

Specific muscle EMG biofeedback for hand dystonia.

PubMed

Deepak, K K; Behari, M

1999-12-01

Currently available therapies have only limited success in patients having hand dystonia (writer's cramp). We employed specific muscle EMG biofeedback (audio feedback of the EMG from proximal large muscles of the limb that show abnormally high activity during writing) in 10 of 13 consecutive patients (age, 19-62 years; all males) with a duration of illness from 6 months to 8 years. In three patients, biofeedback was not applicable due to lack of abnormal EMG values. Nine patients showed dystonic posture during writing and had hypertrophy of one or more large muscles of the dominant hand. The remaining four patients showed either involvement of small muscles or muscle wasting. Ten patients were given four or more sessions of EMG audio biofeedback from the proximal large limb muscles, which showed maximum EMG activity. They also practiced writing daily with the relaxed limb for 5 to 10 min. Nine patients showed improvement from 37 to 93% in handwriting, alleviation of discomfort, and pain (assessed on a visual analogue scale). One patient did not show any improvement. Thus EMG biofeedback improved the clinical and electromyographic picture in those patients with hand dystonia who showed EMG overactivity of proximal limb muscles during writing. This specific type of EMG biofeedback appears to be a promising tool for hand dystonia and might also be applied to other types of dystonias.

Processing Electromyographic Signals to Recognize Words

NASA Technical Reports Server (NTRS)

Jorgensen, C. C.; Lee, D. D.

2009-01-01

A recently invented speech-recognition method applies to words that are articulated by means of the tongue and throat muscles but are otherwise not voiced or, at most, are spoken sotto voce. This method could satisfy a need for speech recognition under circumstances in which normal audible speech is difficult, poses a hazard, is disturbing to listeners, or compromises privacy. The method could also be used to augment traditional speech recognition by providing an additional source of information about articulator activity. The method can be characterized as intermediate between (1) conventional speech recognition through processing of voice sounds and (2) a method, not yet developed, of processing electroencephalographic signals to extract unspoken words directly from thoughts. This method involves computational processing of digitized electromyographic (EMG) signals from muscle innervation acquired by surface electrodes under a subject's chin near the tongue and on the side of the subject s throat near the larynx. After preprocessing, digitization, and feature extraction, EMG signals are processed by a neural-network pattern classifier, implemented in software, that performs the bulk of the recognition task as described.

Nonlinear Analysis of Surface EMG Time Series of Back Muscles

NASA Astrophysics Data System (ADS)

Dolton, Donald C.; Zurcher, Ulrich; Kaufman, Miron; Sung, Paul

2004-10-01

A nonlinear analysis of surface electromyography time series of subjects with and without low back pain is presented. The mean-square displacement and entropy shows anomalous diffusive behavior on intermediate time range 10 ms < t < 1 s. This behavior implies the presence of correlations in the signal. We discuss the shape of the power spectrum of the signal.

Real-time estimation of FES-induced joint torque with evoked EMG : Application to spinal cord injured patients.

PubMed

Li, Zhan; Guiraud, David; Andreu, David; Benoussaad, Mourad; Fattal, Charles; Hayashibe, Mitsuhiro

2016-06-22

Functional electrical stimulation (FES) is a neuroprosthetic technique for restoring lost motor function of spinal cord injured (SCI) patients and motor-impaired subjects by delivering short electrical pulses to their paralyzed muscles or motor nerves. FES induces action potentials respectively on muscles or nerves so that muscle activity can be characterized by the synchronous recruitment of motor units with its compound electromyography (EMG) signal is called M-wave. The recorded evoked EMG (eEMG) can be employed to predict the resultant joint torque, and modeling of FES-induced joint torque based on eEMG is an essential step to provide necessary prediction of the expected muscle response before achieving accurate joint torque control by FES. Previous works on FES-induced torque tracking issues were mainly based on offline analysis. However, toward personalized clinical rehabilitation applications, real-time FES systems are essentially required considering the subject-specific muscle responses against electrical stimulation. This paper proposes a wireless portable stimulator used for estimating/predicting joint torque based on real time processing of eEMG. Kalman filter and recurrent neural network (RNN) are embedded into the real-time FES system for identification and estimation. Prediction results on 3 able-bodied subjects and 3 SCI patients demonstrate promising performances. As estimators, both Kalman filter and RNN approaches show clinically feasible results on estimation/prediction of joint torque with eEMG signals only, moreover RNN requires less computational requirement. The proposed real-time FES system establishes a platform for estimating and assessing the mechanical output, the electromyographic recordings and associated models. It will contribute to open a new modality for personalized portable neuroprosthetic control toward consolidated personal healthcare for motor-impaired patients.

Body position effects on sternocleidomastoid and masseter EMG pattern activity in patients undergoing occlusal splint therapy.

PubMed

Ormeño, G; Miralles, R; Santander, H; Casassus, R; Ferrer, P; Palazzi, C; Moya, H

1997-10-01

This study was conducted in order to determine the effects of body position on electromyographic (EMG) activity of sternocleidomastoid and masseter muscles, in 15 patients with myogenic cranio-cervical-mandibular dysfunction undergoing occlusal splint therapy. EMG activity was recorded by placing surface electrodes on the sternocleidomastoid and masseter muscles (contralateral to the habitual sleeping side of each patient). EMG activity at rest and during swallowing of saliva and maximal voluntary clenching was recorded in the following body positions: standing, supine and lateral decubitus. In the sternocleidomastoid muscle significant higher EMG activities at rest and during swallowing were recorded in the lateral decubitus position, whereas during maximal voluntary clenching EMG activity did not change. In the masseter muscle significant higher EMG activity during maximal voluntary clenching in a standing position was observed, whereas EMG activity at rest and during swallowing did not change. The opposite pattern of EMG activity supports the idea that there may exist a differential modulation of the motor neuron pools of the sternocleidomastoid and masseter muscles, of peripheral and/or central origin. This suggests that the presence of parafunctional habits and body position could be closely correlated with the clinical symptomatology in these muscles in patients with myogenic craniomandibular dysfunction.

Human-machine interfaces based on EMG and EEG applied to robotic systems.

PubMed

Ferreira, Andre; Celeste, Wanderley C; Cheein, Fernando A; Bastos-Filho, Teodiano F; Sarcinelli-Filho, Mario; Carelli, Ricardo

2008-03-26

Two different Human-Machine Interfaces (HMIs) were developed, both based on electro-biological signals. One is based on the EMG signal and the other is based on the EEG signal. Two major features of such interfaces are their relatively simple data acquisition and processing systems, which need just a few hardware and software resources, so that they are, computationally and financially speaking, low cost solutions. Both interfaces were applied to robotic systems, and their performances are analyzed here. The EMG-based HMI was tested in a mobile robot, while the EEG-based HMI was tested in a mobile robot and a robotic manipulator as well. Experiments using the EMG-based HMI were carried out by eight individuals, who were asked to accomplish ten eye blinks with each eye, in order to test the eye blink detection algorithm. An average rightness rate of about 95% reached by individuals with the ability to blink both eyes allowed to conclude that the system could be used to command devices. Experiments with EEG consisted of inviting 25 people (some of them had suffered cases of meningitis and epilepsy) to test the system. All of them managed to deal with the HMI in only one training session. Most of them learnt how to use such HMI in less than 15 minutes. The minimum and maximum training times observed were 3 and 50 minutes, respectively. Such works are the initial parts of a system to help people with neuromotor diseases, including those with severe dysfunctions. The next steps are to convert a commercial wheelchair in an autonomous mobile vehicle; to implement the HMI onboard the autonomous wheelchair thus obtained to assist people with motor diseases, and to explore the potentiality of EEG signals, making the EEG-based HMI more robust and faster, aiming at using it to help individuals with severe motor dysfunctions.

1 μm-thickness ultra-flexible and high electrode-density surface electromyogram measurement sheet with 2 V organic transistors for prosthetic hand control.

PubMed

Fuketa, Hiroshi; Yoshioka, Kazuaki; Shinozuka, Yasuhiro; Ishida, Koichi; Yokota, Tomoyuki; Matsuhisa, Naoji; Inoue, Yusuke; Sekino, Masaki; Sekitani, Tsuyoshi; Takamiya, Makoto; Someya, Takao; Sakurai, Takayasu

2014-12-01

A 64-channel surface electromyogram (EMG) measurement sheet (SEMS) with 2 V organic transistors on a 1 μm-thick ultra-flexible polyethylene naphthalate (PEN) film is developed for prosthetic hand control. The surface EMG electrodes must satisfy the following three requirements; high mechanical flexibility, high electrode density and high signal integrity. To achieve high electrode density and high signal integrity, a distributed and shared amplifier (DSA) architecture is proposed, which enables an in-situ amplification of the myoelectric signal with a fourfold increase in EMG electrode density. In addition, a post-fabrication select-and-connect (SAC) method is proposed to cope with the large mismatch of organic transistors. The proposed SAC method reduces the area and the power overhead by 96% and 98.2%, respectively, compared with the use of conventional parallel transistors to reduce the transistor mismatch by a factor of 10.

Continuous movement decoding using a target-dependent model with EMG inputs.

PubMed

Sachs, Nicholas A; Corbett, Elaine A; Miller, Lee E; Perreault, Eric J

2011-01-01

Trajectory-based models that incorporate target position information have been shown to accurately decode reaching movements from bio-control signals, such as muscle (EMG) and cortical activity (neural spikes). One major hurdle in implementing such models for neuroprosthetic control is that they are inherently designed to decode single reaches from a position of origin to a specific target. Gaze direction can be used to identify appropriate targets, however information regarding movement intent is needed to determine when a reach is meant to begin and when it has been completed. We used linear discriminant analysis to classify limb states into movement classes based on recorded EMG from a sparse set of shoulder muscles. We then used the detected state transitions to update target information in a mixture of Kalman filters that incorporated target position explicitly in the state, and used EMG activity to decode arm movements. Updating the target position initiated movement along new trajectories, allowing a sequence of appropriately timed single reaches to be decoded in series and enabling highly accurate continuous control.

Intraoperative monitoring of motor symptoms using surface electromyography during stereotactic surgery for movement disorders.

PubMed

Liu, Xuguang; Aziz, Tipu Z; Bain, Peter G

2005-06-01

The authors present practical evidence for the usefulness of intraoperative monitoring with surface electromyograms (sEMGs) from the affected muscles to assist electrode implantation and lesioning in patients with movement disorders. In 22 consecutive patients with various movement disorders, sEMGs were monitored in selected muscles during stereotactic surgery that involved either lesioning or electrode implantation. The electromyograms related to major motor symptoms such as tremor, rigidity, myoclonus, dystonia, and chorea were monitored and characterized on-line by both amplitude and frequency. Major motor symptoms were revealed by sEMGs recorded from the affected muscles. Tremor manifested as highly rhythmic bursts with a narrow frequency band; dyskinesias and chorea appeared as irregularly repeated bursts within a broad frequency range of 1 to 5 Hz; and rigidity and dystonia appeared as sustained high-frequency activity and co-contraction between antagonist muscles. The results suggest that intraoperative monitoring of sEMGs could help to functionally refine and confirm target localization. Surface EMGs could be used (1) as reference signals of the motor symptoms so that other signals, such as the oscillatory local field potentials simultaneously recorded via the implanted electrodes, could be correlated with the sEMGs and used to fine-tune or confirm the target localization; (2) to quantify the effects of acute electrical stimulation on the motor symptoms; and (3) to sensitively detect unwanted capsular responses induced by direct stimulation of the internal capsule. The authors conclude that intraoperative monitoring of sEMGs of the affected muscles of patients with movement disorders during stereotactic surgery provides sensitive and quantitative information that can contribute to improved electrode or lesion placement.

The effects of whole body vibration on EMG activity of the upper extremity muscles in static modified push up position.

PubMed

Ashnagar, Zinat; Shadmehr, Azadeh; Hadian, Mohammadreza; Talebian, Saeed; Jalaei, Shohreh

2016-08-10

Whole Body Vibration (WBV) has been reported to change neuromuscular activity which indirectly assessed by electromyography (EMG). Although researches regarding the influence of WBV on EMG activity of the upper extremity muscles are in their infancy, contradictory findings have been reported as a result of dissimilar protocols. The purpose of this study was to investigate the effects of WBV on electromyography (EMG) activity of upper extremity muscles in static modified push up position. Forty recreationally active females were randomly assigned in WBV and control groups. Participants in WBV group received 5 sets of 30 seconds vibration at 5 mm (peak to peak) and 30 Hz by using vibratory platform. No vibration stimulus was used in the control group. Surface EMG was recorded from Upper Trapezius (UT), Serratus Anterior (SA), Biceps Brachii (BB) and Triceps Brachii (TB) muscles before, during and after the vibration protocol while the subjects maintained the static modified push up position. EMG signals were expressed as root mean square (EMGrms) and normalized by maximum voluntary exertion (MVE). EMGrms activity of the studied muscles increased significantly during the vibration protocol in the WBV group comparing to the control group (P ≤ 0.05). The results indicated that vibration stimulus transmitting via hands increased muscle activity of UT, SA, BB and TB muscles by an average of 206%, 60%, 106% and 120%, respectively, comparing to pre vibration values. These findings suggest that short exposure to the WBV could increase the EMGrms activity of the upper extremity muscles in the static modified push-up position. However, more sessions of WBV application require for a proper judgment.

An Analysis of EMG Electrode Configuration for Targeted Muscle Reinnervation Based Neural Machine Interface

PubMed Central

Huang, He; Zhou, Ping; Li, Guanglin; Kuiken, Todd A.

2015-01-01

Targeted muscle reinnervation (TMR) is a novel neural machine interface for improved myoelectric prosthesis control. Previous high-density (HD) surface electromyography (EMG) studies have indicated that tremendous neural control information can be extracted from the reinnervated muscles by EMG pattern recognition (PR). However, using a large number of EMG electrodes hinders clinical application of the TMR technique. This study investigated a reduced number of electrodes and the placement required to extract sufficient neural control information for accurate identification of user movement intents. An electrode selection algorithm was applied to the HD EMG recordings from each of 4 TMR amputee subjects. The results show that when using only 12 selected bipolar electrodes the average accuracy over subjects for classifying 16 movement intents was 93.0(±3.3)%, just 1.2% lower than when using the entire HD electrode complement. The locations of selected electrodes were consistent with the anatomical reinnervation sites. Additionally, a practical protocol for clinical electrode placement was developed, which does not rely on complex HD EMG experiment and analysis while maintaining a classification accuracy of 88.7±4.5%. These outcomes provide important guidelines for practical electrode placement that can promote future clinical application of TMR and EMG PR in the control of multifunctional prostheses. PMID:18303804

Electromyography and Mechanomyography Signals During Swallowing in Healthy Adults and Head and Neck Cancer Survivors.

PubMed

Constantinescu, Gabriela; Hodgetts, William; Scott, Dylan; Kuffel, Kristina; King, Ben; Brodt, Chris; Rieger, Jana

2017-02-01

Surface electromyography (sEMG) is used as an adjuvant to dysphagia therapy to demonstrate the activity of submental muscles during swallowing exercises. Mechanomyography (MMG) has been suggested as a potential superior alternative to sEMG; however, this advantage is not confirmed for signal acquired from submental muscles. This study compared the signal-to-noise ratio (SNR) obtained from sEMG and MMG sensors during swallowing tasks, in healthy participants and those with a history of head and neck cancer (HNC), a population with altered anatomy and a high incidence of dysphagia. Twenty-two healthy adults and 10 adults with a history of HNC participated in this study. sEMG and MMG signals were acquired during dry, thin liquid, effortful, and Mendelsohn maneuver swallows. SNR was compared between the two sensors using repeated measures ANOVAs and subsequent planned pairwise comparisons. Test-retest measures were collected on 20 % of participants. In healthy participants, MMG SNR was higher than that of sEMG for dry [t(21) = -3.02, p = 0.007] and thin liquid swallows [t(21) = -4.24, p < 0.001]. Although a significant difference for sensor was found in HNC participants F(1,9) = 5.54, p = 0.043, planned pairwise comparisons by task revealed no statistically significant difference between the two sensors. sEMG also showed much better test-retest reliability than MMG. Biofeedback provided as an adjuvant to dysphagia therapy in patients with HNC should employ sEMG technology, as this sensor type yielded better SNR and overall test-retest reliability. Poor MMG test-retest reliability was noted in both healthy and HNC participants and may have been related to differences in sensor application.

Hand and finger dexterity as a function of skin temperature, EMG, and ambient condition.

PubMed

Chen, Wen-Lin; Shih, Yuh-Chuan; Chi, Chia-Fen

2010-06-01

This article examines the changes in skin temperature (finger, hand, forearm), manual performance (hand dexterity and strength), and forearm surface electromyograph (EMG) through 40-min, 11 degrees C water cooling followed by 15-min, 34 degrees C water rewarming; additionally, it explores the relationship between dexterity and the factors of skin temperature, EMG, and ambient condition. Hand exposure in cold conditions is unavoidable and significantly affects manual performance. Two tasks requiring gross and fine dexterity were designed, namely, nut loosening and pin insertion, respectively. The nested-factorial design includes factors of gender, participant (nested within gender), immersion duration, muscle type (for EMG), and location (for skin temperature). The responses are changes in dexterity, skin temperature, normalized amplitude of EMG, and grip strength. Finally, factor analysis and stepwise regression are used to explore factors affecting hand and finger dexterity. Dexterity, EMG, and skin temperature fell with prolonged cooling, but the EMG of the flexor digitorum superficialis remained almost unchanged during the nut loosening task. All responses but the forearm skin temperature recovered to the baseline level at the end of rewarming. The three factors extracted by factor analysis are termed skin temperature, ambient condition, and EMG. They explain approximately two thirds of the variation of the linear models for both dexterities, and the factor of skin temperature is the most influential. Sustained cooling and warming significantly decreases and increases finger, hand, and forearm skin temperature. Dexterity, strength, and EMG are positively correlated to skin temperature. Therefore, keeping the finger, hand, and forearm warm is important to maintaining hand performance. The findings could be helpful to building safety guidelines for working in cold environments.

Hybrid soft computing systems for electromyographic signals analysis: a review.

PubMed

Xie, Hong-Bo; Guo, Tianruo; Bai, Siwei; Dokos, Socrates

2014-02-03

Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis.

A Finite Element Model Approach to Determine the Influence of Electrode Design and Muscle Architecture on Myoelectric Signal Properties

PubMed Central

Teklemariam, A.; Hodson-Tole, E. F.; Reeves, N. D.; Costen, N. P.; Cooper, G.

2016-01-01

Introduction Surface electromyography (sEMG) is the measurement of the electrical activity of the skeletal muscle tissue detected at the skin’s surface. Typically, a bipolar electrode configuration is used. Most muscles have pennate and/or curved fibres, meaning it is not always feasible to align the bipolar electrodes along the fibres direction. Hence, there is a need to explore how different electrode designs can affect sEMG measurements. Method A three layer finite element (skin, fat, muscle) muscle model was used to explore different electrode designs. The implemented model used as source signal an experimentally recorded intramuscular EMG taken from the biceps brachii muscle of one healthy male. A wavelet based intensity analysis of the simulated sEMG signal was performed to analyze the power of the signal in the time and frequency domain. Results The model showed muscle tissue causing a bandwidth reduction (to 20-92- Hz). The inter-electrode distance (IED) and the electrode orientation relative to the fibres affected the total power but not the frequency filtering response. The effect of significant misalignment between the electrodes and the fibres (60°- 90°) could be reduced by increasing the IED (25–30 mm), which attenuates signal cancellation. When modelling pennated fibres, the muscle tissue started to act as a low pass filter. The effect of different IED seems to be enhanced in the pennated model, while the filtering response is changed considerably only when the electrodes are close to the signal termination within the model. For pennation angle greater than 20°, more than 50% of the source signal was attenuated, which can be compensated by increasing the IED to 25 mm. Conclusion Differences in tissue filtering properties, shown in our model, indicates that different electrode designs should be considered for muscle with different geometric properties (i.e. pennated muscles). PMID:26886908

A Finite Element Model Approach to Determine the Influence of Electrode Design and Muscle Architecture on Myoelectric Signal Properties.

PubMed

Teklemariam, A; Hodson-Tole, E F; Reeves, N D; Costen, N P; Cooper, G

2016-01-01

Surface electromyography (sEMG) is the measurement of the electrical activity of the skeletal muscle tissue detected at the skin's surface. Typically, a bipolar electrode configuration is used. Most muscles have pennate and/or curved fibres, meaning it is not always feasible to align the bipolar electrodes along the fibres direction. Hence, there is a need to explore how different electrode designs can affect sEMG measurements. A three layer finite element (skin, fat, muscle) muscle model was used to explore different electrode designs. The implemented model used as source signal an experimentally recorded intramuscular EMG taken from the biceps brachii muscle of one healthy male. A wavelet based intensity analysis of the simulated sEMG signal was performed to analyze the power of the signal in the time and frequency domain. The model showed muscle tissue causing a bandwidth reduction (to 20-92- Hz). The inter-electrode distance (IED) and the electrode orientation relative to the fibres affected the total power but not the frequency filtering response. The effect of significant misalignment between the electrodes and the fibres (60°-90°) could be reduced by increasing the IED (25-30 mm), which attenuates signal cancellation. When modelling pennated fibres, the muscle tissue started to act as a low pass filter. The effect of different IED seems to be enhanced in the pennated model, while the filtering response is changed considerably only when the electrodes are close to the signal termination within the model. For pennation angle greater than 20°, more than 50% of the source signal was attenuated, which can be compensated by increasing the IED to 25 mm. Differences in tissue filtering properties, shown in our model, indicates that different electrode designs should be considered for muscle with different geometric properties (i.e. pennated muscles).

sEMG feature evaluation for identification of elbow angle resolution in graded arm movement.

PubMed

Castro, Maria Claudia F; Colombini, Esther L; Aquino, Plinio T; Arjunan, Sridhar P; Kumar, Dinesh K

2014-11-25

Automatic and accurate identification of elbow angle from surface electromyogram (sEMG) is essential for myoelectric controlled upper limb exoskeleton systems. This requires appropriate selection of sEMG features, and identifying the limitations of such a system.This study has demonstrated that it is possible to identify three discrete positions of the elbow; full extension, right angle, and mid-way point, with window size of only 200 milliseconds. It was seen that while most features were suitable for this purpose, Power Spectral Density Averages (PSD-Av) performed best. The system correctly classified the sEMG against the elbow angle for 100% cases when only two discrete positions (full extension and elbow at right angle) were considered, while correct classification was 89% when there were three discrete positions. However, sEMG was unable to accurately determine the elbow position when five discrete angles were considered. It was also observed that there was no difference for extension or flexion phases.

An online hybrid BCI system based on SSVEP and EMG

NASA Astrophysics Data System (ADS)

Lin, Ke; Cinetto, Andrea; Wang, Yijun; Chen, Xiaogang; Gao, Shangkai; Gao, Xiaorong

2016-04-01

Objective. A hybrid brain-computer interface (BCI) is a device combined with at least one other communication system that takes advantage of both parts to build a link between humans and machines. To increase the number of targets and the information transfer rate (ITR), electromyogram (EMG) and steady-state visual evoked potential (SSVEP) were combined to implement a hybrid BCI. A multi-choice selection method based on EMG was developed to enhance the system performance. Approach. A 60-target hybrid BCI speller was built in this study. A single trial was divided into two stages: a stimulation stage and an output selection stage. In the stimulation stage, SSVEP and EMG were used together. Every stimulus flickered at its given frequency to elicit SSVEP. All of the stimuli were divided equally into four sections with the same frequency set. The frequency of each stimulus in a section was different. SSVEPs were used to discriminate targets in the same section. Different sections were classified using EMG signals from the forearm. Subjects were asked to make different number of fists according to the target section. Canonical Correlation Analysis (CCA) and mean filtering was used to classify SSVEP and EMG separately. In the output selection stage, the top two optimal choices were given. The first choice with the highest probability of an accurate classification was the default output of the system. Subjects were required to make a fist to select the second choice only if the second choice was correct. Main results. The online results obtained from ten subjects showed that the mean accurate classification rate and ITR were 81.0% and 83.6 bits min-1 respectively only using the first choice selection. The ITR of the hybrid system was significantly higher than the ITR of any of the two single modalities (EMG: 30.7 bits min-1, SSVEP: 60.2 bits min-1). After the addition of the second choice selection and the correction task, the accurate classification rate and ITR was

Low-power polling mode of the next-generation IMES2 implantable wireless EMG sensor.

PubMed

DeMichele, Glenn A; Hu, Zhe; Troyk, Philip R; Chen, Hongnan; Weir, Richard F ff

2014-01-01

The IMES1 Implantable MyoElectric Sensor device is currently in human clinical trials led by the Alfred Mann Foundation. The IMES is implanted in a residual limb and is powered wirelessly using a magnetic field. EMG signals resulting from the amputee's voluntary movement are amplified and transmitted wirelessly by the IMES to an external controller which controls movement of an external motorized prosthesis. Development of the IMES technology is on-going, producing the next-generation IMES2. Among various improvements, a new feature of the IMES2 is a low-power polling mode. In this low-power mode, the IMES2 power consumption can be dramatically reduced when the limb is inactive through the use of a polled sampling. With the onset of EMG activity, the IMES2 system can switch to the normal higher sample rate to allow the acquisition of high-fidelity EMG data for prosthesis control.

Wavelet-based unsupervised learning method for electrocardiogram suppression in surface electromyograms.

PubMed

Niegowski, Maciej; Zivanovic, Miroslav

2016-03-01

We present a novel approach aimed at removing electrocardiogram (ECG) perturbation from single-channel surface electromyogram (EMG) recordings by means of unsupervised learning of wavelet-based intensity images. The general idea is to combine the suitability of certain wavelet decomposition bases which provide sparse electrocardiogram time-frequency representations, with the capacity of non-negative matrix factorization (NMF) for extracting patterns from images. In order to overcome convergence problems which often arise in NMF-related applications, we design a novel robust initialization strategy which ensures proper signal decomposition in a wide range of ECG contamination levels. Moreover, the method can be readily used because no a priori knowledge or parameter adjustment is needed. The proposed method was evaluated on real surface EMG signals against two state-of-the-art unsupervised learning algorithms and a singular spectrum analysis based method. The results, expressed in terms of high-to-low energy ratio, normalized median frequency, spectral power difference and normalized average rectified value, suggest that the proposed method enables better ECG-EMG separation quality than the reference methods. Copyright © 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

EMG1 is essential for mouse pre-implantation embryo development.

PubMed

Wu, Xiaoli; Sandhu, Sumit; Patel, Nehal; Triggs-Raine, Barbara; Ding, Hao

2010-09-21

Essential for mitotic growth 1 (EMG1) is a highly conserved nucleolar protein identified in yeast to have a critical function in ribosome biogenesis. A mutation in the human EMG1 homolog causes Bowen-Conradi syndrome (BCS), a developmental disorder characterized by severe growth failure and psychomotor retardation leading to death in early childhood. To begin to understand the role of EMG1 in mammalian development, and how its deficiency could lead to Bowen-Conradi syndrome, we have used mouse as a model. The expression of Emg1 during mouse development was examined and mice carrying a null mutation for Emg1 were generated and characterized. Our studies indicated that Emg1 is broadly expressed during early mouse embryonic development. However, in late embryonic stages and during postnatal development, Emg1 exhibited specific expression patterns. To assess a developmental role for EMG1 in vivo, we exploited a mouse gene-targeting approach. Loss of EMG1 function in mice arrested embryonic development prior to the blastocyst stage. The arrested Emg1-/- embryos exhibited defects in early cell lineage-specification as well as in nucleologenesis. Further, loss of p53, which has been shown to rescue some phenotypes resulting from defects in ribosome biogenesis, failed to rescue the Emg1-/- pre-implantation lethality. Our data demonstrate that Emg1 is highly expressed during mouse embryonic development, and essential for mouse pre-implantation development. The absolute requirement for EMG1 in early embryonic development is consistent with its essential role in yeast. Further, our findings also lend support to the previous study that showed Bowen-Conradi syndrome results from a partial EMG1 deficiency. A complete deficiency would not be expected to be compatible with a live birth.

Hybrid soft computing systems for electromyographic signals analysis: a review

PubMed Central

2014-01-01

Electromyographic (EMG) is a bio-signal collected on human skeletal muscle. Analysis of EMG signals has been widely used to detect human movement intent, control various human-machine interfaces, diagnose neuromuscular diseases, and model neuromusculoskeletal system. With the advances of artificial intelligence and soft computing, many sophisticated techniques have been proposed for such purpose. Hybrid soft computing system (HSCS), the integration of these different techniques, aims to further improve the effectiveness, efficiency, and accuracy of EMG analysis. This paper reviews and compares key combinations of neural network, support vector machine, fuzzy logic, evolutionary computing, and swarm intelligence for EMG analysis. Our suggestions on the possible future development of HSCS in EMG analysis are also given in terms of basic soft computing techniques, further combination of these techniques, and their other applications in EMG analysis. PMID:24490979

Compression of electromyographic signals using image compression techniques.

PubMed

Costa, Marcus Vinícius Chaffim; Berger, Pedro de Azevedo; da Rocha, Adson Ferreira; de Carvalho, João Luiz Azevedo; Nascimento, Francisco Assis de Oliveira

2008-01-01

Despite the growing interest in the transmission and storage of electromyographic signals for long periods of time, few studies have addressed the compression of such signals. In this article we present an algorithm for compression of electromyographic signals based on the JPEG2000 coding system. Although the JPEG2000 codec was originally designed for compression of still images, we show that it can also be used to compress EMG signals for both isotonic and isometric contractions. For EMG signals acquired during isometric contractions, the proposed algorithm provided compression factors ranging from 75 to 90%, with an average PRD ranging from 3.75% to 13.7%. For isotonic EMG signals, the algorithm provided compression factors ranging from 75 to 90%, with an average PRD ranging from 3.4% to 7%. The compression results using the JPEG2000 algorithm were compared to those using other algorithms based on the wavelet transform.

Analysis of using EMG and mechanical sensors to enhance intent recognition in powered lower limb prostheses

NASA Astrophysics Data System (ADS)

Young, A. J.; Kuiken, T. A.; Hargrove, L. J.

2014-10-01

Objective. The purpose of this study was to determine the contribution of electromyography (EMG) data, in combination with a diverse array of mechanical sensors, to locomotion mode intent recognition in transfemoral amputees using powered prostheses. Additionally, we determined the effect of adding time history information using a dynamic Bayesian network (DBN) for both the mechanical and EMG sensors. Approach. EMG signals from the residual limbs of amputees have been proposed to enhance pattern recognition-based intent recognition systems for powered lower limb prostheses, but mechanical sensors on the prosthesis—such as inertial measurement units, position and velocity sensors, and load cells—may be just as useful. EMG and mechanical sensor data were collected from 8 transfemoral amputees using a powered knee/ankle prosthesis over basic locomotion modes such as walking, slopes and stairs. An offline study was conducted to determine the benefit of different sensor sets for predicting intent. Main results. EMG information was not as accurate alone as mechanical sensor information (p < 0.05) for any classification strategy. However, EMG in combination with the mechanical sensor data did significantly reduce intent recognition errors (p < 0.05) both for transitions between locomotion modes and steady-state locomotion. The sensor time history (DBN) classifier significantly reduced error rates compared to a linear discriminant classifier for steady-state steps, without increasing the transitional error, for both EMG and mechanical sensors. Combining EMG and mechanical sensor data with sensor time history reduced the average transitional error from 18.4% to 12.2% and the average steady-state error from 3.8% to 1.0% when classifying level-ground walking, ramps, and stairs in eight transfemoral amputee subjects. Significance. These results suggest that a neural interface in combination with time history methods for locomotion mode classification can enhance intent

Surface-EMG analysis for the quantification of thigh muscle dynamic co-contractions during normal gait.

PubMed

Strazza, Annachiara; Mengarelli, Alessandro; Fioretti, Sandro; Burattini, Laura; Agostini, Valentina; Knaflitz, Marco; Di Nardo, Francesco

2017-01-01

The research purpose was to quantify the co-contraction patterns of quadriceps femoris (QF) vs. hamstring muscles during free walking, in terms of onset-offset muscular activation, excitation intensity, and occurrence frequency. Statistical gait analysis was performed on surface-EMG signals from vastus lateralis (VL), rectus femoris (RF), and medial hamstrings (MH), in 16315 strides walked by 30 healthy young adults. Results showed full superimpositions of MH with both VL and RF activity from terminal swing, 80 to 100% of gait cycle (GC), to the successive loading response (≈0-15% of GC), in around 90% of the considered strides. A further superimposition was detected during the push-off phase both between VL and MH activation intervals (38.6±12.8% to 44.1±9.6% of GC) in 21.9±13.6% of strides, and between RF and MH activation intervals (45.9±5.3% to 50.7±9.7 of GC) in 32.7±15.1% of strides. These findings led to identify three different co-contractions among QF and hamstring muscles during able-bodied walking: in early stance (in ≈90% of strides), in push-off (in 25-30% of strides) and in terminal swing (in ≈90% of strides). The co-contraction in terminal swing is the one with the highest levels of muscle excitation intensity. To our knowledge, this analysis represents the first attempt for quantification of QF/hamstring muscles co-contraction in young healthy subjects during normal gait, able to include the physiological variability of the phenomenon. Copyright © 2016 Elsevier B.V. All rights reserved.

Low-Power Polling Mode of the Next-Generation IMES2 Implantable Wireless EMG Sensor

PubMed Central

DeMichele, Glenn A.; Hu, Zhe; Troyk, Philip R.; Chen, Hongnan; Weir, Richard F. ff.

2015-01-01

The IMES1 Implantable MyoElectric Sensor device is currently in human clinical trials led by the Alfred Mann Foundation. The IMES is implanted in a residual limb and is powered wirelessly using a magnetic field. EMG signals resulting from the amputee’s voluntary movement are amplified and transmitted wirelessly by the IMES to an external controller which controls movement of an external motorized prosthesis. Development of the IMES technology is on-going, producing the next-generation IMES2. Among various improvements, a new feature of the IMES2 is a lowpower polling mode. In this low-power mode, the IMES2 power consumption can be dramatically reduced when the limb is inactive through the use of a polled sampling. With the onset of EMG activity, the IMES2 system can switch to the normal higher sample rate to allow the acquisition of high-fidelity EMG data for prosthesis control. PMID:25570642

History dependence of the EMG-torque relationship.

PubMed

Paquin, James; Power, Geoffrey A

2018-05-28

The influence of active lengthening (residual force enhancement: RFE) and shortening (force depression: FD) on the electromyography (EMG)-torque relationship was investigated by matching torque and activation at 20%, 40%, 60%, 80% and 100% maximal voluntary contraction (MVC). Sixteen males performed lengthening and shortening contractions of the dorsiflexors over 25° into an isometric steady-state. There was 5% greater torque, with no change in agonist EMG during the RFE condition as compared to the isometric condition. Sub-maximally, in the force enhanced state, there was less agonist EMG during the torque clamp at all intensities relative to isometric, and greater torque during the activation clamps relative to isometric was observed across all intensities except 20% MVC. During the FD state compared to isometric, there was less torque produced during MVC (∼15%) with no change in agonist EMG. Sub-maximally, in the FD state, there was greater agonist EMG during the torque clamp and less torque during the activation clamp relative to the isometric condition across all intensities. The EMG-torque relationship was bilinear for all contraction types but was shifted to the left and right for FD and RFE, respectively as compared with isometric, indicating altered neuromuscular activation strategies in the history-dependent states of RFE and FD. Copyright © 2018. Published by Elsevier Ltd.

One-Channel Surface Electromyography Decomposition for Muscle Force Estimation.

PubMed

Sun, Wentao; Zhu, Jinying; Jiang, Yinlai; Yokoi, Hiroshi; Huang, Qiang

2018-01-01

Estimating muscle force by surface electromyography (sEMG) is a non-invasive and flexible way to diagnose biomechanical diseases and control assistive devices such as prosthetic hands. To estimate muscle force using sEMG, a supervised method is commonly adopted. This requires simultaneous recording of sEMG signals and muscle force measured by additional devices to tune the variables involved. However, recording the muscle force of the lost limb of an amputee is challenging, and the supervised method has limitations in this regard. Although the unsupervised method does not require muscle force recording, it suffers from low accuracy due to a lack of reference data. To achieve accurate and easy estimation of muscle force by the unsupervised method, we propose a decomposition of one-channel sEMG signals into constituent motor unit action potentials (MUAPs) in two steps: (1) learning an orthogonal basis of sEMG signals through reconstruction independent component analysis; (2) extracting spike-like MUAPs from the basis vectors. Nine healthy subjects were recruited to evaluate the accuracy of the proposed approach in estimating muscle force of the biceps brachii. The results demonstrated that the proposed approach based on decomposed MUAPs explains more than 80% of the muscle force variability recorded at an arbitrary force level, while the conventional amplitude-based approach explains only 62.3% of this variability. With the proposed approach, we were also able to achieve grip force control of a prosthetic hand, which is one of the most important clinical applications of the unsupervised method. Experiments on two trans-radial amputees indicated that the proposed approach improves the performance of the prosthetic hand in grasping everyday objects.

An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control.

PubMed

Adewuyi, Adenike A; Hargrove, Levi J; Kuiken, Todd A

2016-04-01

Pattern recognition control combined with surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for control of multiple prosthetic functions for transradial amputees. There is, however, a need to adapt this control method when implemented for partial-hand amputees, who possess both a functional wrist and information-rich residual intrinsic hand muscles. We demonstrate that combining EMG data from both intrinsic and extrinsic hand muscles to classify hand grasps and finger motions allows up to 19 classes of hand grasps and individual finger motions to be decoded, with an accuracy of 96% for non-amputees and 85% for partial-hand amputees. We evaluated real-time pattern recognition control of three hand motions in seven different wrist positions. We found that a system trained with both intrinsic and extrinsic muscle EMG data, collected while statically and dynamically varying wrist position increased completion rates from 73% to 96% for partial-hand amputees and from 88% to 100% for non-amputees when compared to a system trained with only extrinsic muscle EMG data collected in a neutral wrist position. Our study shows that incorporating intrinsic muscle EMG data and wrist motion can significantly improve the robustness of pattern recognition control for application to partial-hand prosthetic control.

An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control

PubMed Central

Adewuyi, Adenike A.; Hargrove, Levi J.; Kuiken, Todd A.

2015-01-01

Pattern recognition control combined with surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for control of multiple prosthetic functions for transradial amputees. There is, however, a need to adapt this control method when implemented for partial-hand amputees, who possess both a functional wrist and information-rich residual intrinsic hand muscles. We demonstrate that combining EMG data from both intrinsic and extrinsic hand muscles to classify hand grasps and finger motions allows up to 19 classes of hand grasps and individual finger motions to be decoded, with an accuracy of 96% for non-amputees and 85% for partial-hand amputees. We evaluated real-time pattern recognition control of three hand motions in seven different wrist positions. We found that a system trained with both intrinsic and extrinsic muscle EMG data, collected while statically and dynamically varying wrist position increased completion rates from 73% to 96% for partial-hand amputees and from 88% to 100% for non-amputees when compared to a system trained with only extrinsic muscle EMG data collected in a neutral wrist position. Our study shows that incorporating intrinsic muscle EMG data and wrist motion can significantly improve the robustness of pattern recognition control for partial-hand applications. PMID:25955989

Fractal based modelling and analysis of electromyography (EMG) to identify subtle actions.

PubMed

Arjunan, Sridhar P; Kumar, Dinesh K

2007-01-01

The paper reports the use of fractal theory and fractal dimension to study the non-linear properties of surface electromyogram (sEMG) and to use these properties to classify subtle hand actions. The paper reports identifying a new feature of the fractal dimension, the bias that has been found to be useful in modelling the muscle activity and of sEMG. Experimental results demonstrate that the feature set consisting of bias values and fractal dimension of the recordings is suitable for classification of sEMG against the different hand gestures. The scatter plots demonstrate the presence of simple relationships of these features against the four hand gestures. The results indicate that there is small inter-experimental variation but large inter-subject variation. This may be due to differences in the size and shape of muscles for different subjects. The possible applications of this research include use in developing prosthetic hands, controlling machines and computers.

Comparison of methods for removing electromagnetic noise from electromyographic signals.

PubMed

Defreitas, Jason M; Beck, Travis W; Stock, Matt S

2012-02-01

The purpose of this investigation was to compare three different methods of removing noise from monopolar electromyographic (EMG) signals: (a) electrical shielding with a Faraday cage, (b) denoising with a digital notch-filter and (c) applying a bipolar differentiation with another monopolar EMG signal. Ten men and ten women (mean age = 24.0 years) performed isometric muscle actions of the leg extensors at 10-100% of their maximal voluntary contraction on two separate occasions. One trial was performed inside a Faraday tent (a flexible Faraday cage made from conductive material), and the other was performed outside the Faraday tent. The EMG signals collected outside the Faraday tent were analyzed three separate ways: as a raw signal, as a bipolar signal, and as a signal digitally notch filtered to remove 60 Hz noise and its harmonics. The signal-to-noise ratios were greatest after notch-filtering (range: 3.0-33.8), and lowest for the bipolar arrangement (1.6-10.2). Linear slope coefficients for the EMG amplitude versus force relationship were also used to compare the methods of noise removal. The results showed that a bipolar arrangement had a significantly lower linear slope coefficient when compared to the three other conditions (raw, notch and tent). These results suggested that an appropriately filtered monopolar EMG signal can be useful in situations that require a large pick-up area. Furthermore, although it is helpful, a Faraday tent (or cage) is not required to achieve an appropriate signal-to-noise ratio, as long as the correct filters are applied.

Usefulness of BFB/EMG in facial palsy rehabilitation.

PubMed

Dalla Toffola, Elena; Bossi, Daniela; Buonocore, Michelangelo; Montomoli, Cristina; Petrucci, Lucia; Alfonsi, Enrico

2005-07-22

To analyze and to compare the recovery and the development of synkinesis in patients with idiopathic facial palsy (Bell's palsy) following treatment with two methods of rehabilitation, kinesitherapy (KT) and biofeedback/EMG (BFB/EMG). Retrospective cases--series review. Seventy-four patients with Bell' palsy were clinically evaluated within 1 month from onset of palsy and at 12 months after palsy (House scale and synkinesis evaluation). Electromyography (EMG) and Electroneurography (ENG) were performed about 4 weeks after palsy to better evaluate functional abnormalities due to facial nerve lesion. The patients followed two different protocols for rehabilitation: the first 32 patients were treated with therapeutic exercises performed by therapists (KT group), the latter 42 patients were treated using BFB/EMG methods (BFB group) with inhibition of synkinetic movement as the primary goal. KT and BFB patients were evaluated for clinical and neurophysiological characteristics before rehabilitative treatment. BFB patients showed better clinical recovery and minor synkinesis than KT patients. BFB/EMG seems to be more useful than KT in Bell's palsy treatment. This could be due to the fact that BFB/EMG gives more accurate information than KT on muscle activation with better modulation in voluntary recruitment of motor unit.

Comparison of conventional filtering and independent component analysis for artifact reduction in simultaneous gastric EMG and magnetogastrography from porcines.

PubMed

Irimia, Andrei; Richards, William O; Bradshaw, L Alan

2009-11-01

In this study, we perform a comparative study of independent component analysis (ICA) and conventional filtering (CF) for the purpose of artifact reduction from simultaneous gastric EMG and magnetogastrography (MGG). EMG/MGG data were acquired from ten anesthetized pigs by obtaining simultaneous recordings using serosal electrodes (EMG) as well as with a superconducting quantum interference device biomagnetometer (MGG). The analysis of MGG waveforms using ICA and CF indicates that ICA is superior to the CF method in its ability to extract respiration and cardiac artifacts from MGG recordings. A signal frequency analysis of ICA- and CF-processed data was also undertaken using waterfall plots, and it was determined that the two methods produce qualitatively comparable results. Through the use of simultaneous EMG/MGG, we were able to demonstrate the accuracy and trustworthiness of our results by comparison and cross-validation within the framework of a porcine model.

An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot.

PubMed

Kiguchi, K; Hayashi, Y

2012-08-01

Many kinds of power-assist robots have been developed in order to assist self-rehabilitation and/or daily life motions of physically weak persons. Several kinds of control methods have been proposed to control the power-assist robots according to user's motion intention. In this paper, an electromyogram (EMG)-based impedance control method for an upper-limb power-assist exoskeleton robot is proposed to control the robot in accordance with the user's motion intention. The proposed method is simple, easy to design, humanlike, and adaptable to any user. A neurofuzzy matrix modifier is applied to make the controller adaptable to any users. Not only the characteristics of EMG signals but also the characteristics of human body are taken into account in the proposed method. The effectiveness of the proposed method was evaluated by the experiments.

Evaluation of Head Orientation and Neck Muscle EMG Signals as Command Inputs to a Human-Computer Interface for Individuals with High Tetraplegia

PubMed Central

Williams, Matthew R.; Kirsch, Robert F.

2013-01-01

We investigated the performance of three user interfaces for restoration of cursor control in individuals with tetraplegia: head orientation, EMG from face and neck muscles, and a standard computer mouse (for comparison). Subjects engaged in a 2D, center-out, Fitts’ Law style task and performance was evaluated using several measures. Overall, head orientation commanded motion resembled mouse commanded cursor motion (smooth, accurate movements to all targets), although with somewhat lower performance. EMG commanded movements exhibited a higher average speed, but other performance measures were lower, particularly for diagonal targets. Compared to head orientation, EMG as a cursor command source was less accurate, was more affected by target direction and was more prone to overshoot the target. In particular, EMG commands for diagonal targets were more sequential, moving first in one direction and then the other rather than moving simultaneous in the two directions. While the relative performance of each user interface differs, each has specific advantages depending on the application. PMID:18990652

Test-retest reliability of cardinal plane isokinetic hip torque and EMG.

PubMed

Claiborne, Tina L; Timmons, Mark K; Pincivero, Danny M

2009-10-01

The objective of the present study was to establish test-retest reliability of isokinetic hip torque and prime mover electromyogram (EMG) through the three cardinal planes of motion. Thirteen healthy young adults participated in two experimental sessions, separated by approximately one week. During each session, isokinetic hip torque was evaluated on the Biodex Isokinetic Dynamometer at a velocity of 60 deg/s. Subjects performed three maximal-effort concentric and eccentric contractions, separately, for right and left hip abduction/adduction, flexion/extension, and internal/external rotation. Surface EMGs were sampled from the gluteus maximus, gluteus medius, adductor, medial and lateral hamstring, and rectus femoris muscles during all contractions. Intraclass correlation coefficients (ICC - 2,1) and standard errors of measurement (SEM) were calculated for peak torque for each movement direction and contraction mode, while ICCs were only computed for the EMG data. Motions that demonstrated high torque reliability included concentric hip abduction (right and left), flexion (right and left), extension (right) and internal rotation (right and left), and eccentric hip abduction (left), adduction (left), flexion (right), and extension (right and left) (ICC range=0.81-0.91). Motions with moderate torque reliability included concentric hip adduction (right), extension (left), internal rotation (left), and external rotation (right), and eccentric hip abduction and adduction (right), flexion (left), internal rotation (right and left), and external rotation (right and left) (ICC range=0.49-0.79). The majority of the EMG sampled muscles (n=12 and n=11 for concentric and eccentric contractions, respectively) demonstrated high reliability (ICC=0.81-0.95). Instances of low, or unacceptable, EMG reliability values occurred for the medial hamstring muscle of the left leg (both contraction modes) and the adductor muscle of the right leg during eccentric internal rotation. The major

Curved Microneedle Array-Based sEMG Electrode for Robust Long-Term Measurements and High Selectivity

PubMed Central

Kim, Minjae; Kim, Taewan; Kim, Dong Sung; Chung, Wan Kyun

2015-01-01

Surface electromyography is widely used in many fields to infer human intention. However, conventional electrodes are not appropriate for long-term measurements and are easily influenced by the environment, so the range of applications of sEMG is limited. In this paper, we propose a flexible band-integrated, curved microneedle array electrode for robust long-term measurements, high selectivity, and easy applicability. Signal quality, in terms of long-term usability and sensitivity to perspiration, was investigated. Its motion-discriminating performance was also evaluated. The results show that the proposed electrode is robust to perspiration and can maintain a high-quality measuring ability for over 8 h. The proposed electrode also has high selectivity for motion compared with a commercial wet electrode and dry electrode. PMID:26153773

Quantitative analysis of four EMG amplifiers.

PubMed

Perreault, E J; Hunter, I W; Kearney, R E

1993-09-01

Four typical EMG amplifiers were tested quantitatively to observe the diversity and specificity of available equipment. Gain, phase, common mode rejection ratio (CMRR) and noise characteristics were measured for each device. Various gain and phase responses were observed, each best suited to specific application areas. For all amplifiers, the CMRR was shown to decrease dramatically in the presence of input impedance mismatches of more than 10 k omega between the two electrodes. Because such impedance mismatches are common on the skin surface, these results indicate that proper skin preparation is required to maximize the noise rejection capabilities of the tested amplifiers.

A combined sEMG and accelerometer system for monitoring functional activity in stroke.

PubMed

Roy, Serge H; Cheng, M Samuel; Chang, Shey-Sheen; Moore, John; De Luca, Gianluca; Nawab, S Hamid; De Luca, Carlo J

2009-12-01

Remote monitoring of physical activity using body-worn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in patients with stroke. sEMG and ACC data (eight channels each) were recorded from 10 hemiparetic patients while they carried out a sequence of 11 activities of daily living (identification tasks), and 10 activities used to evaluate misclassification errors (nonidentification tasks). The sEMG and ACC sensor data were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system to identify the minimal sensor configuration needed to accurately classify the identification tasks, with a minimal number of misclassifications from the nonidentification tasks. The results demonstrated that the highest sensitivity and specificity for the identification tasks was achieved using a subset of four ACC sensors and adjacent sEMG sensors located on both upper arms, one forearm, and one thigh, respectively. This configuration resulted in a mean sensitivity of 95.0%, and a mean specificity of 99.7% for the identification tasks, and a mean misclassification error of < 10% for the nonidentification tasks. The findings support the feasibility of a hybrid sEMG and ACC wearable sensor system for automatic recognition of motor tasks used to assess functional independence in patients with stroke.

A Combined sEMG and Accelerometer System for Monitoring Functional Activity in Stroke.

PubMed

Roy, S; Cheng, M; Chang, S; Moore, J; De Luca, G; Nawab, S; De Luca, C

2014-04-23

Remote monitoring of physical activity using bodyworn sensors provides an alternative to assessment of functional independence by subjective, paper-based questionnaires. This study investigated the classification accuracy of a combined surface electromyographic (sEMG) and accelerometer (ACC) sensor system for monitoring activities of daily living in patients with stroke. sEMG and ACC data were recorded from 10 hemi paretic patients while they carried out a sequence of 11 activities of daily living (Identification tasks), and 10 activities used to evaluate misclassification errors (non-Identification tasks). The sEMG and ACC sensor data were analyzed using a multilayered neural network and an adaptive neuro-fuzzy inference system to identify the minimal sensor configuration needed to accurately classify the identification tasks, with a minimal number of misclassifications from the non-Identification tasks. The results demonstrated that the highest sensitivity and specificity for the identification tasks was achieved using a subset of 4 ACC sensors and adjacent sEMG sensors located on both upper arms, one forearm, and one thigh, respectively. This configuration resulted in a mean sensitivity of 95.0 %, and a mean specificity of 99.7 % for the identification tasks, and a mean misclassification error of < 10% for the non-Identification tasks. The findings support the feasibility of a hybrid sEMG and ACC wearable sensor system for automatic recognition of motor tasks used to assess functional independence in patients with stroke.

Measuring leg movements during sleep using accelerometry: comparison with EMG and piezo-electric scored events.

PubMed

Terrill, Philip I; Leong, Matthew; Barton, Katrina; Freakley, Craig; Downey, Carl; Vanniekerk, Mark; Jorgensen, Greg; Douglas, James

2013-01-01

Periodic Limb Movements during Sleep (PLMS) can cause significant disturbance to sleep, resulting in daytime sleepiness and reduced quality of life. In conventional clinical practice, PLMS are measured using overnight electromyogram (EMG) of the tibialis anterior muscle, although historically they have also been measured using piezo-electric gauges placed over the muscle. However, PLMS counts (PLM index) do not correlate well with clinical symptomology. In this study, we propose that because EMG and piezo derived signals measure muscle activation rather than actual movement, they may count events with no appreciable movement of the limb and therefore no contribution to sleep disturbance. The aim of this study is thus to determine the percentage of clinically scored limb movements which are not associated with movement of the great toe measured using accelerometry. 9 participants were studied simultaneously with an overnight diagnostic polysomnogram (including EMG and piezo instrumentation of the right leg) and high temporal resolution accelerometry of the right great toe. Limb movements were scored, and peak acceleration during each scored movement was quantified. Across the participant population, 54.9% (range: 26.7-76.3) and 39.0% (range: 4.8-69.6) of limb movements scored using piezo and EMG instrumentation respectively, were not associated with toe movement measured with accelerometry. If sleep disturbance is the consequence of the limb movements, these results may explain why conventional piezo or EMG derived PLMI is poorly correlated with clinical symptomology.

Influence of post-stroke spasticity on EMG-force coupling and force steadiness in biceps brachii.

PubMed

Carlyle, Jennilee K; Mochizuki, George

2018-02-01

Individuals with spasticity after stroke experience a decrease in force steadiness which can impact function. Alterations in the strength of EMG-force coupling may contribute to the reduction in force steadiness observed in spasticity. The aim was to determine the extent to which force steadiness and EMG-force coupling is affected by post-stroke spasticity. This cross-sectional study involved individuals with upper limb spasticity after stroke. Participants were required to generate and maintain isometric contractions of the elbow flexors at varying force levels. Coefficient of variation of force, absolute force, EMG-force cross-correlation function peak and peak latency was measured from both limbs with surface electromyography and isometric dynamometry. Statistically significant differences were observed between the affected and less affected limbs for all outcome measures. Significant main effects of force level were also observed. Force steadiness was not statistically significantly correlated with EMG-force coupling; however, both force steadiness and absolute force were associated with the level of impairment as measured by the Chedoke McMaster Stroke Assessment Scale. Spasticity after stroke uncouples the relationship between EMG and force and is associated with reduced force steadiness during isometric contractions; however, these features of control are not associated in individuals with spasticity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Natural mediotrusive contact: does it affect the masticatory and neck EMG activity during tooth grinding?

PubMed

Fuentes, Aler D; Martin, Conchita; Bull, Ricardo; Santander, Hugo; Gutiérrez, Mario F; Miralles, Rodolfo

2015-12-29

There is scarce knowledge regarding the influence of a natural mediotrusive contact on mandibular and cervical muscular activity. The purpose of this study was to analyze the EMG activity of the anterior temporalis (AT) and sternocleidomastoid (SCM) muscles during awake grinding in healthy subjects with or without a natural mediotrusive occlusal contact. 15 subjects with natural mediotrusive occlusal contact (Group 1) and 15 subjects without natural mediotrusive occlusal contact (Group 2) participated. Bilateral surface EMG activity of AT and SCM muscles was recorded during unilateral eccentric or concentric tooth grinding tasks. EMG activity was normalized against the activity recorded during maximal voluntary clenching in intercuspal position (IP) for AT muscles and during maximal intentional isometric head-neck rotation to each side, for SCM muscles. EMG activity of AT and SCM muscles showed no statistical difference between groups. EMG activity of AT muscle was higher in the working side (WS) than in the non-WS (NWS) in Group 1 during concentric grinding (0.492 vs 0.331, P = 0.047), whereas no difference was observed in Group 2. EMG activity of SCM was similar between working and NWSs in both groups and tasks. Asymmetry indexes (AIs) were not significantly different between groups. These findings in healthy subjects support the assumption that during awake tooth grinding, central nerve control predominates over peripheral inputs, and reinforce the idea of a functional link between the motor-neuron pools that control jaw and neck muscles.

Natural mediotrusive contact: does it affect the masticatory and neck EMG activity during tooth grinding?

PubMed

Fuentes, Aler D; Martin, Conchita; Bull, Ricardo; Santander, Hugo; Gutiérrez, Mario F; Miralles, Rodolfo

2016-07-01

There is scarce knowledge regarding the influence of a natural mediotrusive contact on mandibular and cervical muscular activity. The purpose of this study was to analyze the EMG activity of the anterior temporalis (AT) and sternocleidomastoid (SCM) muscles during awake grinding in healthy subjects with or without a natural mediotrusive occlusal contact. Fifteen subjects with natural mediotrusive occlusal contact (Group 1) and 15 subjects without natural mediotrusive occlusal contact (Group 2) participated. Bilateral surface EMG activity of AT and SCM muscles was recorded during unilateral eccentric or concentric tooth grinding tasks. EMG activity was normalized against the activity recorded during maximal voluntary clenching in intercuspal position (IP) for AT muscles and during maximal intentional isometric head-neck rotation to each side, for SCM muscles. EMG activity of AT and SCM muscles showed no statistical difference between groups. EMG activity of AT muscle was higher in the working side (WS) than in the non-WS (NWS) in Group 1 during concentric grinding (0.492 vs 0.331, p = 0.047), whereas no difference was observed in Group 2. EMG activity of SCM was similar between working and NWSs in both groups and tasks. Asymmetry indexes (AIs) were not significantly different between groups. These findings in healthy subjects support the assumption that during awake tooth grinding, central nerve control predominates over peripheral inputs, and reinforce the idea of a functional link between the motor-neuron pools that control jaw and neck muscles.

Neural Correlates of Facial Mimicry: Simultaneous Measurements of EMG and BOLD Responses during Perception of Dynamic Compared to Static Facial Expressions

PubMed Central

Rymarczyk, Krystyna; Żurawski, Łukasz; Jankowiak-Siuda, Kamila; Szatkowska, Iwona

2018-01-01

Facial mimicry (FM) is an automatic response to imitate the facial expressions of others. However, neural correlates of the phenomenon are as yet not well established. We investigated this issue using simultaneously recorded EMG and BOLD signals during perception of dynamic and static emotional facial expressions of happiness and anger. During display presentations, BOLD signals and zygomaticus major (ZM), corrugator supercilii (CS) and orbicularis oculi (OO) EMG responses were recorded simultaneously from 46 healthy individuals. Subjects reacted spontaneously to happy facial expressions with increased EMG activity in ZM and OO muscles and decreased CS activity, which was interpreted as FM. Facial muscle responses correlated with BOLD activity in regions associated with motor simulation of facial expressions [i.e., inferior frontal gyrus, a classical Mirror Neuron System (MNS)]. Further, we also found correlations for regions associated with emotional processing (i.e., insula, part of the extended MNS). It is concluded that FM involves both motor and emotional brain structures, especially during perception of natural emotional expressions. PMID:29467691

Corticomuscular transmission of tremor signals by propriospinal neurons in Parkinson's disease.

PubMed

Hao, Manzhao; He, Xin; Xiao, Qin; Alstermark, Bror; Lan, Ning

2013-01-01

Cortical oscillatory signals of single and double tremor frequencies act together to cause tremor in the peripheral limbs of patients with Parkinson's disease (PD). But the corticospinal pathway that transmits the tremor signals has not been clarified, and how alternating bursts of antagonistic muscle activations are generated from the cortical oscillatory signals is not well understood. This paper investigates the plausible role of propriospinal neurons (PN) in C3-C4 in transmitting the cortical oscillatory signals to peripheral muscles. Kinematics data and surface electromyogram (EMG) of tremor in forearm were collected from PD patients. A PN network model was constructed based on known neurophysiological connections of PN. The cortical efferent signal of double tremor frequencies were integrated at the PN network, whose outputs drove the muscles of a virtual arm (VA) model to simulate tremor behaviors. The cortical efferent signal of single tremor frequency actuated muscle spindles. By comparing tremor data of PD patients and the results of model simulation, we examined two hypotheses regarding the corticospinal transmission of oscillatory signals in Parkinsonian tremor. Hypothesis I stated that the oscillatory cortical signals were transmitted via the mono-synaptic corticospinal pathways bypassing the PN network. The alternative hypothesis II stated that they were transmitted by way of PN multi-synaptic corticospinal pathway. Simulations indicated that without the PN network, the alternating burst patterns of antagonistic muscle EMGs could not be reliably generated, rejecting the first hypothesis. However, with the PN network, the alternating burst patterns of antagonist EMGs were naturally reproduced under all conditions of cortical oscillations. The results suggest that cortical commands of single and double tremor frequencies are further processed at PN to compute the alternating burst patterns in flexor and extensor muscles, and the neuromuscular dynamics

Corticomuscular Transmission of Tremor Signals by Propriospinal Neurons in Parkinson's Disease

PubMed Central

Hao, Manzhao; He, Xin; Xiao, Qin; Alstermark, Bror; Lan, Ning

2013-01-01

Cortical oscillatory signals of single and double tremor frequencies act together to cause tremor in the peripheral limbs of patients with Parkinson's disease (PD). But the corticospinal pathway that transmits the tremor signals has not been clarified, and how alternating bursts of antagonistic muscle activations are generated from the cortical oscillatory signals is not well understood. This paper investigates the plausible role of propriospinal neurons (PN) in C3–C4 in transmitting the cortical oscillatory signals to peripheral muscles. Kinematics data and surface electromyogram (EMG) of tremor in forearm were collected from PD patients. A PN network model was constructed based on known neurophysiological connections of PN. The cortical efferent signal of double tremor frequencies were integrated at the PN network, whose outputs drove the muscles of a virtual arm (VA) model to simulate tremor behaviors. The cortical efferent signal of single tremor frequency actuated muscle spindles. By comparing tremor data of PD patients and the results of model simulation, we examined two hypotheses regarding the corticospinal transmission of oscillatory signals in Parkinsonian tremor. Hypothesis I stated that the oscillatory cortical signals were transmitted via the mono-synaptic corticospinal pathways bypassing the PN network. The alternative hypothesis II stated that they were transmitted by way of PN multi-synaptic corticospinal pathway. Simulations indicated that without the PN network, the alternating burst patterns of antagonistic muscle EMGs could not be reliably generated, rejecting the first hypothesis. However, with the PN network, the alternating burst patterns of antagonist EMGs were naturally reproduced under all conditions of cortical oscillations. The results suggest that cortical commands of single and double tremor frequencies are further processed at PN to compute the alternating burst patterns in flexor and extensor muscles, and the neuromuscular dynamics

Age related neuromuscular changes in sEMG of m. Tibialis Anterior using higher order statistics (Gaussianity & linearity test).

PubMed

Siddiqi, Ariba; Arjunan, Sridhar P; Kumar, Dinesh K

2016-08-01

Age-associated changes in the surface electromyogram (sEMG) of Tibialis Anterior (TA) muscle can be attributable to neuromuscular alterations that precede strength loss. We have used our sEMG model of the Tibialis Anterior to interpret the age-related changes and compared with the experimental sEMG. Eighteen young (20-30 years) and 18 older (60-85 years) performed isometric dorsiflexion at 6 different percentage levels of maximum voluntary contractions (MVC), and their sEMG from the TA muscle was recorded. Six different age-related changes in the neuromuscular system were simulated using the sEMG model at the same MVCs as the experiment. The maximal power of the spectrum, Gaussianity and Linearity Test Statistics were computed from the simulated and experimental sEMG. A correlation analysis at α=0.05 was performed between the simulated and experimental age-related change in the sEMG features. The results show the loss in motor units was distinguished by the Gaussianity and Linearity test statistics; while the maximal power of the PSD distinguished between the muscular factors. The simulated condition of 40% loss of motor units with halved the number of fast fibers best correlated with the age-related change observed in the experimental sEMG higher order statistical features. The simulated aging condition found by this study corresponds with the moderate motor unit remodelling and negligible strength loss reported in literature for the cohorts aged 60-70 years.

A novel command signal for motor neuroprosthetic control.

PubMed

Moss, Christa W; Kilgore, Kevin L; Peckham, P Hunter

2011-01-01

Neuroprostheses can restore functions such as hand grasp or standing to individuals with spinal cord injury (SCI) using electrical stimulation to elicit movements in paralyzed muscles. Implanted neuroprostheses currently use electromyographic (EMG) activity from muscles above the lesion that remain under volitional control as a command input. Systems in development use a networked approach and will allow for restoration of multiple functions but will require additional command signals to control the system, especially in individuals with high-level tetraplegia. The objective of this study was to investigate the feasibility of using muscles innervated below the injury level as command sources for a neuroprosthesis. Recent anatomical and physiological studies have demonstrated the presence of intact axons across the lesion, even in those diagnosed with a clinically complete SCI; hence, EMG activity may be present in muscles with no sign of movement. Twelve participants with motor complete SCI were enrolled and EMG was recorded with surface electrodes from 8 muscles below the knee in each leg. Significant activity was evident in 89% of the 192 muscles studied during attempted movements of the foot and lower limb. At least 2 muscles from each participant were identified as potential command signals for a neuroprosthesis based on 2-state, threshold classification. Results suggest that voluntary activity is present and recordable in below lesion muscles even after clinically complete SCI.

An internet-based wearable watch-over system for elderly and disabled utilizing EMG and accelerometer.

PubMed

Kishimoto, M; Yoshida, T; Hayasaka, T; Mori, D; Imai, Y; Matsuki, N; Ishikawa, T; Yamaguchi, T

2009-01-01

An effective way for preventing injuries and diseases among the elderly is to monitor their daily lives. In this regard, we propose the use of a "Hyper Hospital Network", which is an information support system for elderly people and patients. In the current study, we developed a wearable system for monitoring electromyography (EMG) and acceleration using the Hyper Hospital Network plan. The current system is an upgraded version of our previous system for gait analysis (Yoshida et al. [13], Telemedicine and e-Health 13 703-714), and lets us monitor decreases in exercise and the presence of a hemiplegic gait more accurately. To clarify the capabilities and reliability of the system, we performed three experimental evaluations: one to verify the performance of the wearable system, a second to detect a hemiplegic gait, and a third to monitor EMG and accelerations simultaneously. Our system successfully detected a lack of exercise by monitoring the iEMG in healthy volunteers. Moreover, by using EMG and acceleration signals simultaneously, the reliability of the Hampering Index (HI) for detecting hemiplegia walking was improved significantly. The present study provides useful knowledge for the development of a wearable computer designed to monitor the physical conditions of older persons and patients.

Capacitively coupled EMG detection via ultra-low-power microcontroller STFT.

PubMed

Roland, Theresa; Baumgartner, Werner; Amsuess, Sebastian; Russold, Michael F

2017-07-01

As motion artefacts are a major problem with electromyography sensors, a new algorithm is developed to differentiate artefacts to contraction EMG. The performance of myoelectric prosthesis is increased with this algorithm. The implementation is done for an ultra-low-power microcontroller with limited calculation resources and memory. Short Time Fourier Transformation is used to enable real-time application. The sum of the differences (SOD) of the currently measured EMG to a reference contraction EMG is calculated. The SOD is a new parameter introduced for EMG classification. The satisfactory error rates are determined by measurements done with the capacitively coupling EMG prototype, recently developed by the research group.

Effects of seated posture on erector spinae EMG activity during whole body vibration.

PubMed

Zimmermann, C L; Cook, T M; Goel, V K

1993-06-01

The purpose of this study was to evaluate the electromyographic (EMG) response of the erector spinae to whole body vibration in three different unsupported seated postures: neutral upright, forward lean, and posterior lean. Subjects were 11 healthy college-age men. EMG was collected using bipolar surface electrodes placed bilaterally over the erector spinae at the L4 level. A modified chair with attached accelerometer was affixed to an induction type vibrator. Subjects were vibrated vertically at 4.5 Hz and 6.21 m.s-2 RMS. Data were collected in each of the three postures for 30 s pre- and post-vibration and for 2 min during vibration. Mean EMG values were determined for each sampling period and compared using ANOVA. The mean value for anterior lean was significantly larger (p < 0.05) than that for posterior lean and neutral. EMG data analysed by triggered averaging showed a phase-dependent response to the vibratory cycle for the forward leaning and neutral upright postures. The results of this study indicate that the magnitude of the vibration synchronous response of the erector spinae musculature is dependent upon body posture. This response may be an important factor in the onset of muscular fatigue and the increased incidence of back disorders among individuals exposed to whole body vibration.

Fractal and twin SVM-based handgrip recognition for healthy subjects and trans-radial amputees using myoelectric signal.

PubMed

Arjunan, Sridhar Poosapadi; Kumar, Dinesh Kant; Jayadeva J

2016-02-01

Identifying functional handgrip patterns using surface electromygram (sEMG) signal recorded from amputee residual muscle is required for controlling the myoelectric prosthetic hand. In this study, we have computed the signal fractal dimension (FD) and maximum fractal length (MFL) during different grip patterns performed by healthy and transradial amputee subjects. The FD and MFL of the sEMG, referred to as the fractal features, were classified using twin support vector machines (TSVM) to recognize the handgrips. TSVM requires fewer support vectors, is suitable for data sets with unbalanced distributions, and can simultaneously be trained for improving both sensitivity and specificity. When compared with other methods, this technique resulted in improved grip recognition accuracy, sensitivity, and specificity, and this improvement was significant (κ=0.91).

The effect of hip abduction on the EMG activity of vastus medialis obliquus, vastus lateralis longus and vastus lateralis obliquus in healthy subjects

PubMed Central

Bevilaqua-Grossi, Débora; Monteiro-Pedro, Vanessa; de Vasconcelos, Rodrigo Antunes; Arakaki, Juliano Coelho; Bérzin, Fausto

2006-01-01

Study design Controlled laboratory study. Objectives The purposes of this paper were to investigate (d) whether vastus medialis obliquus (VMO), vastus lateralis longus (VLL) and vastus lateralis obliquus (VLO) EMG activity can be influenced by hip abduction performed by healthy subjects. Background Some clinicians contraindicate hip abduction for patellofemoral patients (with) based on the premise that hip abduction could facilitate the VLL muscle activation leading to a VLL and VMO imbalance Methods and measures Twenty-one clinically healthy subjects were involved in the study, 10 women and 11 men (aged X = 23.3 ± 2.9). The EMG signals were collected using a computerized EMG VIKING II, with 8 channels and three pairs of surface electrodes. EMG activity was obtained from MVIC knee extension at 90° of flexion in a seated position and MVIC hip abduction at 0° and 30° with patients in side-lying position with the knee in full extension. The data were normalized in the MVIC knee extension at 50° of flexion in a seated position, and were submitted to ANOVA test with subsequent application of the Bonferroni multiple comparisons analysis test. The level of significance was defined as p ≤ 0.05. Results The VLO muscle demonstrated a similar pattern to the VMO muscle showing higher EMG activity in MVIC knee extension at 90° of flexion compared with MVIC hip abduction at 0° and 30° of abduction for male (p < 0.0007) and MVIC hip abduction at 0° of abduction for female subjects (p < 0.02196). There were no statistically significant differences in the VLL EMG activity among the three sets of exercises tested. Conclusion The results showed that no selective EMG activation was observed when comparison was made between the VMO, VLL and VLO muscles while performing MVIC hip abduction at 0° and 30° of abduction and MVIC knee extension at 90° of flexion in both male and female subjects. Our findings demonstrate that hip abduction do not facilitated VLL and VLO activity

Use of electromyographic and electrocardiographic signals to detect sleep bruxism episodes in a natural environment.

PubMed

Castroflorio, Tommaso; Mesin, Luca; Tartaglia, Gianluca Martino; Sforza, Chiarella; Farina, Dario

2013-11-01

Diagnosis of bruxism is difficult since not all contractions of masticatory muscles during sleeping are bruxism episodes. In this paper, we propose the use of both EMG and ECG signals for the detection of sleep bruxism. Data have been acquired from 21 healthy volunteers and 21 sleep bruxers. The masseter surface EMGs were detected with bipolar concentric electrodes and the ECG with monopolar electrodes located on the clavicular regions. Recordings were made at the subjects' homes during sleeping. Bruxism episodes were automatically detected as characterized by masseter EMG amplitude greater than 10% of the maximum and heart rate increasing by more than 25% with respect to baseline within 1 s before the increase in EMG amplitude above the 10% threshold. Furthermore, the subjects were classified as bruxers and nonbruxers by a neural network. The number of bruxism episodes per night was 24.6 ± 8.4 for bruxers and 4.3 ± 4.5 for controls ( P < 0.0001). The classification error between bruxers and nonbruxers was 1% which was substantially lower than when using EMG only for the classification. These results show that the proposed system, based on the joint analysis of EMG and ECG, can provide support for the clinical diagnosis of bruxism.

EMG parameters and EEG α Index change at fatigue period during different types of muscle contraction

NASA Astrophysics Data System (ADS)

Zhang, Li; Zhou, Bin; Song, Gaoqing

2010-10-01

The purpose of this study is to measure and analyze the characteristics in change of EMG and EEG parameters at muscle fatigue period in participants with different exercise capacity. Twenty participants took part in the tests. They were divided into two groups, Group A (constant exerciser) and Group B (seldom-exerciser). MVC dynamic and 1/3 isometric exercises were performed; EMG and EEG signals were recorded synchronously during different type of muscle contraction. Results indicated that values of MVC, RMS and IEMG in Group A were greater than Group B, but isometric exercise time was shorter than the time of dynamic exercise although its intensity was light. Turning point of IEMG and α Index occurred synchronously during constant muscle contraction of isometric or dynamic exercise. It is concluded that IEMG turning point may be an indication to justify muscle fatigue. Synchronization of EEG and EMG reflects its common characteristics on its bio-electric change.

EMG parameters and EEG α Index change at fatigue period during different types of muscle contraction

NASA Astrophysics Data System (ADS)

Zhang, Li; Zhou, Bin; Song, Gaoqing

2011-03-01

The purpose of this study is to measure and analyze the characteristics in change of EMG and EEG parameters at muscle fatigue period in participants with different exercise capacity. Twenty participants took part in the tests. They were divided into two groups, Group A (constant exerciser) and Group B (seldom-exerciser). MVC dynamic and 1/3 isometric exercises were performed; EMG and EEG signals were recorded synchronously during different type of muscle contraction. Results indicated that values of MVC, RMS and IEMG in Group A were greater than Group B, but isometric exercise time was shorter than the time of dynamic exercise although its intensity was light. Turning point of IEMG and α Index occurred synchronously during constant muscle contraction of isometric or dynamic exercise. It is concluded that IEMG turning point may be an indication to justify muscle fatigue. Synchronization of EEG and EMG reflects its common characteristics on its bio-electric change.

Inverse Modelling to Obtain Head Movement Controller Signal

NASA Technical Reports Server (NTRS)

Kim, W. S.; Lee, S. H.; Hannaford, B.; Stark, L.

1984-01-01

Experimentally obtained dynamics of time-optimal, horizontal head rotations have previously been simulated by a sixth order, nonlinear model driven by rectangular control signals. Electromyography (EMG) recordings have spects which differ in detail from the theoretical rectangular pulsed control signal. Control signals for time-optimal as well as sub-optimal horizontal head rotations were obtained by means of an inverse modelling procedures. With experimentally measured dynamical data serving as the input, this procedure inverts the model to produce the neurological control signals driving muscles and plant. The relationships between these controller signals, and EMG records should contribute to the understanding of the neurological control of movements.

Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors.

PubMed

Xi, Xugang; Tang, Minyan; Miran, Seyed M; Luo, Zhizeng

2017-05-27

As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface electromyography (sEMG), with the purpose of determining the best features and classifiers of sEMG for daily living activities monitoring and fall detection. This is done by a serial of experiments. In the experiments, four channels of sEMG signal from wireless, wearable sensors located on lower limbs are recorded from three subjects while they perform seven activities of daily living (ADL). A simulated trip fall scenario is also considered with a custom-made device attached to the ankle. With this experimental setting, 15 feature extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are analyzed based on class separability and calculation complexity, and five classification methods, each with 15 features, are estimated with respect to the accuracy rate of recognition and calculation complexity for activity monitoring and fall detection. It is shown that a high accuracy rate of recognition and a minimal calculation time for daily activity monitoring and fall detection can be achieved in the current experimental setting. Specifically, the Wilson Amplitude (WAMP) feature performs the best, and the classifier Gaussian Kernel Support Vector Machine (GK-SVM) with Permutation Entropy (PE) or WAMP results in the highest accuracy for activity monitoring with recognition rates of 97.35% and 96.43%. For fall detection, the classifier Fuzzy Min-Max Neural Network (FMMNN) has the best sensitivity and specificity at the cost of the longest calculation time, while the classifier Gaussian Kernel Fisher Linear Discriminant Analysis (GK-FDA) with the feature WAMP guarantees a high sensitivity (98.70%) and specificity (98.59%) with a short

Evaluation of Feature Extraction and Recognition for Activity Monitoring and Fall Detection Based on Wearable sEMG Sensors

PubMed Central

Xi, Xugang; Tang, Minyan; Miran, Seyed M.; Luo, Zhizeng

2017-01-01

As an essential subfield of context awareness, activity awareness, especially daily activity monitoring and fall detection, plays a significant role for elderly or frail people who need assistance in their daily activities. This study investigates the feature extraction and pattern recognition of surface electromyography (sEMG), with the purpose of determining the best features and classifiers of sEMG for daily living activities monitoring and fall detection. This is done by a serial of experiments. In the experiments, four channels of sEMG signal from wireless, wearable sensors located on lower limbs are recorded from three subjects while they perform seven activities of daily living (ADL). A simulated trip fall scenario is also considered with a custom-made device attached to the ankle. With this experimental setting, 15 feature extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are analyzed based on class separability and calculation complexity, and five classification methods, each with 15 features, are estimated with respect to the accuracy rate of recognition and calculation complexity for activity monitoring and fall detection. It is shown that a high accuracy rate of recognition and a minimal calculation time for daily activity monitoring and fall detection can be achieved in the current experimental setting. Specifically, the Wilson Amplitude (WAMP) feature performs the best, and the classifier Gaussian Kernel Support Vector Machine (GK-SVM) with Permutation Entropy (PE) or WAMP results in the highest accuracy for activity monitoring with recognition rates of 97.35% and 96.43%. For fall detection, the classifier Fuzzy Min-Max Neural Network (FMMNN) has the best sensitivity and specificity at the cost of the longest calculation time, while the classifier Gaussian Kernel Fisher Linear Discriminant Analysis (GK-FDA) with the feature WAMP guarantees a high sensitivity (98.70%) and specificity (98.59%) with a short

A Comparison of a Maximum Exertion Method and a Model-Based, Sub-Maximum Exertion Method for Normalizing Trunk EMG

PubMed Central

Cholewicki, Jacek; van Dieën, Jaap; Lee, Angela S.; Reeves, N. Peter

2011-01-01

The problem with normalizing EMG data from patients with painful symptoms (e.g. low back pain) is that such patients may be unwilling or unable to perform maximum exertions. Furthermore, the normalization to a reference signal, obtained from a maximal or sub-maximal task, tends to mask differences that might exist as a result of pathology. Therefore, we presented a novel method (GAIN method) for normalizing trunk EMG data that overcomes both problems. The GAIN method does not require maximal exertions (MVC) and tends to preserve distinct features in the muscle recruitment patterns for various tasks. Ten healthy subjects performed various isometric trunk exertions, while EMG data from 10 muscles were recorded and later normalized using the GAIN and MVC methods. The MVC method resulted in smaller variation between subjects when tasks were executed at the three relative force levels (10%, 20%, and 30% MVC), while the GAIN method resulted in smaller variation between subjects when the tasks were executed at the three absolute force levels (50 N, 100 N, and 145 N). This outcome implies that the MVC method provides a relative measure of muscle effort, while the GAIN-normalized EMG data gives an estimate of the absolute muscle force. Therefore, the GAIN-normalized EMG data tends to preserve the EMG differences between subjects in the way they recruit their muscles to execute various tasks, while the MVC-normalized data will tend to suppress such differences. The appropriate choice of the EMG normalization method will depend on the specific question that an experimenter is attempting to answer. PMID:21665489

Assessment of the paraspinal muscles of subjects presenting an idiopathic scoliosis: an EMG pilot study

PubMed Central

Gaudreault, Nathaly; Arsenault, A Bertrand; Larivière, Christian; DeSerres, Sophie J; Rivard, Charles-Hilaire

2005-01-01

Background It is known that the back muscles of scoliotic subjects present abnormalities in their fiber type composition. Some researchers have hypothesized that abnormal fiber composition can lead to paraspinal muscle dysfunction such as poor neuromuscular efficiency and muscle fatigue. EMG parameters were used to evaluate these impairments. The purpose of the present study was to examine the clinical potential of different EMG parameters such as amplitude (RMS) and median frequency (MF) of the power spectrum in order to assess the back muscles of patients presenting idiopathic scoliosis in terms of their neuromuscular efficiency and their muscular fatigue. Methods L5/S1 moments during isometric efforts in extension were measured in six subjects with idiopathic scoliosis and ten healthy controls. The subjects performed three 7 s ramp contractions ranging from 0 to 100% maximum voluntary contraction (MVC) and one 30 s sustained contraction at 75% MVC. Surface EMG activity was recorded bilaterally from the paraspinal muscles at L5, L3, L1 and T10. The slope of the EMG RMS/force (neuromuscular efficiency) and MF/force (muscle composition) relationships were computed during the ramp contractions while the slope of the EMG RMS/time and MF/time relationships (muscle fatigue) were computed during the sustained contraction. Comparisons were performed between the two groups and between the left and right sides for the EMG parameters. Results No significant group or side differences between the slopes of the different measures used were found at the level of the apex (around T10) of the major curve of the spine. However, a significant side difference was seen at a lower level (L3, p = 0.01) for the MF/time parameter. Conclusion The EMG parameters used in this study could not discriminate between the back muscles of scoliotic subjects and those of control subject regarding fiber type composition, neuromuscular efficiency and muscle fatigue at the level of the apex. The

Altered EMG patterns in diabetic neuropathic and not neuropathic patients during step ascending and descending.

PubMed

Spolaor, Fabiola; Sawacha, Zimi; Guarneri, Gabriella; Del Din, Silvia; Avogaro, Angelo; Cobelli, Claudio

2016-12-01

Diabetic peripheral neuropathy (DPN) causes motor control alterations during daily life activities. Tripping during walking or stair climbing is the predominant cause of falls in the elderly subjects with DPN and without (NoDPN). Surface Electromyography (sEMG) has been shown to be a valid tool for detecting alterations of motor functions in subjects with DPN. This study aims at investigating the presence of functional alterations in diabetic subjects during stair climbing and at exploring the relationship between altered muscle activation and temporal parameter. Lower limb muscle activities, temporal parameters and speed were evaluated in 50 subjects (10 controls, 20 with DPN, 20 without DPN), while climbing up and down a stair, using sEMG, three-dimentional motion capture and force plates. Magnitude and timing of sEMG linear envelopes peaks were extracted. Level walking was used as reference condition for the comparison with step negotiation. sEMG, speed and temporal parameters revealed significant differences among all groups of patients. Results showed an association between earlier activation of lower limb muscles and reduced speed in subjects with DPN. Speed and temporal parameters significantly correlated with sEMG (p<0.05). The findings of this study are encouraging and could be used to improve rehabilitation programs aiming at reducing falls risk in diabetic subjects. Copyright © 2016 Elsevier Ltd. All rights reserved.

Robust functional statistics applied to Probability Density Function shape screening of sEMG data.

PubMed

Boudaoud, S; Rix, H; Al Harrach, M; Marin, F

2014-01-01

Recent studies pointed out possible shape modifications of the Probability Density Function (PDF) of surface electromyographical (sEMG) data according to several contexts like fatigue and muscle force increase. Following this idea, criteria have been proposed to monitor these shape modifications mainly using High Order Statistics (HOS) parameters like skewness and kurtosis. In experimental conditions, these parameters are confronted with small sample size in the estimation process. This small sample size induces errors in the estimated HOS parameters restraining real-time and precise sEMG PDF shape monitoring. Recently, a functional formalism, the Core Shape Model (CSM), has been used to analyse shape modifications of PDF curves. In this work, taking inspiration from CSM method, robust functional statistics are proposed to emulate both skewness and kurtosis behaviors. These functional statistics combine both kernel density estimation and PDF shape distances to evaluate shape modifications even in presence of small sample size. Then, the proposed statistics are tested, using Monte Carlo simulations, on both normal and Log-normal PDFs that mimic observed sEMG PDF shape behavior during muscle contraction. According to the obtained results, the functional statistics seem to be more robust than HOS parameters to small sample size effect and more accurate in sEMG PDF shape screening applications.

Muscle fatigue evaluation of astronaut upper limb based on sEMG and subjective assessment

NASA Astrophysics Data System (ADS)

Zu, Xiaoqi; Zhou, Qianxiang; Li, Yun

2012-07-01

All movements are driven by muscle contraction, and it is easy to cause muscle fatigue. Evaluation of muscle fatigue is a hot topic in the area of astronaut life support training and rehabilitation. If muscle gets into fatigue condition, it may reduce work efficiency and has an impact on psychological performance. Therefore it is necessary to develop an accurate and usable method on muscle fatigue evaluation of astronaut upper limb. In this study, we developed a method based on surface electromyography (sEMG) and subjective assessment (Borg scale) to evaluate local muscle fatigue. Fifteen healthy young male subjects participated in the experiment. They performed isometric muscle contractions of the upper limb. sEMG of the biceps brachii were recorded during the entire process of isotonic muscle contraction and Borg scales of muscle fatigue were collected in certain times. sEMG were divided into several parts, and then mean energy of each parts were calculated by the one-twelfth band octave method. Equations were derived based on the relationship between the mean energy of sEMG and Borg scale. The results showed that cubic curve could describe the degree of local muscle fatigue, and could be used to evaluate and monitor local muscle fatigue during the entire process.

Predicting electromyographic signals under realistic conditions using a multiscale chemo-electro-mechanical finite element model.

PubMed

Mordhorst, Mylena; Heidlauf, Thomas; Röhrle, Oliver

2015-04-06

This paper presents a novel multiscale finite element-based framework for modelling electromyographic (EMG) signals. The framework combines (i) a biophysical description of the excitation-contraction coupling at the half-sarcomere level, (ii) a model of the action potential (AP) propagation along muscle fibres, (iii) a continuum-mechanical formulation of force generation and deformation of the muscle, and (iv) a model for predicting the intramuscular and surface EMG. Owing to the biophysical description of the half-sarcomere, the model inherently accounts for physiological properties of skeletal muscle. To demonstrate this, the influence of membrane fatigue on the EMG signal during sustained contractions is investigated. During a stimulation period of 500 ms at 100 Hz, the predicted EMG amplitude decreases by 40% and the AP propagation velocity decreases by 15%. Further, the model can take into account contraction-induced deformations of the muscle. This is demonstrated by simulating fixed-length contractions of an idealized geometry and a model of the human tibialis anterior muscle (TA). The model of the TA furthermore demonstrates that the proposed finite element model is capable of simulating realistic geometries, complex fibre architectures, and can include different types of heterogeneities. In addition, the TA model accounts for a distributed innervation zone, different fibre types and appeals to motor unit discharge times that are based on a biophysical description of the α motor neurons.

Predicting electromyographic signals under realistic conditions using a multiscale chemo–electro–mechanical finite element model

PubMed Central

Mordhorst, Mylena; Heidlauf, Thomas; Röhrle, Oliver

2015-01-01

This paper presents a novel multiscale finite element-based framework for modelling electromyographic (EMG) signals. The framework combines (i) a biophysical description of the excitation–contraction coupling at the half-sarcomere level, (ii) a model of the action potential (AP) propagation along muscle fibres, (iii) a continuum-mechanical formulation of force generation and deformation of the muscle, and (iv) a model for predicting the intramuscular and surface EMG. Owing to the biophysical description of the half-sarcomere, the model inherently accounts for physiological properties of skeletal muscle. To demonstrate this, the influence of membrane fatigue on the EMG signal during sustained contractions is investigated. During a stimulation period of 500 ms at 100 Hz, the predicted EMG amplitude decreases by 40% and the AP propagation velocity decreases by 15%. Further, the model can take into account contraction-induced deformations of the muscle. This is demonstrated by simulating fixed-length contractions of an idealized geometry and a model of the human tibialis anterior muscle (TA). The model of the TA furthermore demonstrates that the proposed finite element model is capable of simulating realistic geometries, complex fibre architectures, and can include different types of heterogeneities. In addition, the TA model accounts for a distributed innervation zone, different fibre types and appeals to motor unit discharge times that are based on a biophysical description of the α motor neurons. PMID:25844148

EMG synchrony to assess impaired corticomotor control of locomotion after stroke.

PubMed

Lodha, Neha; Chen, Yen-Ting; McGuirk, Theresa E; Fox, Emily J; Kautz, Steven A; Christou, Evangelos A; Clark, David J

2017-12-01

Adapting one's gait pattern requires a contribution from cortical motor commands. Evidence suggests that frequency-based analysis of electromyography (EMG) can be used to detect this cortical contribution. Specifically, increased EMG synchrony between synergistic muscles in the Piper frequency band has been linked to heightened corticomotor contribution to EMG. Stroke-related damage to cerebral motor pathways would be expected to diminish EMG Piper synchrony. The objective of this study is therefore to test the hypothesis that EMG Piper synchrony is diminished in the paretic leg relative to nonparetic and control legs, particularly during a long-step task of walking adaptability. Twenty adults with post-stroke hemiparesis and seventeen healthy controls participated in this study. EMG Piper synchrony increased more for the control legs compare to the paretic legs when taking a non-paretic long step (5.02±3.22% versus 0.86±2.62%), p<0.01) and when taking a paretic long step (2.04±1.98% versus 0.70±2.34%, p<0.05). A similar but non-significant trend was evident when comparing non-paretic and paretic legs. No statistically significant differences in EMG Piper synchrony were found between legs for typical walking. EMG Piper synchrony was positively associated with walking speed and step length within the stroke group. These findings support the assertion that EMG Piper synchrony indicates corticomotor contribution to walking. Published by Elsevier Ltd.

Effect of whole-body vibration on lower-limb EMG activity in subjects with and without spinal cord injury

PubMed Central

Alizadeh-Meghrazi, Milad; Masani, Kei; Zariffa, José; Sayenko, Dimitry G.; Popovic, Milos R.; Craven, B. Catharine

2014-01-01

Objective Traumatic spinal cord injury (SCI) results in substantial reductions in lower extremity muscle mass and bone mineral density below the level of the lesion. Whole-body vibration (WBV) has been proposed as a means of counteracting or treating musculoskeletal degradation after chronic motor complete SCI. To ascertain how WBV might be used to augment muscle and bone mass, we investigated whether WBV could evoke lower extremity electromyography (EMG) activity in able-bodied individuals and individuals with SCI, and which vibration parameters produced the largest magnitude of effect. Methods Ten male subjects participated in the study, six able-bodied and four with chronic SCI. Two different manufacturers' vibration platforms (WAVE® and Juvent™) were evaluated. The effects of vibration amplitude (0.2, 0.6 or 1.2 mm), vibration frequency (25, 35, or 45 Hz), and subject posture (knee angle of 140°, 160°, or 180°) on lower extremity EMG activation were determined (not all combinations of parameters were possible on both platforms). A novel signal processing technique was proposed to estimate the power of the EMG waveform while minimizing interference and artifacts from the plate vibration. Results WBV can elicit EMG activity among subjects with chronic SCI, if appropriate vibration parameters are employed. The amplitude of vibration had the greatest influence on EMG activation, while the frequency of vibration had lesser but statistically significant impact on the measured lower extremity EMG activity. Conclusion These findings suggest that WBV with appropriate parameters may constitute a promising intervention to treat musculoskeletal degradation after chronic SCI. PMID:24986541

Intramuscular pressure: A better tool than EMG to optimize exercise for long-duration space flight

NASA Technical Reports Server (NTRS)

Hargens, A. R.; Ballard, R. E.; Aratow, M.; Crenshaw, A.; Styf, J.; Kahan, N.; Watenpaugh, D. E.

1992-01-01

A serious problem experienced by astronauts during long-duration space flight is muscle atrophy. In order to develop countermeasures for this problem, a simple method for monitoring in vivo function of specific muscles is needed. Previous studies document that both intramuscular pressure (IMP) and electromyography (EMG) provide quantitative indices of muscle contraction force during isometric exercise. However, at present there are no data available concerning the usefulness of IMP versus EMG during dynamic exercise. Methods: IMP (Myopress catheter) and surface EMG activity were measured continuously and simultaneously in the tibalis anterior (TA) and soleus (SOL) muscles of 9 normal male volunteers (28-54 years). These parameters were recorded during both concentric and eccentric exercises which consisted of plantarflexon and dorsiflexon of the ankle joint. A Lido Active Isokinetic Dynamometer concurrently recorded ankle joint torque and position. Results: Intramuscular pressure correlated linearly with contraction force for both SOL (r exp 2 = 0.037) and TA (R exp 2 = 0.716 and r exp 2 = 0.802, respectively). During eccentric exercises, SOL and TA IMP also correlated linearly with contraction force (r(exp 2) = 0.883 and r(exp 2) = 0.904 respectively), but SOL and TA EMG correlated poorly with force (r(exp 2) = 0.489 and r(exp 2) = 0.702 respectively). Conclusion: IMP measurement provides a better index of muscle contraction force than EMG during concentric and eccentric exercise. IMP reflects intrinsic mechanical properties of individual muscles, such as length tension relationships. Although invasive, IMP provides a more powerful tool and EMG for developing exercise hardware and protocols for astronauts exposed to long-duration space flight.

A Novel Hybrid Model for Drawing Trace Reconstruction from Multichannel Surface Electromyographic Activity.

PubMed

Chen, Yumiao; Yang, Zhongliang

2017-01-01

Recently, several researchers have considered the problem of reconstruction of handwriting and other meaningful arm and hand movements from surface electromyography (sEMG). Although much progress has been made, several practical limitations may still affect the clinical applicability of sEMG-based techniques. In this paper, a novel three-step hybrid model of coordinate state transition, sEMG feature extraction and gene expression programming (GEP) prediction is proposed for reconstructing drawing traces of 12 basic one-stroke shapes from multichannel surface electromyography. Using a specially designed coordinate data acquisition system, we recorded the coordinate data of drawing traces collected in accordance with the time series while 7-channel EMG signals were recorded. As a widely-used time domain feature, Root Mean Square (RMS) was extracted with the analysis window. The preliminary reconstruction models can be established by GEP. Then, the original drawing traces can be approximated by a constructed prediction model. Applying the three-step hybrid model, we were able to convert seven channels of EMG activity recorded from the arm muscles into smooth reconstructions of drawing traces. The hybrid model can yield a mean accuracy of 74% in within-group design (one set of prediction models for all shapes) and 86% in between-group design (one separate set of prediction models for each shape), averaged for the reconstructed x and y coordinates. It can be concluded that it is feasible for the proposed three-step hybrid model to improve the reconstruction ability of drawing traces from sEMG.

Detecting Nasal Vowels in Speech Interfaces Based on Surface Electromyography

PubMed Central

Freitas, João; Teixeira, António; Silva, Samuel; Oliveira, Catarina; Dias, Miguel Sales

2015-01-01

Nasality is a very important characteristic of several languages, European Portuguese being one of them. This paper addresses the challenge of nasality detection in surface electromyography (EMG) based speech interfaces. We explore the existence of useful information about the velum movement and also assess if muscles deeper down in the face and neck region can be measured using surface electrodes, and the best electrode location to do so. The procedure we adopted uses Real-Time Magnetic Resonance Imaging (RT-MRI), collected from a set of speakers, providing a method to interpret EMG data. By ensuring compatible data recording conditions, and proper time alignment between the EMG and the RT-MRI data, we are able to accurately estimate the time when the velum moves and the type of movement when a nasal vowel occurs. The combination of these two sources revealed interesting and distinct characteristics in the EMG signal when a nasal vowel is uttered, which motivated a classification experiment. Overall results of this experiment provide evidence that it is possible to detect velum movement using sensors positioned below the ear, between mastoid process and the mandible, in the upper neck region. In a frame-based classification scenario, error rates as low as 32.5% for all speakers and 23.4% for the best speaker have been achieved, for nasal vowel detection. This outcome stands as an encouraging result, fostering the grounds for deeper exploration of the proposed approach as a promising route to the development of an EMG-based speech interface for languages with strong nasal characteristics. PMID:26069968

A patient-specific EMG-driven neuromuscular model for the potential use of human-inspired gait rehabilitation robots.

PubMed

Ma, Ye; Xie, Shengquan; Zhang, Yanxin

2016-03-01

A patient-specific electromyography (EMG)-driven neuromuscular model (PENm) is developed for the potential use of human-inspired gait rehabilitation robots. The PENm is modified based on the current EMG-driven models by decreasing the calculation time and ensuring good prediction accuracy. To ensure the calculation efficiency, the PENm is simplified into two EMG channels around one joint with minimal physiological parameters. In addition, a dynamic computation model is developed to achieve real-time calculation. To ensure the calculation accuracy, patient-specific muscle kinematics information, such as the musculotendon lengths and the muscle moment arms during the entire gait cycle, are employed based on the patient-specific musculoskeletal model. Moreover, an improved force-length-velocity relationship is implemented to generate accurate muscle forces. Gait analysis data including kinematics, ground reaction forces, and raw EMG signals from six adolescents at three different speeds were used to evaluate the PENm. The simulation results show that the PENm has the potential to predict accurate joint moment in real-time. The design of advanced human-robot interaction control strategies and human-inspired gait rehabilitation robots can benefit from the application of the human internal state provided by the PENm. Copyright © 2016 Elsevier Ltd. All rights reserved.

Influence of Inter-Electrode Distance on EMG

DTIC Science & Technology

2001-10-25

has been observed that at low levels of muscle contraction there was no significant variation due to the change in the distance between the...a variation of the spectral content of the EMG with change in the IED. The study also has shown that there is a variation of the EMG with muscle ... contraction but that the comparison should be done if the distance between the electrodes has been kept constant.

The Effect of Involuntary Motor Activity on Myoelectric Pattern Recognition: A Case Study with Chronic Stroke Patients

PubMed Central

Zhang, Xu; Li, Yun; Chen, Xiang; Li, Guanglin; Rymer, William Zev; Zhou, Ping

2013-01-01

This study investigates the effect of involuntary motor activity of paretic-spastic muscles on classification of surface electromyography (EMG) signals. Two data collection sessions were designed for 8 stroke subjects to voluntarily perform 11 functional movements using their affected forearm and hand at a relatively slow and fast speed. For each stroke subject, the degree of involuntary motor activity present in voluntary surface EMG recordings was qualitatively described from such slow and fast experimental protocols. Myoelectric pattern recognition analysis was performed using different combinations of voluntary surface EMG data recorded from slow and fast sessions. Across all tested stroke subjects, our results revealed that when involuntary surface EMG was absent or present in both training and testing datasets, high accuracies (> 96%, > 98%, respectively, averaged over all the subjects) can be achieved in classification of different movements using surface EMG signals from paretic muscles. When involuntary surface EMG was solely involved in either training or testing datasets, the classification accuracies were dramatically reduced (< 89%, < 85%, respectively). However, if both training and testing datasets contained EMG signals with presence and absence of involuntary EMG interference, high accuracies were still achieved (> 97%). The findings of this study can be used to guide appropriate design and implementation of myoelectric pattern recognition based systems or devices toward promoting robot-aided therapy for stroke rehabilitation. PMID:23860192

A Spiking Neural Network in sEMG Feature Extraction.

PubMed

Lobov, Sergey; Mironov, Vasiliy; Kastalskiy, Innokentiy; Kazantsev, Victor

2015-11-03

We have developed a novel algorithm for sEMG feature extraction and classification. It is based on a hybrid network composed of spiking and artificial neurons. The spiking neuron layer with mutual inhibition was assigned as feature extractor. We demonstrate that the classification accuracy of the proposed model could reach high values comparable with existing sEMG interface systems. Moreover, the algorithm sensibility for different sEMG collecting systems characteristics was estimated. Results showed rather equal accuracy, despite a significant sampling rate difference. The proposed algorithm was successfully tested for mobile robot control.

Spatial distribution of surface action potentials generated by individual motor units in the human biceps brachii muscle.

PubMed

Rodriguez-Falces, Javier; Negro, Francesco; Gonzalez-Izal, Miriam; Farina, Dario

2013-08-01

This study analyses the spatial distribution of individual motor unit potentials (MUPs) over the skin surface and the influence of motor unit depth and recording configuration on this distribution. Multichannel surface (13×5 electrode grid) and intramuscular (wire electrodes inserted with needles of lengths 15 and 25mm) electromyographic (EMG) signals were concurrently recorded with monopolar derivations from the biceps brachii muscle of 10 healthy subjects during 60-s isometric contractions at 20% of the maximum torque. Multichannel monopolar MUPs of the target motor unit were obtained by spike-triggered averaging of the surface EMG. Amplitude and frequency characteristics of monopolar and bipolar MUPs were calculated for locations along the fibers' direction (longitudinal), and along the direction perpendicular (transverse) to the fibers. In the longitudinal direction, monopolar and bipolar MUPs exhibited marked amplitude changes that extended for 16-32mm and 16-24mm over the innervation and tendon zones, respectively. The variation of monopolar and bipolar MUP characteristics was not symmetrical about the innervation zone. Motor unit depth had a considerable influence on the relative longitudinal variation of amplitude for monopolar MUPs, but not for bipolar MUPs. The transverse extension of bipolar MUPs ranged between 24 and 32mm, whereas that of monopolar MUPs ranged between 72 and 96mm. The mean power spectral frequency of surface MUPs was highly dependent on the transverse electrode location but not on depth. This study provides a basis for the interpretation of the contribution of individual motor units to the interference surface EMG signal. Copyright © 2013 Elsevier Ltd. All rights reserved.

The influence of digital filter type, amplitude normalisation method, and co-contraction algorithm on clinically relevant surface electromyography data during clinical movement assessments.

PubMed

Devaprakash, Daniel; Weir, Gillian J; Dunne, James J; Alderson, Jacqueline A; Donnelly, Cyril J

2016-12-01

There is a large and growing body of surface electromyography (sEMG) research using laboratory-specific signal processing procedures (i.e., digital filter type and amplitude normalisation protocols) and data analyses methods (i.e., co-contraction algorithms) to acquire practically meaningful information from these data. As a result, the ability to compare sEMG results between studies is, and continues to be challenging. The aim of this study was to determine if digital filter type, amplitude normalisation method, and co-contraction algorithm could influence the practical or clinical interpretation of processed sEMG data. Sixteen elite female athletes were recruited. During data collection, sEMG data was recorded from nine lower limb muscles while completing a series of calibration and clinical movement assessment trials (running and sidestepping). Three analyses were conducted: (1) signal processing with two different digital filter types (Butterworth or critically damped), (2) three amplitude normalisation methods, and (3) three co-contraction ratio algorithms. Results showed the choice of digital filter did not influence the clinical interpretation of sEMG; however, choice of amplitude normalisation method and co-contraction algorithm did influence the clinical interpretation of the running and sidestepping task. Care is recommended when choosing amplitude normalisation method and co-contraction algorithms if researchers/clinicians are interested in comparing sEMG data between studies. Copyright © 2016 Elsevier Ltd. All rights reserved.

EMG changes in thigh and calf muscles in fin swimming exercise.

PubMed

Jammes, Y; Delliaux, S; Coulange, M; Jammes, C; Kipson, N; Brerro-Saby, C; Bregeon, F

2010-08-01

Because previous researchers have reported a reduced lactic acid production that accompanies a delayed or an absent ventilatory threshold (VTh) in water-based exercise, we hypothesized that the metaboreflex, activated by muscle acidosis, might be absent in fin swimming. This motor response, delaying the occurrence of fatigue, is characterized by a decreased median frequency (MF) of electromyographic (EMG) power spectrum. Seven healthy subjects performed a maximal fin swimming exercise protocol with simultaneous recordings of surface EMGs in VASTUS MEDIALIS (VM), TIBIALIS ANTERIOR (TA) and GASTROCNEMIUS MEDIALIS (GM). We computed the root mean square (RMS) and MF and recorded the compound evoked muscle potential (M-wave) in VM. We also measured the propulsive force and oxygen uptake (VO (2)), and determined VTh. VTh was absent in 4/7 subjects and measured at 70-90% of VO (2max) in the other three. In the three studied muscles, the global EMG activity (RMS) increased while the MF decreased in proportion of VO (2), the MF changes being significantly higher in VM (-29%) and GM (-39%) than in TA (-19%). Because no M-wave changes were noted, the MF decline was attributed to the recruitment of low-frequency, fatigue-resistant motor units. Our most important finding is the persistence of the metaboreflex even in a situation of reduced muscle acidosis. (c) Georg Thieme Verlag KG Stuttgart . New York.

Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration.

PubMed

Siu, Ho Chit; Arenas, Ana M; Sun, Tingxiao; Stirling, Leia A

2018-02-05

Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG), but these systems are usually reactionary, and/or rely on entirely hand-tuned parameters. sEMG-based systems may be able to provide anticipatory control, since they interface directly with muscle signals, but typically require expert placement of sensors on muscle bodies. We present an implementation of an adaptive sEMG-based exoskeleton controller that learns a mapping between muscle activation and the desired system state during interaction with a user, generating a personalized sEMG feature classifier to allow for anticipatory control. This system is robust to novice placement of sEMG sensors, as well as subdermal muscle shifts. We validate this method with 18 subjects using a thumb exoskeleton to complete a book-placement task. This learning-from-demonstration system for exoskeleton control allows for very short training times, as well as the potential for improvement in intent recognition over time, and adaptation to physiological changes in the user, such as those due to fatigue.

Implementation of a Surface Electromyography-Based Upper Extremity Exoskeleton Controller Using Learning from Demonstration

PubMed Central

Arenas, Ana M.; Sun, Tingxiao

2018-01-01

Upper-extremity exoskeletons have demonstrated potential as augmentative, assistive, and rehabilitative devices. Typical control of upper-extremity exoskeletons have relied on switches, force/torque sensors, and surface electromyography (sEMG), but these systems are usually reactionary, and/or rely on entirely hand-tuned parameters. sEMG-based systems may be able to provide anticipatory control, since they interface directly with muscle signals, but typically require expert placement of sensors on muscle bodies. We present an implementation of an adaptive sEMG-based exoskeleton controller that learns a mapping between muscle activation and the desired system state during interaction with a user, generating a personalized sEMG feature classifier to allow for anticipatory control. This system is robust to novice placement of sEMG sensors, as well as subdermal muscle shifts. We validate this method with 18 subjects using a thumb exoskeleton to complete a book-placement task. This learning-from-demonstration system for exoskeleton control allows for very short training times, as well as the potential for improvement in intent recognition over time, and adaptation to physiological changes in the user, such as those due to fatigue. PMID:29401754

Further observations on the relationship of EMG and muscle force

NASA Technical Reports Server (NTRS)

Agarwal, G. C.; Cecchini, L. R.; Gottlieb, G. L.

1972-01-01

Human skeletal muscle may be regarded as an electro-mechanical transducer. Its physiological input is a neural signal originating at the alpha motoneurons in the spinal cord and its output is force and muscle contraction, these both being dependent on the external load. Some experimental data taken during voluntary efforts around the ankle joint and by direct electrical stimulation of the nerve are described. Some of these experiments are simulated by an analog model, the input of which is recorded physiological soleus muscle EMG. The output is simulated foot torque. Limitations of a linear model and effect of some nonlinearities are discussed.

Surface electromyogram for the control of anthropomorphic teleoperator fingers.

PubMed

Gupta, V; Reddy, N P

1996-01-01

Growing importance of telesurgery has led to the need for the development of synergistic control of anthropomorphic teleoperators. Synergistic systems can be developed using direct biological control. The purpose of this study was to develop techniques for direct biocontrol of anthropomorphic teleoperators using surface electromyogram (EMG). A computer model of a two finger teleoperator was developed and controlled using surface EMG from the flexor digitorum superficialis during flexion-extension of the index finger. The results of the study revealed a linear relationship between the RMS EMG and the flexion-extension of the finger model. Therefore, surface EMG can be used as a direct biocontrol for teleoperators and in VR applications.

Masticatory Muscle Sleep Background EMG Activity is Elevated in Myofascial TMD Patients

PubMed Central

Raphael, Karen G.; Janal, Malvin N.; Sirois, David A.; Dubrovsky, Boris; Wigren, Pia E.; Klausner, Jack J.; Krieger, Ana C.; Lavigne, Gilles J.

2013-01-01

Despite theoretical speculation and strong clinical belief, recent research using laboratory polysomnographic (PSG) recording has provided new evidence that frequency of sleep bruxism (SB) masseter muscle events, including grinding or clenching of the teeth during sleep, is not increased for women with chronic myofascial temporomandibular disorder (TMD). The current case-control study compares a large sample of women suffering from chronic myofascial TMD (n=124) with a demographically matched control group without TMD (n=46) on sleep background electromyography (EMG) during a laboratory PSG study. Background EMG activity was measured as EMG root mean square (RMS) from the right masseter muscle after lights out. Sleep background EMG activity was defined as EMG RMS remaining after activity attributable to SB, other orofacial activity, other oromotor activity and movement artifacts were removed. Results indicated that median background EMG during these non SB-event periods was significantly higher (p<.01) for women with myofascial TMD (median=3.31 μV and mean=4.98 μV) than for control women (median=2.83 μV and mean=3.88 μV) with median activity in 72% of cases exceeding control activity. Moreover, for TMD cases, background EMG was positively associated and SB event-related EMG was negatively associated with pain intensity ratings (0–10 numerical scale) on post sleep waking. These data provide the foundation for a new focus on small, but persistent, elevations in sleep EMG activity over the course of the night as a mechanism of pain induction or maintenance. PMID:24237356

Efficacy of EMG-triggered electrical arm stimulation in chronic hemiparetic stroke patients.

PubMed

von Lewinski, Friederike; Hofer, Sabine; Kaus, Jürgen; Merboldt, Klaus-Dietmar; Rothkegel, Holger; Schweizer, Renate; Liebetanz, David; Frahm, Jens; Paulus, Walter

2009-01-01

EMG-triggered electrostimulation (EMG-ES) may improve the motor performance of affected limbs of hemiparetic stroke patients even in the chronic stage. This study was designed to characterize cortical activation changes following intensified EMG-ES in chronic stroke patients and to identify predictors for successful rehabilitation depending on disease severity. We studied 9 patients with severe residual hemiparesis, who underwent 8 weeks of daily task-orientated multi-channel EMG-ES of the paretic arm. Before and after treatment, arm function was evaluated clinically and cortical activation patterns were assessed with functional MRI (fMRI) and/or transcranial magnetic stimulation (TMS). As response to therapy, arm function improved in a subset of patients with more capacity in less affected subjects, but there was no significant gain for those with Box & Block test values below 4 at inception. The clinical improvement, if any, was accompanied by an ipsilesional increase in the sensorimotor cortex (SMC) activation area in fMRI and enhanced intracortical facilitation (ICF) as revealed by paired TMS. The SMC activation change in fMRI was predicted by the presence or absence of motor-evoked potentials (MEPs) on the affected side. The present findings support the notion that intensified EMG-ES may improve the arm function in individual chronic hemiparetic stroke patients but not in more severely impaired individuals. Functional improvements are paralleled by increased ipsilesional SMC activation and enhanced ICF supporting neuroplasticity as contributor to rehabilitation. The clinical score at inception and the presence of MEPs have the best predictive potential.

A three-dimensional muscle activity imaging technique for assessing pelvic muscle function

NASA Astrophysics Data System (ADS)

Zhang, Yingchun; Wang, Dan; Timm, Gerald W.

2010-11-01

A novel multi-channel surface electromyography (EMG)-based three-dimensional muscle activity imaging (MAI) technique has been developed by combining the bioelectrical source reconstruction approach and subject-specific finite element modeling approach. Internal muscle activities are modeled by a current density distribution and estimated from the intra-vaginal surface EMG signals with the aid of a weighted minimum norm estimation algorithm. The MAI technique was employed to minimally invasively reconstruct electrical activity in the pelvic floor muscles and urethral sphincter from multi-channel intra-vaginal surface EMG recordings. A series of computer simulations were conducted to evaluate the performance of the present MAI technique. With appropriate numerical modeling and inverse estimation techniques, we have demonstrated the capability of the MAI technique to accurately reconstruct internal muscle activities from surface EMG recordings. This MAI technique combined with traditional EMG signal analysis techniques is being used to study etiologic factors associated with stress urinary incontinence in women by correlating functional status of muscles characterized from the intra-vaginal surface EMG measurements with the specific pelvic muscle groups that generated these signals. The developed MAI technique described herein holds promise for eliminating the need to place needle electrodes into muscles to obtain accurate EMG recordings in some clinical applications.

Comparison between sEMG and force as control interfaces to support planar arm movements in adults with Duchenne: a feasibility study.

PubMed

Lobo-Prat, Joan; Nizamis, Kostas; Janssen, Mariska M H P; Keemink, Arvid Q L; Veltink, Peter H; Koopman, Bart F J M; Stienen, Arno H A

2017-07-12

Adults with Duchenne muscular dystrophy (DMD) can benefit from devices that actively support their arm function. A critical component of such devices is the control interface as it is responsible for the human-machine interaction. Our previous work indicated that surface electromyography (sEMG) and force-based control with active gravity and joint-stiffness compensation were feasible solutions for the support of elbow movements (one degree of freedom). In this paper, we extend the evaluation of sEMG- and force-based control interfaces to simultaneous and proportional control of planar arm movements (two degrees of freedom). Three men with DMD (18-23 years-old) with different levels of arm function (i.e. Brooke scores of 4, 5 and 6) performed a series of line-tracing tasks over a tabletop surface using an experimental active arm support. The arm movements were controlled using three control methods: sEMG-based control, force-based control with stiffness compensation (FSC), and force-based control with no compensation (FNC). The movement performance was evaluated in terms of percentage of task completion, tracing error, smoothness and speed. For subject S1 (Brooke 4) FNC was the preferred method and performed better than FSC and sEMG. FNC was not usable for subject S2 (Brooke 5) and S3 (Brooke 6). Subject S2 presented significantly lower movement speed with sEMG than with FSC, yet he preferred sEMG since FSC was perceived to be too fatiguing. Subject S3 could not successfully use neither of the two force-based control methods, while with sEMG he could reach almost his entire workspace. Movement performance and subjective preference of the three control methods differed with the level of arm function of the participants. Our results indicate that all three control methods have to be considered in real applications, as they present complementary advantages and disadvantages. The fact that the two weaker subjects (S2 and S3) experienced the force-based control

Open-Box Muscle-Computer Interface: Introduction to Human-Computer Interactions in Bioengineering, Physiology, and Neuroscience Courses

ERIC Educational Resources Information Center

Landa-Jiménez, M. A.; González-Gaspar, P.; Pérez-Estudillo, C.; López-Meraz, M. L.; Morgado-Valle, C.; Beltran-Parrazal, L.

2016-01-01

A Muscle-Computer Interface (muCI) is a human-machine system that uses electromyographic (EMG) signals to communicate with a computer. Surface EMG (sEMG) signals are currently used to command robotic devices, such as robotic arms and hands, and mobile robots, such as wheelchairs. These signals reflect the motor intention of a user before the…

The effect of involuntary motor activity on myoelectric pattern recognition: a case study with chronic stroke patients

NASA Astrophysics Data System (ADS)

Zhang, Xu; Li, Yun; Chen, Xiang; Li, Guanglin; Zev Rymer, William; Zhou, Ping

2013-08-01

Objective. This study investigates the effect of the involuntary motor activity of paretic-spastic muscles on the classification of surface electromyography (EMG) signals. Approach. Two data collection sessions were designed for 8 stroke subjects to voluntarily perform 11 functional movements using their affected forearm and hand at relatively slow and fast speeds. For each stroke subject, the degree of involuntary motor activity present in the voluntary surface EMG recordings was qualitatively described from such slow and fast experimental protocols. Myoelectric pattern recognition analysis was performed using different combinations of voluntary surface EMG data recorded from the slow and fast sessions. Main results. Across all tested stroke subjects, our results revealed that when involuntary surface EMG is absent or present in both the training and testing datasets, high accuracies (>96%, >98%, respectively, averaged over all the subjects) can be achieved in the classification of different movements using surface EMG signals from paretic muscles. When involuntary surface EMG was solely involved in either the training or testing datasets, the classification accuracies were dramatically reduced (<89%, <85%, respectively). However, if both the training and testing datasets contained EMG signals with the presence and absence of involuntary EMG interference, high accuracies were still achieved (>97%). Significance. The findings of this study can be used to guide the appropriate design and implementation of myoelectric pattern recognition based systems or devices toward promoting robot-aided therapy for stroke rehabilitation.

Noninvasive measurement of physiological signals on a modified home bathroom scale.

PubMed

Inan, O T; Dookun Park; Giovangrandi, L; Kovacs, G T A

2012-08-01

A commercial bathroom scale with both handlebar and footpad electrodes was modified to enable measurement of four physiological signals: the ballistocardiogram (BCG), electrocardiogram (ECG), lower body impedance plethysmogram (IPG), and lower body electromyogram (EMG). The BCG, which describes the reaction of the body to cardiac ejection of blood, was measured using the strain gauges in the scale. The ECG was detected using handlebar electrodes with a two-electrode amplifier. For the lower body IPG, the two electrodes under the subject's toes were driven with an ac current stimulus, and the resulting differential voltage across the heels was measured and demodulated synchronously with the source. The voltage signal from the same two footpad electrodes under the heels was passed through a passive low-pass filter network into another amplifier, and the output was the lower body EMG signal. The signals were measured from nine healthy subjects, and the average signal-to-noise ratio (SNR) while the subjects were standing still was estimated for the four signals as follows: BCG, 7.6 dB; ECG, 15.8 dB; IPG, 10.7 dB. During periods of motion, the decrease in SNR for the BCG signal was found to be correlated to the increase in rms power for the lower body EMG (r = 0.89, p <; 0.01). The EMG could, thus, be used to flag noise-corrupted segments of the BCG, increasing the measurement robustness. This setup could be used for monitoring the cardiovascular health of patients at home.

Fabrication of Micro-Needle Electrodes for Bio-Signal Recording by a Magnetization-Induced Self-Assembly Method

PubMed Central

Chen, Keyun; Ren, Lei; Chen, Zhipeng; Pan, Chengfeng; Zhou, Wei; Jiang, Lelun

2016-01-01

Micro-needle electrodes (MEs) have attracted more and more attention for monitoring physiological electrical signals, including electrode-skin interface impedance (EII), electromyography (EMG) and electrocardiography (ECG) recording. A magnetization-induced self-assembling method (MSM) was developed to fabricate a microneedle array (MA). A MA coated with Ti/Au film was assembled as a ME. The fracture and insertion properties of ME were tested by experiments. The bio-signal recording performance of the ME was measured and compared with a typical commercial wet electrode (Ag/AgCl electrode). The results show that the MA self-assembled from the magnetic droplet array under the sum of gravitational surface tension and magnetic potential energies. The ME had good toughness and could easily pierce rabbit skin without being broken or buckling. When the compression force applied on the ME was larger than 2 N, ME could stably record EII, which was a lower value than that measured by Ag/AgCl electrodes. EMG signals collected by ME varied along with the contraction of biceps brachii muscle. ME could record static ECG signals with a larger amplitude and dynamic ECG signals with more distinguishable features in comparison with a Ag/AgCl electrode, therefore, ME is an alternative electrode for bio-signal monitoring in some specific situations. PMID:27657072

Effect of hypnosis on masseter EMG recorded during the 'resting' and a slightly open jaw posture.

PubMed

Al-Enaizan, N; Davey, K J; Lyons, M F; Cadden, S W

2015-11-01

The aim of this experimental study was to determine whether minimal levels of electromyographic activity in the masseter muscle are altered when individuals are in a verified hypnotic state. Experiments were performed on 17 volunteer subjects (8 male, 9 female) all of whom gave informed consent. The subjects were dentate and had no symptoms of pain or masticatory dysfunction. Surface electromyograms (EMGs) were made from the masseter muscles and quantified by integration following full-wave rectification and averaging. The EMGs were obtained (i) with the mandible in 'resting' posture; (ii) with the mandible voluntarily lowered (but with the lips closed); (iii) during maximum voluntary clenching (MVC). The first two recordings were made before, during and after the subjects were in a hypnotic state. Susceptibility to hypnosis was assessed with Spiegel's eye-roll test, and the existence of the hypnotic state was verified by changes in ventilatory pattern. On average, EMG levels expressed as percentages of MVC were less: (i) when the jaw was deliberately lowered as opposed to being in the postural position: (ii) during hypnosis compared with during the pre- and post-hypnotic periods. However, analysis of variance followed by post hoc tests with multiple comparison corrections (Bonferroni) revealed that only the differences between the level during hypnosis and those before and after hypnosis were statistically significant (P < 0·05). As the level of masseter EMG when the mandible was in 'resting' posture was reduced by hypnosis, it appears that part of that EMG is of biological origin. © 2015 John Wiley & Sons Ltd.

Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms.

PubMed

Karthick, P A; Ghosh, Diptasree Maitra; Ramakrishnan, S