Science.gov

Sample records for surface emg signal

  1. Analysis of surface EMG signal morphology in Parkinson's disease.

    PubMed

    Rissanen, Saara; Kankaanpää, Markku; Tarvainen, Mika P; Nuutinen, Juho; Tarkka, Ina M; Airaksinen, Olavi; Karjalainen, Pasi A

    2007-12-01

    A novel approach is presented for the analysis of surface electromyogram (EMG) morphology in Parkinson's disease (PD). The method is based on histogram and crossing rate (CR) analysis of the EMG signal. In the method, histograms and CR values are used as high-dimensional feature vectors. The dimensionality of them is then reduced using the Karhunen-Loève transform (KLT). Finally, the discriminant analysis of feature vectors is performed in low-dimensional eigenspace. Histograms and CR values were chosen for analysis, because Parkinsonian EMG signals typically involve patterns of EMG bursts. Traditional methods of EMG amplitude and spectral analysis are not effective in analyzing impulse-like signals. The method, which was tested with EMG signals measured from 25 patients with PD and 22 healthy controls, was promising for discriminating between these two groups of subjects. The ratio of correct discrimination by augmented KLT was 86% for the control group and 72% for the patient group. On the basis of these results, further studies are suggested in order to evaluate the usability of this method in early stage diagnostics of PD. PMID:18057515

  2. Characterization of surface EMG signals using improved approximate entropy*

    PubMed Central

    Chen, Wei-ting; Wang, Zhi-zhong; Ren, Xiao-mei

    2006-01-01

    An improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often confronted with the problem of insufficient data points and noisy circumstances, which led to unsatisfactory results. Compared with fractal dimension as well as the standard ApEn, the improved ApEn can extract information underlying sEMG signals more efficiently and accurately. The method introduced here can also be applied to other medium-sized and noisy physiological signals. PMID:16972328

  3. ECG Artifact Removal from Surface EMG Signal Using an Automated Method Based on Wavelet-ICA.

    PubMed

    Abbaspour, Sara; Lindén, Maria; Gholamhosseini, Hamid

    2015-01-01

    This study aims at proposing an efficient method for automated electrocardiography (ECG) artifact removal from surface electromyography (EMG) signals recorded from upper trunk muscles. Wavelet transform is applied to the simulated data set of corrupted surface EMG signals to create multidimensional signal. Afterward, independent component analysis (ICA) is used to separate ECG artifact components from the original EMG signal. Components that correspond to the ECG artifact are then identified by an automated detection algorithm and are subsequently removed using a conventional high pass filter. Finally, the results of the proposed method are compared with wavelet transform, ICA, adaptive filter and empirical mode decomposition-ICA methods. The automated artifact removal method proposed in this study successfully removes the ECG artifacts from EMG signals with a signal to noise ratio value of 9.38 while keeping the distortion of original EMG to a minimum. PMID:25980853

  4. A Novel Technique for Muscle Onset Detection Using Surface EMG Signals without Removal of ECG Artifacts

    PubMed Central

    Zhou, Ping; Zhang, Xu

    2014-01-01

    Surface electromyogram (EMG) signal from trunk muscles is often contaminated by electrocardiogram (ECG) artifacts. This study presents a novel method for muscle activity onset detection by processing surface EMG against ECG artifacts. The method does not require removal of ECG artifacts from raw surface EMG signals. Instead, it applies the sample entropy (SampEn) analysis to highlight EMG activity and suppress ECG artifacts in the signal complexity domain. A SampEn threshold can then be determined for detection of muscle activity. The performance of the proposed method was examined with different SampEn analysis window lengths, using a series of combinations of “clean” experimental EMG and ECG recordings over a wide range of signal to noise ratios (SNRs) from −10 dB to 10 dB. For all the examined SNRs, the window length of 128 ms yielded the best performance among all the tested lengths. Compared with the conventional amplitude thresholding and integrated profile methods, the SampEn analysis based method achieved significantly better performance, demonstrated as the shortest average latency or error among the three methods (p<0.001 for any of the examined SNRs except 10 dB). PMID:24345857

  5. Discrimination of Combined Motions for Prosthetic Hands Using Surface EMG Signals

    NASA Astrophysics Data System (ADS)

    Ibe, Ayuko; Gouko, Manabu; Ito, Koji

    The present paper proposes a multiple step discrimination method to determine single and combined movements intended by an amputee from surface electromyogram (EMG) signals. Most previous approaches to the discrimination of movement using EMG signals have been restricted to single joint movements. Our approach enables the amputee's intended movement to be determined from among four single and two combined limb functions using an initial rise zone 125 msec long. Experiments with ten subjects and four electrodes demonstrated that our proposal determines six forearm movements at a discrimination rate exceeding than 90%.

  6. Real-time intelligent pattern recognition algorithm for surface EMG signals

    PubMed Central

    Khezri, Mahdi; Jahed, Mehran

    2007-01-01

    Background Electromyography (EMG) is the study of muscle function through the inquiry of electrical signals that the muscles emanate. EMG signals collected from the surface of the skin (Surface Electromyogram: sEMG) can be used in different applications such as recognizing musculoskeletal neural based patterns intercepted for hand prosthesis movements. Current systems designed for controlling the prosthetic hands either have limited functions or can only be used to perform simple movements or use excessive amount of electrodes in order to achieve acceptable results. In an attempt to overcome these problems we have proposed an intelligent system to recognize hand movements and have provided a user assessment routine to evaluate the correctness of executed movements. Methods We propose to use an intelligent approach based on adaptive neuro-fuzzy inference system (ANFIS) integrated with a real-time learning scheme to identify hand motion commands. For this purpose and to consider the effect of user evaluation on recognizing hand movements, vision feedback is applied to increase the capability of our system. By using this scheme the user may assess the correctness of the performed hand movement. In this work a hybrid method for training fuzzy system, consisting of back-propagation (BP) and least mean square (LMS) is utilized. Also in order to optimize the number of fuzzy rules, a subtractive clustering algorithm has been developed. To design an effective system, we consider a conventional scheme of EMG pattern recognition system. To design this system we propose to use two different sets of EMG features, namely time domain (TD) and time-frequency representation (TFR). Also in order to decrease the undesirable effects of the dimension of these feature sets, principle component analysis (PCA) is utilized. Results In this study, the myoelectric signals considered for classification consists of six unique hand movements. Features chosen for EMG signal are time and time

  7. A novel approach for removing ECG interferences from surface EMG signals using a combined ANFIS and wavelet.

    PubMed

    Abbaspour, Sara; Fallah, Ali; Lindén, Maria; Gholamhosseini, Hamid

    2016-02-01

    In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS-wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97dB and 0.02 respectively and a significantly higher correlation coefficient (p<0.05). PMID:26643795

  8. Fast generation model of high density surface EMG signals in a cylindrical conductor volume.

    PubMed

    Carriou, Vincent; Boudaoud, Sofiane; Laforet, Jeremy; Ayachi, Fouaz Sofiane

    2016-07-01

    In the course of the last decade, fast and qualitative computing power developments have undoubtedly permitted for a better and more realistic modeling of complex physiological processes. Due to this favorable environment, a fast, generic and reliable model for high density surface electromyographic (HD-sEMG) signal generation with a multilayered cylindrical description of the volume conductor is presented in this study. Its main peculiarity lies in the generation of a high resolution potential map over the skin related to active Motor Units (MUs). Indeed, the analytical calculus is fully performed in the frequency domain. HD-sEMG signals are obtained by surfacic numerical integration of the generated high resolution potential map following a variety of electrode shapes. The suggested model is implemented using parallel computing techniques as well as by using an object-oriented approach which is comprehensive enough to be fairly quickly understood, used and potentially upgraded. To illustrate the model abilities, several simulation analyses are put forward in the results section. These simulations have been performed on the same muscle anatomy while varying the number of processes in order to show significant speed improvement. Accuracy of the numerical integration method, illustrating electrode shape diversity, is also investigated in comparison to analytical transfer functions definition. An additional section provides an insight on the volume detection of a circular electrode according to its radius. Furthermore, a large scale simulation is introduced with 300MUs in the muscle and a HD-sEMG electrode grid composed of 16×16 electrodes for three constant isometric contractions in 12s. Finally, advantages and limitations of the proposed model are discussed with a focus on perspective works. PMID:27183535

  9. Neural network committees for finger joint angle estimation from surface EMG signals

    PubMed Central

    Shrirao, Nikhil A; Reddy, Narender P; Kosuri, Durga R

    2009-01-01

    Background In virtual reality (VR) systems, the user's finger and hand positions are sensed and used to control the virtual environments. Direct biocontrol of VR environments using surface electromyography (SEMG) signals may be more synergistic and unconstraining to the user. The purpose of the present investigation was to develop a technique to predict the finger joint angle from the surface EMG measurements of the extensor muscle using neural network models. Methodology SEMG together with the actual joint angle measurements were obtained while the subject was performing flexion-extension rotation of the index finger at three speeds. Several neural networks were trained to predict the joint angle from the parameters extracted from the SEMG signals. The best networks were selected to form six committees. The neural network committees were evaluated using data from new subjects. Results There was hysteresis in the measured SMEG signals during the flexion-extension cycle. However, neural network committees were able to predict the joint angle with reasonable accuracy. RMS errors ranged from 0.085 ± 0.036 for fast speed finger-extension to 0.147 ± 0.026 for slow speed finger extension, and from 0.098 ± 0.023 for the fast speed finger flexion to 0.163 ± 0.054 for slow speed finger flexion. Conclusion Although hysteresis was observed in the measured SEMG signals, the committees of neural networks were able to predict the finger joint angle from SEMG signals. PMID:19154615

  10. Arm Orthosis/Prosthesis Movement Control Based on Surface EMG Signal Extraction.

    PubMed

    Suberbiola, Aaron; Zulueta, Ekaitz; Lopez-Guede, Jose Manuel; Etxeberria-Agiriano, Ismael; Graña, Manuel

    2015-05-01

    This paper shows experimental results on electromyography (EMG)-based system control applied to motorized orthoses. Biceps and triceps EMG signals are captured through two biometrical sensors, which are then filtered and processed by an acquisition system. Finally an output/control signal is produced and sent to the actuators, which will then perform the actual movement, using algorithms based on autoregressive (AR) models and neural networks, among others. The research goal is to predict the desired movement of the lower arm through the analysis of EMG signals, so that the movement can be reproduced by an arm orthosis, powered by two linear actuators. In this experiment, best accuracy has achieved values up to 91%, using a fourth-order AR-model and 100ms block length. PMID:25851029

  11. Age Related Differences in the Surface EMG Signals on Adolescent's Muscle during Contraction

    NASA Astrophysics Data System (ADS)

    Uddin Ahamed, Nizam; Taha, Zahari; Alqahtani, Mahdi; Altwijri, Omar; Rahman, Matiur; Deboucha, Abdelhakim

    2016-02-01

    The aim of this study was to investigate whether there are differences in the amplitude of the EMG signal among five different age groups of adolescent's muscle. Fifteen healthy adolescents participated in this study and they were divided into five age groups (13, 14, 15, 16 and 17 years). Subjects were performed dynamic contraction during lifting a standard weight (3-kg dumbbell) and EMG signals were recorded from their Biceps Brachii (BB) muscle. Two common EMG analysis techniques namely root mean square (RMS) and mean absolute values (MAV) were used to find the differences. The statistical analysis was included: linear regression to examine the relationships between EMG amplitude and age, repeated measures ANOVA to assess differences among the variables, and finally Coefficient of Variation (CoV) for signal steadiness among the groups of subjects during contraction. The result from RMS and MAV analysis shows that the 17-years age groups exhibited higher activity (0.28 and 0.19 mV respectively) compare to other groups (13-Years: 0.26 and 0.17 mV, 14-years: 0.25 and 0.23 mV, 15-Years: 0.23 and 0.16 mV, 16-years: 0.23 and 0.16 mV respectively). Also, this study shows modest correlation between age and signal activities among all age group's muscle. The experiential results can play a pivotal role for developing EMG prosthetic hand controller, neuromuscular system, EMG based rehabilitation aid and movement biomechanics, which may help to separate age groups among the adolescents.

  12. An optimized method for tremor detection and temporal tracking through repeated second order moment calculations on the surface EMG signal.

    PubMed

    De Marchis, Cristiano; Schmid, Maurizio; Conforto, Silvia

    2012-11-01

    In this study, the problem of detecting and tracking tremor from the surface myoelectric signal is addressed. A method based on the calculation of a Second Order Moment Function (SOMF) inside a window W sliding over the sEMG signal is here presented. An analytical formulation of the detector allows the extraction of the optimal parameters characterizing the algorithm. Performance of the optimized method is assessed on a set of synthetic tremor sEMG signals in terms of sensitivity, precision and accuracy through the use of a properly defined cost function able to explain the overall detector performance. The obtained results are compared to those emerging from the application of optimized versions of traditional detection techniques. Once tested on a database of synthetic tremor sEMG data, a quantitative assessment of the SOMF algorithm performance is carried out on experimental tremor sEMG signals recorded from two patients affected by Essential Tremor and from two patients affected by Parkinson's Disease. The SOMF algorithm outperforms the traditional techniques both in detecting (sensitivity and positive predictive value >99% for SNR higher than 3dB) and in estimating timings of muscular tremor bursts (bias and standard deviation on the estimation of the onset and offset time instants lower than 8ms). Its independence from the SNR level and its low computational cost make it suitable for real-time implementation and clinical use. PMID:22257701

  13. 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. PMID:24403424

  14. Decomposition of indwelling EMG signals

    PubMed Central

    Nawab, S. Hamid; Wotiz, Robert P.; De Luca, Carlo J.

    2008-01-01

    Decomposition of indwelling electromyographic (EMG) signals is challenging in view of the complex and often unpredictable behaviors and interactions of the action potential trains of different motor units that constitute the indwelling EMG signal. These phenomena create a myriad of problem situations that a decomposition technique needs to address to attain completeness and accuracy levels required for various scientific and clinical applications. Starting with the maximum a posteriori probability classifier adapted from the original precision decomposition system (PD I) of LeFever and De Luca (25, 26), an artificial intelligence approach has been used to develop a multiclassifier system (PD II) for addressing some of the experimentally identified problem situations. On a database of indwelling EMG signals reflecting such conditions, the fully automatic PD II system is found to achieve a decomposition accuracy of 86.0% despite the fact that its results include low-amplitude action potential trains that are not decomposable at all via systems such as PD I. Accuracy was established by comparing the decompositions of indwelling EMG signals obtained from two sensors. At the end of the automatic PD II decomposition procedure, the accuracy may be enhanced to nearly 100% via an interactive editor, a particularly significant fact for the previously indecomposable trains. PMID:18483170

  15. Ambulatory device for surface EMG recordings.

    PubMed

    Airaksinen, O; Airaksinen, K

    1998-01-01

    The principles of electromyographic (EMG) analysis can be divided into the following groups: signal or motor unit shape analysis, amplitude analysis, multi-channel or successive time difference analysis, signal frequency composition analysis, change of frequency time based analysis based on simultaneous amplitude or frequency based analysis or concentric and excentric work based shape and amplitude ratio analysis. The aim of this paper is to present an ambulatory portable device for surface EMG analysing both for integrated EMG and for spectral analysis. The reliability of surface EMG recordings have established. The recent new technology gets a possibility to measure by reliable way surface EMG on-line during exercise, rehabilitation or occupational conditions. Portable EMG measurement unit and analysing program seems to be suitable for documentation of the response of rehabilitation programs, effects of physiotherapy, analysing the muscle balance and activity of sportsman and for documentation of occupational health problems. Automatic interpretation and wide data base for patient data makes the system useful in daily practice. PMID:9607100

  16. 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

  17. 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.

  18. Recognition of hand motions via surface EMG signal with rough entropy.

    PubMed

    Zhong, Jin; Shi, Jun; Cai, Yin; Zhang, Qi

    2011-01-01

    The rough entropy (RoughEn) is developed based on the rough set theory. It has the advantage of low computational complexity, because there is no parameter to set in RoughEn. In this paper, we characterized the feature of surface electromyography (SEMG) signal with RoughEn and then used support vector machine to classify six different hand motions. The sample entropy, wavelet entropy and approximate entropy were compared with RoughEn to evaluate the performance of characterizing SEMG signals. The experimental results indicated that the RoughEn-based classification outperformed other entropy based methods for recognizing six hand motions from four-channel SEMG signals with the best recognition accuracy of 95.19 ± 2.99%. The results suggest that RoughEn has the potential to be used in the SEMG-based prosthetic control as a method of feature extraction. PMID:22255241

  19. Removing ECG Artifact from the Surface EMG Signal Using Adaptive Subtraction Technique

    PubMed Central

    Abbaspour, S; Fallah, A

    2014-01-01

    Background: The electrocardiogram artifact is a major contamination in the electromyogram signals when electromyogram signal is recorded from upper trunk muscles and because of that the contaminated electromyogram is not useful. Objective: Removing electrocardiogram contamination from electromyogram signals. Methods: In this paper, the clean electromyogram signal, electrocardiogram artifact and electrocardiogram signal were recorded from leg muscles, the pectoralis major muscle of the left side and V4, respectively. After the pre-processing, contaminated electromyogram signal is simulated with a combination of clean electromyogram and electrocardiogram artifact. Then, contaminated electromyogram is cleaned using adaptive subtraction method. This method contains some steps; (1) QRS detection, (2) formation of electrocardiogram template by averaging the electrocardiogram complexes, (3) using low pass filter to remove undesirable artifacts, (4) subtraction. Results: Performance of our method is evaluated using qualitative criteria, power spectrum density and coherence and quantitative criteria signal to noise ratio, relative error and cross correlation. The result of signal to noise ratio, relative error and cross correlation is equal to 10.493, 0.04 and %97 respectively. Finally, there is a comparison between proposed method and some existing methods. Conclusion: The result indicates that adaptive subtraction method is somewhat effective to remove electrocardiogram artifact from contaminated electromyogram signal and has an acceptable result. PMID:25505766

  20. 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.

  1. An EMG-based robot control scheme robust to time-varying EMG signal features.

    PubMed

    Artemiadis, Panagiotis K; Kyriakopoulos, Kostas J

    2010-05-01

    Human-robot control interfaces have received increased attention during the past decades. With the introduction of robots in everyday life, especially in providing services to people with special needs (i.e., elderly, people with impairments, or people with disabilities), there is a strong necessity for simple and natural control interfaces. In this paper, electromyographic (EMG) signals from muscles of the human upper limb are used as the control interface between the user and a robot arm. EMG signals are recorded using surface EMG electrodes placed on the user's skin, making the user's upper limb free of bulky interface sensors or machinery usually found in conventional human-controlled systems. The proposed interface allows the user to control in real time an anthropomorphic robot arm in 3-D space, using upper limb motion estimates based only on EMG recordings. Moreover, the proposed interface is robust to EMG changes with respect to time, mainly caused by muscle fatigue or adjustments of contraction level. The efficiency of the method is assessed through real-time experiments, including random arm motions in the 3-D space with variable hand speed profiles. PMID:20172839

  2. Estimation of muscle strength during motion recognition using multichannel surface EMG signals.

    PubMed

    Nagata, Kentaro; Nakano, Takemi; Magatani, Kazushige; Yamada, Masafumi

    2008-01-01

    The use of kinesiological electromyography is established as an evaluation tool for various kinds of applied research, and surface electromyogram (SEMG) has been widely used as a control source for human interfaces such as in a myoelectric prosthetic hand (we call them 'SEMG interfaces'). It is desirable to be able to control the SEMG interfaces with the same feeling as body movement. The existing SEMG interface mainly focuses on how to achieve accurate recognition of the intended movement. However, detecting muscular strength and reduced number of electrodes are also an important factor in controlling them. Therefore, our objective in this study is the development of and the estimation method for muscular strength that maintains the accuracy of hand motion recognition to reflect the result of measured power in a controlled object. Although the muscular strength can be evaluated by various methods, in this study a grasp force index was applied to evaluate the muscular strength. In order to achieve our objective, we directed our attention to measuring all valuable information for SEMG. This work proposes an application method of two simple linear models, and the selection method of an optimal electrode configuration to use them effectively. Our system required four SEMG measurement electrodes in which locations differed for every subject depending on the individual's characteristics, and those were selected from a 96ch multi electrode using the Monte Carlo method. From the experimental results, the performance in six normal subjects indicated that the recognition rate of four motions were perfect and the grasp force estimated result fit well with the actual measurement result. PMID:19162665

  3. 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. PMID:19747943

  4. 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

  5. Accuracy assessment of CKC high-density surface EMG decomposition in biceps femoris muscle

    NASA Astrophysics Data System (ADS)

    Marateb, H. R.; McGill, K. C.; Holobar, A.; Lateva, Z. C.; Mansourian, M.; Merletti, R.

    2011-10-01

    The aim of this study was to assess the accuracy of the convolution kernel compensation (CKC) method in decomposing high-density surface EMG (HDsEMG) signals from the pennate biceps femoris long-head muscle. Although the CKC method has already been thoroughly assessed in parallel-fibered muscles, there are several factors that could hinder its performance in pennate muscles. Namely, HDsEMG signals from pennate and parallel-fibered muscles differ considerably in terms of the number of detectable motor units (MUs) and the spatial distribution of the motor-unit action potentials (MUAPs). In this study, monopolar surface EMG signals were recorded from five normal subjects during low-force voluntary isometric contractions using a 92-channel electrode grid with 8 mm inter-electrode distances. Intramuscular EMG (iEMG) signals were recorded concurrently using monopolar needles. The HDsEMG and iEMG signals were independently decomposed into MUAP trains, and the iEMG results were verified using a rigorous a posteriori statistical analysis. HDsEMG decomposition identified from 2 to 30 MUAP trains per contraction. 3 ± 2 of these trains were also reliably detected by iEMG decomposition. The measured CKC decomposition accuracy of these common trains over a selected 10 s interval was 91.5 ± 5.8%. The other trains were not assessed. The significant factors that affected CKC decomposition accuracy were the number of HDsEMG channels that were free of technical artifact and the distinguishability of the MUAPs in the HDsEMG signal (P < 0.05). These results show that the CKC method reliably identifies at least a subset of MUAP trains in HDsEMG signals from low force contractions in pennate muscles.

  6. Comparative study of PCA in classification of multichannel EMG signals.

    PubMed

    Geethanjali, P

    2015-06-01

    Electromyographic (EMG) signals are abundantly used in the field of rehabilitation engineering in controlling the prosthetic device and significantly essential to find fast and accurate EMG pattern recognition system, to avoid intrusive delay. The main objective of this paper is to study the influence of Principal component analysis (PCA), a transformation technique, in pattern recognition of six hand movements using four channel surface EMG signals from ten healthy subjects. For this reason, time domain (TD) statistical as well as auto regression (AR) coefficients are extracted from the four channel EMG signals. The extracted statistical features as well as AR coefficients are transformed using PCA to 25, 50 and 75 % of corresponding original feature vector space. The classification accuracy of PCA transformed and non-PCA transformed TD statistical features as well as AR coefficients are studied with simple logistic regression (SLR), decision tree (DT) with J48 algorithm, logistic model tree (LMT), k nearest neighbor (kNN) and neural network (NN) classifiers in the identification of six different movements. The Kruskal-Wallis (KW) statistical test shows that there is a significant reduction (P < 0.05) in classification accuracy with PCA transformed features compared to non-PCA transformed features. SLR with non-PCA transformed time domain (TD) statistical features performs better in accuracy and computational power compared to other features considered in this study. In addition, the motion control of three drives for six movements of the hand is implemented with SLR using TD statistical features in off-line with TMSLF2407 digital signal controller (DSC). PMID:25860845

  7. Voluntary Movement Controlled by the Surface EMG Signal for Tissue-Engineered Skeletal Muscle on a Gripping Tool

    PubMed Central

    Kabumoto, Ken-ichiro; Hoshino, Takayuki; Akiyama, Yoshitake

    2013-01-01

    We have developed a living prosthesis consisting of a living muscle-powered device, which is controlled by neuronal signals to recover some of the functions of a lost extremity. A tissue-engineered skeletal muscle was fabricated with two anchorage points from a primary rat myoblast cultured in a collagen Matrigel mixed gel. Differentiation to the skeletal muscle was confirmed in the tissue-engineered skeletal muscle, and the contraction force increased with increasing frequency of electric stimulation. Then, the tissue-engineered skeletal muscle was assembled into a gripper-type microhand. The tissue-engineered skeletal muscle of the microhand was stimulated electrically, which was then followed by the voluntary movement of the subject's hand. The signal of the surface electromyogram from a subject was processed to mimic the firing spikes of a neuromuscular junction to control the contraction of the tissue-engineered skeletal muscle. The tele-operation of the microhand was demonstrated by optical microscope observations. PMID:23444880

  8. 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.

  9. Driving Electric Vehicle by EMG Signal Considering Frequency Components

    NASA Astrophysics Data System (ADS)

    Aso, Shinichi; Sasaki, Akinori; Hashimoto, Hiroshi; Ishii, Chiharu

    This paper proposes a useful method driving the electric vehicle by EMG signals (Electromyographic signals) which are filtered on the basis of frequency components which change with muscle contraction. This method estimates strength of muscular tension by a single EMG signal. By our method, user is able to control speed of the electric vehicle by strength of muscular tension. The method of speed control may give user good or bad operation feeling in the meaning of SD (Semantic Differential) method and factor analysis. The operation feeling is evaluated by experiment on EMG interface in cases of using filters or not. As a result, it is shown that operation feeling is influenced by this method.

  10. Study on upper limb rehabilitation system based on surface EMG.

    PubMed

    Wang, Lan; Li, Hailong; Wang, Zhengyu; Meng, Fandong

    2015-01-01

    During the rehabilitation process, it is essential to accurately judge a patient's recovery in a timely manner. A reasonable and matched training program is significant in the development of rehabilitation system. This paper presents a new upper limb rehabilitation training system, which consists of an upper limb rehabilitation training device, a current detection circuit, a motor speed test circuit, a surface EMG (sEMG) sensor, and a dSPACE HIL simulation platform. The real-time output torque of the servo motor is calculated by using the motor's real-time current and speed, in order to monitor the patient's training situation. The signal of sEMG is collected in real time and is processed with root mean square (RMS) to characterize the degree of muscle activation. Based on this rehabilitation system, maximum voluntary contraction (MVC) experiments, passive training experiments under different speeds, and active training experiments under different damping are studied. The results show that this new system performs real-time and accurate monitoring of a patient's training situation. It can also assess a patient's recovery through muscle activation. To a certain extent, this system provides a platform for research and development of rehabilitation medical engineering. PMID:26406076

  11. 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.

  12. Surface EMG Decomposition Based on K-means Clustering and Convolution Kernel Compensation

    PubMed Central

    Ning, Yong; Zhu, Xiangjun; Zhu, Shanan; Zhang, Yingchun

    2015-01-01

    A new approach has been developed by combining the K-mean clustering (KMC) method and a modified convolution kernel compensation (CKC) method for multi-channel surface electromyogram (EMG) decomposition. The KMC method was first utilized to cluster vectors of observations at different time instants and then estimate the initial innervation pulse train (IPT). The CKC method, modified with a novel multi-step iterative process, was conducted to update the estimated IPT. The performance of the proposed K-means clustering - Modified CKC (KmCKC) approach was evaluated by reconstructing IPTs from both simulated and experimental surface EMG signals. The KmCKC approach successfully reconstructed all 10 IPTs from the simulated surface EMG signals with true positive rates (TPR) of over 90% with a low signal-to-noise ratio (SNR) of −10dB. Over 10 motor units were also successfully extracted from the 64-channel experimental surface EMG signals of the first dorsal interosseous (FDI) muscles when a contraction force was held at 8 N by using the KmCKC approach. A ‘two-source’ test was further conducted with 64-channel surface EMG signals. The high percentage of common MUs and common pulses (over 92% at all force levels) between the IPTs reconstructed from the two independent groups of surface EMG signals demonstrates the reliability and capability of the proposed KmCKC approach in multi-channel surface EMG decomposition. Results from both simulated and experimental data are consistent and confirm that the proposed KmCKC approach can successfully reconstruct IPTs with high accuracy at different levels of contraction. PMID:25486655

  13. Subspace based adaptive denoising of surface EMG from neurological injury patients

    NASA Astrophysics Data System (ADS)

    Liu, Jie; Ying, Dongwen; Zev Rymer, William; Zhou, Ping

    2014-10-01

    Objective: After neurological injuries such as spinal cord injury, voluntary surface electromyogram (EMG) signals recorded from affected muscles are often corrupted by interferences, such as spurious involuntary spikes and background noises produced by physiological and extrinsic/accidental origins, imposing difficulties for signal processing. Conventional methods did not well address the problem caused by interferences. It is difficult to mitigate such interferences using conventional methods. The aim of this study was to develop a subspace-based denoising method to suppress involuntary background spikes contaminating voluntary surface EMG recordings. Approach: The Karhunen-Loeve transform was utilized to decompose a noisy signal into a signal subspace and a noise subspace. An optimal estimate of EMG signal is derived from the signal subspace and the noise power. Specifically, this estimator is capable of making a tradeoff between interference reduction and signal distortion. Since the estimator partially relies on the estimate of noise power, an adaptive method was presented to sequentially track the variation of interference power. The proposed method was evaluated using both semi-synthetic and real surface EMG signals. Main results: The experiments confirmed that the proposed method can effectively suppress interferences while keep the distortion of voluntary EMG signal in a low level. The proposed method can greatly facilitate further signal processing, such as onset detection of voluntary muscle activity. Significance: The proposed method can provide a powerful tool for suppressing background spikes and noise contaminating voluntary surface EMG signals of paretic muscles after neurological injuries, which is of great importance for their multi-purpose applications.

  14. 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.

  15. Volume conduction in an anatomically based surface EMG model.

    PubMed

    Lowery, Madeleine M; Stoykov, Nikolay S; Dewald, Julius P A; Kuiken, Todd A

    2004-12-01

    A finite-element model to simulate surface electromyography (EMG) in a realistic human upper arm is presented. The model is used to explore the effect of limb geometry on surface-detected muscle fiber action potentials. The model was based on magnetic resonance images of the subject's upper arm and includes both resistive and capacitive material properties. To validate the model geometry, experimental and simulated potentials were compared at different electrode sites during the application of a subthreshold sinusoidal current source to the skin surface. Of the material properties examined, the closest approximation to the experimental data yielded a mean root-mean-square (rms) error of the normalized surface potential of 18% or 27%, depending on the site of the applied source. Surface-detected action potentials simulated using the realistic volume conductor model and an idealized cylindrical model based on the same limb geometry were then compared. Variation in the simulated limb geometry had a considerable effect on action potential shape. However, the rate of decay of the action potential amplitude with increasing distance from the fiber was similar in both models. Inclusion of capacitive material properties resulted in temporal low-pass filtering of the surface action potentials. This effect was most pronounced in the end-effect components of action potentials detected at locations far from the active fiber. It is concluded that accurate modeling of the limb geometry, asymmetry, tissue capacitance and fiber curvature is important when the specific action potential shapes are of interest. However, if the objective is to examine more qualitative features of the surface EMG signal, then an idealized volume conductor model with appropriate tissue thicknesses provides a close approximation. PMID:15605861

  16. 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.

  17. Uneven spatial distribution of surface EMG: what does it mean?

    PubMed

    Gallina, Alessio; Merletti, Roberto; Gazzoni, Marco

    2013-04-01

    The aim of this work is to show how changes in surface electromyographic activity (sEMG) during a repetitive, non-constant force contraction can be detected and interpreted on the basis of the amplitude distribution provided by high-density sEMG techniques. Twelve healthy male subjects performed isometric shoulder elevations, repeating five times a force ramp profile up to 25 % of the maximal voluntary contraction (MVC). A 64-electrode matrix was used to detect sEMG from the trapezius muscle. The sEMG amplitude distribution was obtained for the force levels in the range 5-25 % MVC with steps of 5 % MVC. The effect of force level, subject, electrode position and ramp repetition on the sEMG amplitude distribution was tested. The sEMG amplitude was significantly smaller in the columns of the electrode grid over the tendons (repeated measures ANOVA, p < 0.01). The barycentre of the distribution of sEMG amplitude was subject-specific (Kruskal-Wallis test, p < 0.01), and shifted caudally with the increase of force levels and cranially with the repetition of the motor task (both p < 0.01, repeated measures ANOVA). The results are discussed in terms of motor unit recruitment in different muscle sub-portions. It is concluded that the sEMG amplitude distribution obtained by multichannel techniques provides useful information in the study of muscle activity, and that changes in the spatial distribution of the recruited motor units during a force varying isometric contraction might partially explain the variability observed in the activation pattern of the upper trapezius muscle. PMID:23001682

  18. Surface EMG pattern recognition for real-time control of a wrist exoskeleton

    PubMed Central

    2010-01-01

    Background Surface electromyography (sEMG) signals have been used in numerous studies for the classification of hand gestures and movements and successfully implemented in the position control of different prosthetic hands for amputees. sEMG could also potentially be used for controlling wearable devices which could assist persons with reduced muscle mass, such as those suffering from sarcopenia. While using sEMG for position control, estimation of the intended torque of the user could also provide sufficient information for an effective force control of the hand prosthesis or assistive device. This paper presents the use of pattern recognition to estimate the torque applied by a human wrist and its real-time implementation to control a novel two degree of freedom wrist exoskeleton prototype (WEP), which was specifically developed for this work. Methods Both sEMG data from four muscles of the forearm and wrist torque were collected from eight volunteers by using a custom-made testing rig. The features that were extracted from the sEMG signals included root mean square (rms) EMG amplitude, autoregressive (AR) model coefficients and waveform length. Support Vector Machines (SVM) was employed to extract classes of different force intensity from the sEMG signals. After assessing the off-line performance of the used classification technique, the WEP was used to validate in real-time the proposed classification scheme. Results The data gathered from the volunteers were divided into two sets, one with nineteen classes and the second with thirteen classes. Each set of data was further divided into training and testing data. It was observed that the average testing accuracy in the case of nineteen classes was about 88% whereas the average accuracy in the case of thirteen classes reached about 96%. Classification and control algorithm implemented in the WEP was executed in less than 125 ms. Conclusions The results of this study showed that classification of EMG signals by

  19. 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.

  20. 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. PMID:25836061

  1. Wiener filtering of surface EMG with a priori SNR estimation toward myoelectric control for neurological injury patients.

    PubMed

    Liu, Jie; Ying, Dongwen; Zhou, Ping

    2014-12-01

    Voluntary surface electromyogram (EMG) signals from neurological injury patients are often corrupted by involuntary background interference or spikes, imposing difficulties for myoelectric control. We present a novel framework to suppress involuntary background spikes during voluntary surface EMG recordings. The framework applies a Wiener filter to restore voluntary surface EMG signals based on tracking a priori signal to noise ratio (SNR) by using the decision-directed method. Semi-synthetic surface EMG signals contaminated by different levels of involuntary background spikes were constructed from a database of surface EMG recordings in a group of spinal cord injury subjects. After the processing, the onset detection of voluntary muscle activity was significantly improved against involuntary background spikes. The magnitude of voluntary surface EMG signals can also be reliably estimated for myoelectric control purpose. Compared with the previous sample entropy analysis for suppressing involuntary background spikes, the proposed framework is characterized by quick and simple implementation, making it more suitable for application in a myoelectric control system toward neurological injury rehabilitation. PMID:25443536

  2. Simulation of surface EMG for the analysis of muscle activity during whole body vibratory stimulation.

    PubMed

    Fratini, Antonio; Bifulco, Paolo; Romano, Maria; Clemente, Fabrizio; Cesarelli, Mario

    2014-01-01

    This study aims to reproduce the effect of motor-unit synchronization on surface EMG recordings during vibratory stimulation to highlight vibration evoked muscle activity. The authors intended to evaluate, through numerical simulations, the changes in surface EMG spectrum in muscles undergoing whole body vibration stimulation. In some specific bands, in fact, vibration induced motion artifacts are also typically present. In addition, authors meant to compare the simulated EMGs with respect to real recordings in order to discriminate the effect of synchronization of motor units discharges with vibration frequencies from motion artifacts. Computations were performed using a model derived from previous studies and modified to consider the effect of vibratory stimulus, the motor unit synchronization and the endplates-electrodes relative position on the EMG signal. Results revealed that, in particular conditions, synchronization of MUs' discharge generates visible peaks at stimulation frequency and its harmonics. However, only a part of the total power of surface EMGs might be enclosed within artifacts related bands (± 1 Hz centered at the stimulation frequency and its superior harmonics) even in case of strong synchronization of motor units discharges with the vibratory stimulus. PMID:24183387

  3. 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. PMID:24552510

  4. Hardware System for Real-Time EMG Signal Acquisition and Separation Processing during Electrical Stimulation.

    PubMed

    Hsueh, Ya-Hsin; Yin, Chieh; Chen, Yan-Hong

    2015-09-01

    The study aimed to develop a real-time electromyography (EMG) signal acquiring and processing device that can acquire signal during electrical stimulation. Since electrical stimulation output can affect EMG signal acquisition, to integrate the two elements into one system, EMG signal transmitting and processing method has to be modified. The whole system was designed in a user-friendly and flexible manner. For EMG signal processing, the system applied Altera Field Programmable Gate Array (FPGA) as the core to instantly process real-time hybrid EMG signal and output the isolated signal in a highly efficient way. The system used the power spectral density to evaluate the accuracy of signal processing, and the cross correlation showed that the delay of real-time processing was only 250 μs. PMID:26210898

  5. 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

  6. 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 (R2 = 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 (R2 = 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 (R2 = 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

  7. Nonlinear Analysis of Surface EMG Time Series

    NASA Astrophysics Data System (ADS)

    Zurcher, Ulrich; Kaufman, Miron; Sung, Paul

    2004-04-01

    Applications of nonlinear analysis of surface electromyography time series of patients with and without low back pain are presented. Limitations of the standard methods based on the power spectrum are discussed.

  8. Absolute and relative intrasession reliability of surface EMG variables for voluntary precise forearm movements.

    PubMed

    Carius, Daniel; Kugler, Patrick; Kuhwald, Hans-Marten; Wollny, Rainer

    2015-12-01

    The reliability of surface electromyography (EMG) derived parameters is of high importance, but there is distinct lack of studies concerning the reliability during dynamic contractions. Especially Amplitude, Fourier and Wavelet parameter in conjunction have not been tested so far. The interpretation of the EMG variables might be difficult because the movement itself introduces additional factors that affect its characteristics. The aim of this study was to determine the relative and absolute intrasession reliability of electromyographic (EMG) variables of selected arm muscles during concurrent precise elbow extension/flexion movements at different force levels and movement speed. Participants (all-male: n = 17, range 20-32 years) were asked to adapt to a gross-motor visuomotor tracking task (elbow extension/flexion movement) using a custom-built lever arm apparatus. After sufficient adaptation surface electromyography was used to record the electrical activity of mm. biceps brachii, brachioradialis and triceps brachii, and the signal amplitude (RMS [μV]) and the mean frequency of the power spectrum (MNF [Hz]) were computed. Additionally Wavelet analysis was used. Relative reproducibility (intraclass correlation) for signal amplitude, mean frequency of the power spectrum and Wavelet intensity during dynamic contractions was fair to good, independent of force level and movement speed (ICC = 0.71-0.98). The amount of absolute intrasession reliability (coefficient of variation) of EMG variables depends on muscle and force level. PMID:26391454

  9. Estimation of elbow-induced wrist force with EMG signals using fast orthogonal search.

    PubMed

    Mobasser, Farid; Eklund, J Mikael; Hashtrudi-Zaad, Keyvan

    2007-04-01

    In many studies and applications that include direct human involvement-such as human-robot interaction, control of prosthetic arms, and human factor studies-hand force is needed for monitoring or control purposes. The use of inexpensive and easily portable active electromyogram (EMG) electrodes and position sensors would be advantageous in these applications compared to the use of force sensors, which are often very expensive and require bulky frames. Multilayer perceptron artificial neural networks (MLPANN) have been used commonly in the literature to model the relationship between surface EMG signals and muscle or limb forces for different anatomies. This paper investigates the use of fast orthogonal search (FOS), a time-domain method for rapid nonlinear system identification, for elbow-induced wrist force estimation. It further compares the forces estimated using FOS with the forces estimated by MLPANN for the same human anatomy under an ensemble of operational conditions. In this paper, the EMG signal readings from upper arm muscles involved in elbow joint movement and sensed elbow angular position and velocity are utilized as inputs. A single degree-of-freedom robotic experimental testbed has been constructed and used for data collection, training and validation. PMID:17405375

  10. 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. PMID:18003415

  11. Amplitude and frequency changes in surface EMG of biceps femoris during five days Bruce Protocol treadmill test.

    PubMed

    Jamaluddin, Fauzani N; Ahmad, Siti A; Noor, Samsul Bahari Mohd; Hassan, Wan Zuha Wan; Yaakob, Azhar; Adam, Yunus; Ali, Sawal H M

    2015-08-01

    Electromyography (EMG) is one of the indirect tools in indexing fatigue. Fatigue can be detected when there are changes on amplitude and frequency. However, various outcomes from literature make researchers conclude that EMG is not a reliable tool to measure fatigue. This paper investigates EMG behavior of biceps femoris in median frequency and mean absolute value during five days of Bruce Protocol treadmill test. Before that, surface EMG signals are filtered using band pass filter cut-off at 20-500Hz and are de-noised using db45 1-decimated wavelet transform. Five participants achieved more than 85% of their maximal heart rate during the running activity. The authors also consider other markers of fatigue such as performance, muscle soreness and lethargy as indicators to adaptation and maladaptation conditions. Result shows that turning points of median frequency and mean absolute value are very significant in indexing fatigue and indicators to adaptation of resistive training. PMID:26737713

  12. Long term stability of surface EMG pattern classification for prosthetic control.

    PubMed

    Amsüss, Sebastian; Paredes, Liliana P; Rudigkeit, Nina; Graimann, Bernhard; Herrmann, Michael J; Farina, Dario

    2013-01-01

    Long-term functioning of a hand prosthesis is crucial for its acceptance by patients with upper limb deficit. In this study the reliability over days of the performance of pattern classification approaches based on surface electromyography (sEMG) signal for the control of upper limb prostheses was investigated. Recordings of sEMG from the forearm muscles were obtained across five consecutive days from five healthy subjects. It was demonstrated that the classification performance decreased monotonically on average by 4.1% per day. It was also found that the accumulated error was confined to three of the eight movement classes investigated. This contribution gives insight on the long term behavior of pattern classification, which is crucial for commercial viability. PMID:24110514

  13. 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…

  14. 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. PMID:24948528

  15. Feature extraction and classification of sEMG signals applied to a virtual hand prosthesis.

    PubMed

    Tello, Richard M G; Bastos-Filho, Teodiano; Frizera-Neto, Anselmo; Arjunan, Sridhar; Kumar, Dinesh K

    2013-01-01

    This paper presents the classification of motor tasks, using surface electromyography (sEMG) to control a virtual prosthetic hand for rehabilitation of amputees. Two types of classifiers are compared: k-Nearest Neighbor (k-NN) and Bayesian (Discriminant Analysis). Motor tasks are divided into four groups correlated. The volunteers were people without amputation and several analyzes of each of the signals were conducted. The online simulations use the sliding window technique and for feature extraction RMS (Root Mean Square), VAR (Variance) and WL (Waveform Length) values were used. A model is proposed for reclassification using cross-validation in order to validate the classification, and a visualization in Sammon Maps is provided in order to observe the separation of the classes for each set of motor tasks. Finally, the proposed method can be implemented in a computer interface providing a visual feedback through an virtual hand prosthetic developed in Visual C++ and MATLAB commands. PMID:24110086

  16. Effective low-power wearable wireless surface EMG sensor design based on analog-compressed sensing.

    PubMed

    Balouchestani, Mohammadreza; Krishnan, Sridhar

    2014-01-01

    Surface Electromyography (sEMG) is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1) they are not able to provide real-time monitoring; (2) they suffer from long processing time and low speed; (3) they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS) architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC). At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL) framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR). In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD) to 24%, Root Mean Squared Error (RMSE) to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR) for the reconstruction process. PMID:25526357

  17. Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing

    PubMed Central

    Balouchestani, Mohammadreza; Krishnan, Sridhar

    2014-01-01

    Surface Electromyography (sEMG) is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1) they are not able to provide real-time monitoring; (2) they suffer from long processing time and low speed; (3) they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS) architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC). At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL) framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR). In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD) to 24%, Root Mean Squared Error (RMSE) to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR) for the reconstruction process. PMID:25526357

  18. 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

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

    PubMed

    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

  20. Waterproofing EMG Instrumentation.

    PubMed

    Benfield, Rebecca D; Newton, Edward R; Hortobágyi, Tibor

    2007-01-01

    While still experimental, measurement of external uterine electromyographic (EMG) activity is a more sensitive and noninvasive method for measuring uterine contractility in human labor than the methods currently used in clinical practice. Hydrotherapy is purported to improve contractility in labor, yet there have been no reports of abdominal uterine EMG activity measured during immersion. To test telemetric EMG equipment and different waterproofing techniques under dry and immersed conditions, the authors recorded surface EMG activity from the abdominal muscles of 11 healthy, nonpregnant women, 22 to 51 years of age. After attaching one pair of electrodes to the skin on either side of the umbilicus and applying the waterproofing material, the authors tested the signal by asking participants to perform a short series of leg lifts while seated in a chair to evoke abdominal muscle contractions. They were then immersed to the chest in a hydrotherapy tub while performing two to three leg lifts over 60 s every 5 min for 60 min with 20 lb of weight suspended from their ankles to counteract the buoyancy effect of water. EMG activity was continuously recorded. They then repeated the dry-measures sequence. While waterproofing remained intact, EMG signals were essentially unchanged between dry and wet conditions. Of the 11 waterproofing applications tested, 10 failed at some point. In the data from the successful application, EMG signals in both channels exhibited stable baselines throughout and an absence of low-frequency artifact. The development of this technique allows for the recording of external uterine EMG activity during hydrotherapy. The authors have begun using it to investigate the effects of hydrotherapy on uterine contractility during human labor. PMID:17172318

  1. 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.

  2. Relation between isometric muscle force and surface EMG in intrinsic hand muscles as function of the arm geometry.

    PubMed

    Del Santo, Francesco; Gelli, Francesca; Ginanneschi, Federica; Popa, Traian; Rossi, Alessandro

    2007-08-13

    Evidence exists that shoulder joint geometry influences recruitment efficiency and force-generating capacity of hand muscles [Ginanneschi, F., Del Santo, F., Dominici, F., Gelli, F., Mazzocchio, R., Rossi, A., 2005. Changes in corticomotor excitability of hand muscles in relation to static shoulder positions. Exp. Brain Res. 161 (3), 374-382; Dominici, F., Popa, T., Ginanneschi, F., Mazzocchio, R., Rossi, A., 2005. Cortico-motoneural output to intrinsic hand muscles is differentially influenced by static changes in shoulder positions. Exp. Brain Res. 164 (4), 500-504]. The present study was designed to examine the impact of changing shoulder joint position on the relation between surface EMG amplitude and isometric force production of the abductor digiti minimi muscle (ADM). EMG-force relation of ADM was examined in two shoulder positions: 30 degrees adduction (ANT) and 30 degrees abduction (POST) on the horizontal plane, i.e. under higher and lower force-generating capacity, respectively. The relation was studied over the full range isometric force (10-100% of maximum force in 10% increments, 3 s duration) by analysing root mean square (RMS), median frequency (Mf) of the power spectrum and non-linear recurrence quantification analysis (percentage of determinism: %DET) of the surface EMG signals. We found that in POST, the slope of the RMS-force relation was significantly higher than in ANT, while its general shape (strictly linear) was preserved. Averaged Mf of the EMG power spectrum was significantly higher in POST that in ANT, while no difference in %DET was observed between the two shoulder positions. The higher slope of the EMG-force relation in POST than in ANT is interpreted in terms of increased gain of the excitatory drive-firing rate relation. It is concluded that discharge from sensory receptors signalling shoulder position may act to regulate the gain of the excitatory drive-firing rate relation of motoneurones in order to compensate for reduced

  3. High-density surface EMG maps from upper-arm and forearm muscles

    PubMed Central

    2012-01-01

    Background sEMG signal has been widely used in different applications in kinesiology and rehabilitation as well as in the control of human-machine interfaces. In general, the signals are recorded with bipolar electrodes located in different muscles. However, such configuration may disregard some aspects of the spatial distribution of the potentials like location of innervation zones and the manifestation of inhomogineties in the control of the muscular fibers. On the other hand, the spatial distribution of motor unit action potentials has recently been assessed with activation maps obtained from High Density EMG signals (HD-EMG), these lasts recorded with arrays of closely spaced electrodes. The main objective of this work is to analyze patterns in the activation maps, associating them with four movement directions at the elbow joint and with different strengths of those tasks. Although the activation pattern can be assessed with bipolar electrodes, HD-EMG maps could enable the extraction of features that depend on the spatial distribution of the potentials and on the load-sharing between muscles, in order to have a better differentiation between tasks and effort levels. Methods An experimental protocol consisting of isometric contractions at three levels of effort during flexion, extension, supination and pronation at the elbow joint was designed and HD-EMG signals were recorded with 2D electrode arrays on different upper-limb muscles. Techniques for the identification and interpolation of artifacts are explained, as well as a method for the segmentation of the activation areas. In addition, variables related to the intensity and spatial distribution of the maps were obtained, as well as variables associated to signal power of traditional single bipolar recordings. Finally, statistical tests were applied in order to assess differences between information extracted from single bipolar signals or from HD-EMG maps and to analyze differences due to type of task and

  4. Novel Methods for Surface EMG Analysis and Exploration Based on Multi-Modal Gaussian Mixture Models.

    PubMed

    Vögele, Anna Magdalena; Zsoldos, Rebeka R; Krüger, Björn; Licka, Theresia

    2016-01-01

    This paper introduces a new method for data analysis of animal muscle activation during locomotion. It is based on fitting Gaussian mixture models (GMMs) to surface EMG data (sEMG). This approach enables researchers/users to isolate parts of the overall muscle activation within locomotion EMG data. Furthermore, it provides new opportunities for analysis and exploration of sEMG data by using the resulting Gaussian modes as atomic building blocks for a hierarchical clustering. In our experiments, composite peak models representing the general activation pattern per sensor location (one sensor on the long back muscle, three sensors on the gluteus muscle on each body side) were identified per individual for all 14 horses during walk and trot in the present study. Hereby we show the applicability of the method to identify composite peak models, which describe activation of different muscles throughout cycles of locomotion. PMID:27362752

  5. Novel Methods for Surface EMG Analysis and Exploration Based on Multi-Modal Gaussian Mixture Models

    PubMed Central

    Vögele, Anna Magdalena; Zsoldos, Rebeka R.; Krüger, Björn; Licka, Theresia

    2016-01-01

    This paper introduces a new method for data analysis of animal muscle activation during locomotion. It is based on fitting Gaussian mixture models (GMMs) to surface EMG data (sEMG). This approach enables researchers/users to isolate parts of the overall muscle activation within locomotion EMG data. Furthermore, it provides new opportunities for analysis and exploration of sEMG data by using the resulting Gaussian modes as atomic building blocks for a hierarchical clustering. In our experiments, composite peak models representing the general activation pattern per sensor location (one sensor on the long back muscle, three sensors on the gluteus muscle on each body side) were identified per individual for all 14 horses during walk and trot in the present study. Hereby we show the applicability of the method to identify composite peak models, which describe activation of different muscles throughout cycles of locomotion. PMID:27362752

  6. Comparison study of EMG signals compression by methods transform using vector quantization, SPIHT and arithmetic coding.

    PubMed

    Ntsama, Eloundou Pascal; Colince, Welba; Ele, Pierre

    2016-01-01

    In this article, we make a comparative study for a new approach compression between discrete cosine transform (DCT) and discrete wavelet transform (DWT). We seek the transform proper to vector quantization to compress the EMG signals. To do this, we initially associated vector quantization and DCT, then vector quantization and DWT. The coding phase is made by the SPIHT coding (set partitioning in hierarchical trees coding) associated with the arithmetic coding. The method is demonstrated and evaluated on actual EMG data. Objective performance evaluations metrics are presented: compression factor, percentage root mean square difference and signal to noise ratio. The results show that method based on the DWT is more efficient than the method based on the DCT. PMID:27104132

  7. 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

  8. A hybrid classifier fusion approach for motor unit potential classification during EMG signal decomposition.

    PubMed

    Rasheed, Sarbast; Stashuk, Daniel W; Kamel, Mohamed S

    2007-09-01

    In this paper, we propose a hybrid classifier fusion scheme for motor unit potential classification during electromyographic (EMG) signal decomposition. The scheme uses an aggregator module consisting of two stages of classifier fusion: the first at the abstract level using class labels and the second at the measurement level using confidence values. Performance of the developed system was evaluated using one set of real signals and two sets of simulated signals and was compared with the performance of the constituent base classifiers and the performance of a one-stage classifier fusion approach. Across the EMG signal data sets used and relative to the performance of base classifiers, the hybrid approach had better average classification performance overall. For the set of simulated signals of varying intensity, the hybrid classifier fusion system had on average an improved correct classification rate (CCr) (6.1%) and reduced error rate (Er) (0.4%). For the set of simulated signals of varying amounts of shape and/or firing pattern variability, the hybrid classifier fusion system had on average an improved CCr (6.2%) and reduced Er (0.9%). For real signals, the hybrid classifier fusion system had on average an improved CCr (7.5%) and reduced Er (1.7%). PMID:17867366

  9. Integrating heterogeneous classifier ensembles for EMG signal decomposition based on classifier agreement.

    PubMed

    Rasheed, Sarbast; Stashuk, Daniel W; Kamel, Mohamed S

    2010-05-01

    In this paper, we present a design methodology for integrating heterogeneous classifier ensembles by employing a diversity-based hybrid classifier fusion approach, whose aggregator module consists of two classifier combiners, to achieve an improved classification performance for motor unit potential classification during electromyographic (EMG) signal decomposition. Following the so-called overproduce and choose strategy to classifier ensemble combination, the developed system allows the construction of a large set of base classifiers, and then automatically chooses subsets of classifiers to form candidate classifier ensembles for each combiner. The system exploits kappa statistic diversity measure to design classifier teams through estimating the level of agreement between base classifier outputs. The pool of base classifiers consists of different kinds of classifiers: the adaptive certainty-based, the adaptive fuzzy k -NN, and the adaptive matched template filter classifiers; and utilizes different types of features. Performance of the developed system was evaluated using real and simulated EMG signals, and was compared with the performance of the constituent base classifiers. Across the EMG signal datasets used, the developed system had better average classification performance overall, especially in terms of reducing classification errors. For simulated signals of varying intensity, the developed system had an average correct classification rate CCr of 93.8% and an error rate Er of 2.2% compared to 93.6% and 3.2%, respectively, for the best base classifier in the ensemble. For simulated signals with varying amounts of shape and/or firing pattern variability, the developed system had a CCr of 89.1% with an Er of 4.7% compared to 86.3% and 5.6%, respectively, for the best classifier. For real signals, the developed system had a CCr of 89.4% with an Er of 3.9% compared to 84.6% and 7.1%, respectively, for the best classifier. PMID:19171524

  10. Effect of experimental jaw-muscle pain on the spatial distribution of surface EMG activity of the human masseter muscle during tooth clenching.

    PubMed

    Castroflorio, T; Falla, D; Wang, K; Svensson, P; Farina, D

    2012-02-01

    This study tested the hypothesis that painful injections of glutamate into the human masseter muscle differentially affect the distribution of the electromyographic (EMG) activity in the masseter muscle at rest and during tooth clenching. Surface EMG signals were recorded bilaterally from the superficial masseter of nine healthy men with a grid of 32 electrodes, before and after intramuscular injection of glutamate or isotonic saline, during rest and isometric contractions at 20%, 40%, 60% and 80% of the maximal voluntary bite force. Intramuscular injection of glutamate evoked moderate pain (0-10 visual analogue scale: 6·4 ± 1·4), with sensory-discriminative characteristics of the perceived pain, evaluated with the use of the McGill Pain Questionnaire (MPQ), similar to those previously reported for patients with temporomandibular disorders. There was no effect of the glutamate injection on EMG amplitude during rest, whereas during tooth clenching, the spatial distribution of the masseter EMG activity on both sides was more uniform in the painful condition compared to the control condition. Moreover, the overall EMG amplitude decreased on both sides during the more forceful tooth clenching following glutamate injection. In conclusion, a unilateral painful stimulation was associated with a bilateral inhibition of the masseter muscles during tooth clenching which resulted in a more uniform distribution of EMG activity. PMID:21848526

  11. 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.

  12. Aerobic-anaerobic transition intensity measured via EMG signals in athletes with different physical activity patterns.

    PubMed

    Jürimäe, Jaak; von Duvillard, Serge P; Mäestu, Jarek; Cicchella, Antonio; Purge, Priit; Ruosi, Sergio; Jürimäe, Toivo; Hamra, Jena

    2007-10-01

    The purpose of the present study was to investigate the use of electromyographic signals (EMG), to determine the EMG threshold (EMGT) in four lower extremity muscles and to compare these thresholds with the second ventilatory threshold (VT2) in subjects participating in different sports and at different performance levels. Forty-nine subjects (23.8 +/- 5.7 years, 182.7 +/- 5.3 cm, 79.1 +/- 8.6 kg) including eleven cyclists, ten team-handball players, nine kayakers, eight power lifters and eleven controls were investigated utilizing a cycle ergometer. Respiratory gas exchange measures were collected and EMG activity was continuously recorded from four muscles (vastus lateralis, vastus medialis, biceps femoris and gastrocnemius lateralis). The VO(2)max averaged 56.1 +/- 11.1 ml kg(-1) min(-1), the average aerobic power was 348.5 +/- 61.0 W and the corresponding VT2 occurred at 271.4 +/- 64.0 W. The EMGT ranged from 80 to 98% of power output for the different muscles. The VT2 and EMG thresholds from four different muscles were not different. When thresholds were analyzed among different groups of subjects, no significant difference was observed between VT2 and EMGT despite threshold differences between the groups. All four EMGT were significantly related to maximal aerobic power (r = 0.73-0.83) and were highly correlated to each other (r = 0.57-0.88). In conclusion, EMGT can be used to determine the VT2 for individuals independent of sport specificity or performance level. PMID:17624542

  13. 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.

  14. Surface EMG Recording of the Perioral Reflexes: Preliminary Observations on Stutterers and Nonstutterers.

    ERIC Educational Resources Information Center

    McClean, Michael D.

    1987-01-01

    Surface electrodes were used to describe the perioral reflexes in seven stutterers and five nonstutterers and electromyographic (EMG) recordings were obtained at electrode sites associated with the orbicularis oris inferior muscle and the depressor labia inferior muscle. A difference was noted in the pattern of reflex response between the two…

  15. 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.

  16. Between-day reliability of triceps surae responses to standing perturbations in people post-stroke and healthy controls: A high-density surface EMG investigation.

    PubMed

    Gallina, A; Pollock, C L; Vieira, T M; Ivanova, T D; Garland, S J

    2016-02-01

    The reliability of triceps surae electromyographic responses to standing perturbations in people after stroke and healthy controls is unknown. High-Density surface Electromyography (HDsEMG) is a technique that records electromyographic signals from different locations over a muscle, overcoming limitations of traditional surface EMG such as between-day differences in electrode placement. In this study, HDsEMG was used to measure responses from soleus (SOL, 18 channels) and medial and lateral gastrocnemius (MG and LG, 16 channels each) in 10 people after stroke and 10 controls. Timing and amplitude of the response were estimated for each channel of the grids. Intraclass Correlation Coefficient (ICC) and normalized Standard Error of Measurement (SEM%) were calculated for each channel individually (single-channel configuration) and on the median of each grid (all-channels configuration). Both timing (single-channel: ICC=0.75-0.96, SEM%=5.0-9.1; all-channels: ICC=0.85-0.97; SEM%=3.5-6.2%) and amplitude (single-channel: ICC=0.60-0.91, SEM%=25.1-46.6; ICC=0.73-0.95, SEM%=19.3-42.1) showed good-to-excellent reliability. HDsEMG provides reliable estimates of EMG responses to perturbations both in individuals after stroke and in healthy controls; reliability was marginally better for the all-channels compared to the single-channel configuration. PMID:27004641

  17. The effect of single-pulse transcranial magnetic stimulation and peripheral nerve stimulation on complexity of EMG signal: fractal analysis.

    PubMed

    Cukic, M; Oommen, J; Mutavdzic, D; Jorgovanovic, N; Ljubisavljevic, M

    2013-07-01

    The aim of this study was to examine whether single-pulse transcranial magnetic stimulation (spTMS) affects the pattern of corticospinal activity once voluntary drive has been restored after spTMS-induced EMG silence. We used fractal dimension (FD) to explore the 'complexity' of the electromyography (EMG) signal, and median frequency of the spectra (MDF) to examine changes in EMG spectral characteristics. FD and MDF of the raw EMG epochs immediately before were compared with those obtained from epochs after the EMG silence. Changes in FD and MDF after spTMS were examined with three levels of muscle contraction corresponding to weak (20-40%), moderate (40-60%) and strong (60-80% of maximal voluntary contraction) and three intensities of stimulation set at 10, 20 and 30% above the resting motor threshold. FD was calculated using the Higuchi fractal dimension algorithm. Finally, to discern the origin of FD changes between the CNS and muscle, we compared the effects of spTMS with the effects of peripheral nerve stimulation (PNS) on FD and MDF. The results show that spTMS induced significant decrease in both FD and MDF of EMG signal after stimulation. PNS did not have any significant effects on FD nor MDF. Changes in TMS intensity did not have any significant effect on FD or MDF after stimulation nor had the strength of muscle contraction. However, increase in contraction strength decreased FD before stimulation but only between weak and moderate contraction. The results suggest that the effects of spTMS on corticospinal activity, underlying voluntary motor output, outlast the TMS stimulus. It appears that the complexity of the EMG signal is reduced after spTMS, suggesting that TMS alters the dynamics of the ongoing corticospinal activity most likely temporarily synchronizing the neural network activity. Further studies are needed to confirm whether observed changes after TMS occur at the cortical level. PMID:23652725

  18. Rectification is required to extract oscillatory envelope modulation from surface electromyographic signals.

    PubMed

    Dakin, Christopher J; Dalton, Brian H; Luu, Billy L; Blouin, Jean-Sébastien

    2014-10-01

    Rectification of surface electromyographic (EMG) recordings prior to their correlation with other signals is a widely used form of preprocessing. Recently this practice has come into question, elevating the subject of EMG rectification to a topic of much debate. Proponents for rectifying suggest it accentuates the EMG spike timing information, whereas opponents indicate it is unnecessary and its nonlinear distortion of data is potentially destructive. Here we examine the necessity of rectification on the extraction of muscle responses, but for the first time using a known oscillatory input to the muscle in the form of electrical vestibular stimulation. Participants were exposed to sinusoidal vestibular stimuli while surface and intramuscular EMG were recorded from the left medial gastrocnemius. We compared the unrectified and rectified surface EMG to single motor units to determine which method best identified stimulus-EMG coherence and phase at the single-motor unit level. Surface EMG modulation at the stimulus frequency was obvious in the unrectified surface EMG. However, this modulation was not identified by the fast Fourier transform, and therefore stimulus coherence with the unrectified EMG signal failed to capture this covariance. Both the rectified surface EMG and single motor units displayed significant coherence over the entire stimulus bandwidth (1-20 Hz). Furthermore, the stimulus-phase relationship for the rectified EMG and motor units shared a moderate correlation (r = 0.56). These data indicate that rectification of surface EMG is a necessary step to extract EMG envelope modulation due to motor unit entrainment to a known stimulus. PMID:24990563

  19. EMG responses to maintain stance during multidirectional surface translations

    NASA Technical Reports Server (NTRS)

    Henry, S. M.; Fung, J.; Horak, F. B.; Peterson, B. W. (Principal Investigator)

    1998-01-01

    To characterize muscle synergy organization underlying multidirectional control of stance posture, electromyographic activity was recorded from 11 lower limb and trunk muscles of 7 healthy subjects while they were subjected to horizontal surface translations in 12 different, randomly presented directions. The latency and amplitude of muscle responses were quantified for each perturbation direction. Tuning curves for each muscle were examined to relate the amplitude of the muscle response to the direction of surface translation. The latencies of responses for the shank and thigh muscles were constant, regardless of perturbation direction. In contrast, the latencies for another thigh [tensor fascia latae (TFL)] and two trunk muscles [rectus abdominis (RAB) and erector spinae (ESP)] were either early or late, depending on the perturbation direction. These three muscles with direction-specific latencies may play different roles in postural control as prime movers or as stabilizers for different translation directions, depending on the timing of recruitment. Most muscle tuning curves were within one quadrant, having one direction of maximal activity, generally in response to diagonal surface translations. Two trunk muscles (RAB and ESP) and two lower limb muscles (semimembranosus and peroneus longus) had bipolar tuning curves, with two different directions of maximal activity, suggesting that these muscle can play different roles as part of different synergies, depending on translation direction. Muscle tuning curves tended to group into one of three regions in response to 12 different directions of perturbations. Two muscles [rectus femoris (RFM) and TFL] were maximally active in response to lateral surface translations. The remaining muscles clustered into one of two diagonal regions. The diagonal regions corresponded to the two primary directions of active horizontal force vector responses. Two muscles (RFM and adductor longus) were maximally active orthogonal to

  20. Complexity analysis of EMG signals for patients after stroke during robot-aided rehabilitation training using fuzzy approximate entropy.

    PubMed

    Sun, Rui; Song, Rong; Tong, Kai-yu

    2014-09-01

    The paper presents a novel viewpoint to monitor the motor function improvement during a robot-aided rehabilitation training. Eight chronic poststroke subjects were recruited to attend the 20-session training, and in each session, subjects were asked to perform voluntary movements of elbow flexion and extension together with the robotic system. The robotic system was continuously controlled by the electromyographic (EMG) signal from the affected triceps. Fuzzy approximate entropy (fApEn) was applied to investigate the complexity of the EMG segment, and maximum voluntary contraction (MVC) during elbow flexion and extension was applied to reflect force generating capacity of the affected muscles. The results showed that the group mean fApEn of EMG signals from triceps and biceps increased significantly after the robot-aided rehabilitation training . There was also significant increase in maximum voluntary flexion and extension torques after the robot-aided rehabilitation training . There was significant correlation between fApEn of agonist and MVC , which implied that the increase of motorneuron number is one of factors that may explain the increase in muscle strength. These findings based on fApEn of the EMG signals expand the existing interpretation of training-induced function improvement in patients after stroke, and help us to understand the neurological change induced by the robot-aided rehabilitation training. PMID:24240006

  1. Robust decomposition of single-channel intramuscular EMG signals at low force levels

    NASA Astrophysics Data System (ADS)

    Marateb, Hamid R.; Muceli, Silvia; McGill, Kevin C.; Merletti, Roberto; Farina, Dario

    2011-10-01

    This paper presents a density-based method to automatically decompose single-channel intramuscular electromyogram (EMG) signals into their component motor unit action potential (MUAP) trains. In contrast to most previous decomposition methods, which require pre-setting and (or) tuning of multiple parameters, the proposed method takes advantage of the data-dependent strategies in the pattern recognition procedures. In this method, outliers (superpositions) are excluded prior to classification and MUAP templates are identified by an adaptive density-based clustering procedure. MUAP trains are then identified by a novel density-based classifier that incorporates MUAP shape and discharge time information. MUAP trains are merged by a fuzzy system that incorporates expert human knowledge. Finally, superimpositions are resolved to fill the gaps in the MUAP trains. The proposed decomposition algorithm has been experimentally tested on signals from low-force (<=30% maximal) isometric contractions of the vastus medialis obliquus, vastus lateralis, biceps femoris long-head and tibialis anterior muscles. Comparison with expert manual decomposition that had been verified using a rigorous statistical analysis showed that the algorithm identified 80% of the total 229 motor unit trains with an accuracy greater than 90%. The algorithm is robust and accurate, and therefore it is a promising new tool for decomposing single-channel multi-unit signals.

  2. Spatial correlation of high density EMG signals provides features robust to electrode number and shift in pattern recognition for myocontrol.

    PubMed

    Stango, Antonietta; Negro, Francesco; Farina, Dario

    2015-03-01

    Research on pattern recognition for myoelectric control has usually focused on a small number of electromyography (EMG) channels because of better clinical acceptability and low computational load with respect to multi-channel EMG. However, recently, high density (HD) EMG technology has substantially improved, also in practical usability, and can thus be applied in myocontrol. HD EMG provides several closely spaced recordings in multiple locations over the skin surface. This study considered the use of HD EMG for controlling upper limb prostheses, based on pattern recognition. In general, robustness and reliability of classical pattern recognition systems are influenced by electrode shift in dons and doff, and by the presence of malfunctioning channels. The aim of this study is to propose a new approach to attenuate these issues. The HD EMG grid of electrodes is an ensemble of sensors that records data spatially correlated. The experimental variogram, which is a measure of the degree of spatial correlation, was used as feature for classification, contrary to previous approaches that are based on temporal or frequency features. The classification based on the variogram was tested on seven able-bodied subjects and one subject with amputation, for the classification of nine and seven classes, respectively. The performance of the proposed approach was comparable with the classic methods based on time-domain and autoregressive features (average classification accuracy over all methods ∼ 95% for nine classes). However, the new spatial features demonstrated lower sensitivity to electrode shift ( ± 1 cm) with respect to the classic features . When even just one channel was noisy, the classification accuracy dropped by ∼ 10% for all methods. However, the new method could be applied without any retraining to a subset of high-quality channels whereas the classic methods require retraining when some channels are omitted. In conclusion, the new spatial feature space

  3. 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

  4. 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.

  5. Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders.

    PubMed

    Istenic, Rok; Kaplanis, Prodromos A; Pattichis, Constantinos S; Zazula, Damjan

    2010-08-01

    We introduce a novel method for an automatic classification of subjects to those with or without neuromuscular disorders. This method is based on multiscale entropy of recorded surface electromyograms (sEMGs) and support vector classification. The method was evaluated on a single-channel experimental sEMGs recorded from biceps brachii muscle of nine healthy subjects, nine subjects with muscular and nine subjects with neuronal disorders, at 10%, 30%, 50%, 70% and 100% of maximal voluntary contraction force. Leave-one-out cross-validation was performed, deploying binary (healthy/patient) and three-class classification (healthy/myopathic/neuropathic). In the case of binary classification, subjects were distinguished with 81.5% accuracy (77.8% sensitivity at 83.3% specificity). At three-class classification, the accuracy decreased to 70.4% (myopathies were recognized with a sensitivity of 55.6% at specificity 88.9%, neuropathies with a sensitivity of 66.7% at specificity 83.3%). The proposed method is suitable for fast and non-invasive discrimination of healthy and neuromuscular patient groups, but it fails to recognize the type of pathology. PMID:20490940

  6. iEMG: Imaging electromyography.

    PubMed

    Urbanek, Holger; van der Smagt, Patrick

    2016-04-01

    Advanced data analysis and visualization methodologies have played an important role in making surface electromyography both a valuable diagnostic methodology of neuromuscular disorders and a robust brain-machine interface, usable as a simple interface for prosthesis control, arm movement analysis, stiffness control, gait analysis, etc. But for diagnostic purposes, as well as for interfaces where the activation of single muscles is of interest, surface EMG suffers from severe crosstalk between deep and superficial muscle activation, making the reliable detection of the source of the signal, as well as reliable quantification of deeper muscle activation, prohibitively difficult. To address these issues we present a novel approach for processing surface electromyographic data. Our approach enables the reconstruction of 3D muscular activity location, making the depth of muscular activity directly visible. This is even possible when deep muscles are overlaid with superficial muscles, such as seen in the human forearm. The method, which we call imaging EMG (iEMG), is based on using the crosstalk between a sufficiently large number of surface electromyographic electrodes to reconstruct the 3D generating electrical potential distribution within a given area. Our results are validated by in vivo measurements of iEMG and ultrasound on the human forearm. PMID:26852113

  7. Comparison of complexity of EMG signals between a normal subject and a patient after stroke--a case study.

    PubMed

    Ao, Di; Sun, Rui; Song, Rong

    2013-01-01

    An innovative method to quantitatively assess the motor function of upper extremities for post-stroke patients is proposed. A post-stroke patient and a normal subject were recruited to conduct a special performance of voluntary elbow flexion and extension by following a sinusoidal trajectory from 30° to 90° at 6 different peak angular velocities in a horizontal plane. During the test, the elbow angle and subject's electromyographic (EMG) signal (biceps brachii and triceps brachii) were recorded simultaneously. Fuzzy approximate entropy (fApEn) was applied to analyze the EMG signals. The results showed observable differences in fApEn when the control and the patient (unaffected and affected arms) were compared, and an uptrend of fApEn was detected with the increase in the tracking velocities in both the normal individual and patient (unaffected and affected arm). The fApEn values, which are a measure of complexity of EMG, could be used for the quantitative evaluation of the deficiencies of motor control induced by stroke. PMID:24110849

  8. The System Design for the Extraction and Pre-processing of Surface EMG

    NASA Astrophysics Data System (ADS)

    Sun, Baofeng; Chen, Wanzhong; Zheng, Xin

    This paper design an acquisition instrument of surface EMG (SEMG)based on a high common mode rejection ratio(CMRR) preamplifier to deal with the difficulties in capturing the SEMG which is small range,low SNR,easy to be disturbed and the high prices of present acquisition equipment.This design can restain the common mode interferrence and power frequency interferrance effectively with a low price.By making use of wireless communication module PTR2000 in connecting C8051F320 MCU to host computer for data transmission,a wireless real-time acquisition system of SEMG is constitued.The complete and accurate SEMG is obtained in the host computer,providing a reliable source for the further analysis and processing of SEMG.

  9. Predicting muscle forces in gait from EMG signals and musculotendon kinematics.

    PubMed

    White, S C; Winter, D A

    1992-01-01

    An EMG-driven muscle model for determining muscle force-time histories during gait is presented. The model, based on Hill's equation (1938), incorporates morphological data and accounts for changes in musculotendon length, velocity, and the level of muscle excitation for both concentric and eccentric contractions. Musculotendon kinematics were calculated using three-dimensional cinematography with a model of the musculoskeletal system. Muscle force-length-EMG relations were established from slow isokinetic calibrations. Walking muscle force-time histories were determined for two subjects. Joint moments calculated from the predicted muscle forces were compared with moments calculated using a linked segment, inverse dynamics approach. Moment curve correlations ranged from r = 0.72 to r = 0.97 and the root mean square (RMS) differences were from 10 to 20 Nm. Expressed as a relative RMS, the moment differences ranged from a low of 23% at the ankle to a high of 72% at the hip. No single reason for the differences between the two moment curves could be identified. Possible explanations discussed include the linear EMG-to-force assumption and how well the EMG-to-force calibration represented excitation for the whole muscle during gait, assumptions incorporated in the muscle modeling procedure, and errors inherent in validating joint moments predicted from the model to moments calculated using linked segment, inverse dynamics. The closeness with which the joint moment curves matched in the present study supports using the modeling approach proposed to determine muscle forces in gait. PMID:20719615

  10. Improvement of spatial resolution in surface-EMG: a theoretical and experimental comparison of different spatial filters.

    PubMed

    Disselhorst-Klug, C; Silny, J; Rau, G

    1997-07-01

    The conventional bipolar surface electromyography (EMG) technique detects, due to its low spatial resolution, the superimposed electromyographic activity of a large number of motor units (MU's). In superficial muscles the isolated action potentials of the most superficial MU's can be recorded noninvasively by means of surface electrodes, if the method of spatial filtering, in connection with electrode arrays, is used. Up to now, only filters with an anisotropic transfer function have been used. As the surface potential distribution generated by the excitation of the MU's contains spatial frequencies in the anisotropic range of those filters, it can be assumed that isotropic spatial filters detect the single MU activity more effectively. In the present study, different isotropic and anisotropic filters have been compared by means of theoretical field simulations and experiments in volunteers. A tripole model for an excited MU was used as the basis for simulating the spatial extension of the filter response for each of the investigated filters. The spatial extension is an indicative of the spatial resolution. For the experimental validation, the total number of single motor units was not directly investigated, but the signal-to-noise ratio (SNR) has been determined. Therefore, the potential distribution generated on the skin surface during maximum voluntary contraction has been simultaneous spatially filtered with each of the investigated filters. The simulations show that an isotropic spatial filtering procedure reduces the spatial extension of the filter response and improves the spatial resolution of the EMG-recording arrangement in comparison to anisotropic spatial filters up to 30%. In other words, the spatial selectivity of the arrangement is increased. This improvement in the filter performance is more pronounced for MU's located close to the skin surface than for MU's more distantly located. Additionally, this theoretical improvement in selectivity depends on

  11. 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.

  12. An investigative redesign of the ECG and EMG signal conditioning circuits for two-fault tolerance and circuit improvement

    NASA Astrophysics Data System (ADS)

    Obrien, Edward M.

    1991-12-01

    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.

  13. Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment.

    PubMed

    Blana, Dimitra; Kyriacou, Theocharis; Lambrecht, Joris M; Chadwick, Edward K

    2016-08-01

    Transhumeral amputation has a significant effect on a person's independence and quality of life. Myoelectric prostheses have the potential to restore upper limb function, however their use is currently limited due to lack of intuitive and natural control of multiple degrees of freedom. The goal of this study was to evaluate a novel transhumeral prosthesis controller that uses a combination of kinematic and electromyographic (EMG) signals recorded from the person's proximal humerus. Specifically, we trained a time-delayed artificial neural network to predict elbow flexion/extension and forearm pronation/supination from six proximal EMG signals, and humeral angular velocity and linear acceleration. We evaluated this scheme with ten able-bodied subjects offline, as well as in a target-reaching task presented in an immersive virtual reality environment. The offline training had a target of 4° for flexion/extension and 8° for pronation/supination, which it easily exceeded (2.7° and 5.5° respectively). During online testing, all subjects completed the target-reaching task with path efficiency of 78% and minimal overshoot (1.5%). Thus, combining kinematic and muscle activity signals from the proximal humerus can provide adequate prosthesis control, and testing in a virtual reality environment can provide meaningful data on controller performance. PMID:26190031

  14. 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. PMID:24760934

  15. 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

    2015-01-01

    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. PMID:26384112

  16. A Real-Time Pinch-to-Zoom Motion Detection by Means of a Surface EMG-Based Human-Computer Interface

    PubMed Central

    Kim, Jongin; Cho, Dongrae; Lee, Kwang Jin; Lee, Boreom

    2015-01-01

    In this paper, we propose a system for inferring the pinch-to-zoom gesture using surface EMG (Electromyography) signals in real time. Pinch-to-zoom, which is a common gesture in smart devices such as an iPhone or an Android phone, is used to control the size of images or web pages according to the distance between the thumb and index finger. To infer the finger motion, we recorded EMG signals obtained from the first dorsal interosseous muscle, which is highly related to the pinch-to-zoom gesture, and used a support vector machine for classification between four finger motion distances. The powers which are estimated by Welch's method were used as feature vectors. In order to solve the multiclass classification problem, we applied a one-versus-one strategy, since a support vector machine is basically a binary classifier. As a result, our system yields 93.38% classification accuracy averaged over six subjects. The classification accuracy was estimated using 10-fold cross validation. Through our system, we expect to not only develop practical prosthetic devices but to also construct a novel user experience (UX) for smart devices. PMID:25551482

  17. A real-time pinch-to-zoom motion detection by means of a surface EMG-based human-computer interface.

    PubMed

    Kim, Jongin; Cho, Dongrae; Lee, Kwang Jin; Lee, Boreom

    2015-01-01

    In this paper, we propose a system for inferring the pinch-to-zoom gesture using surface EMG (Electromyography) signals in real time. Pinch-to-zoom, which is a common gesture in smart devices such as an iPhone or an Android phone, is used to control the size of images or web pages according to the distance between the thumb and index finger. To infer the finger motion, we recorded EMG signals obtained from the first dorsal interosseous muscle, which is highly related to the pinch-to-zoom gesture, and used a support vector machine for classification between four finger motion distances. The powers which are estimated by Welch's method were used as feature vectors. In order to solve the multiclass classification problem, we applied a one-versus-one strategy, since a support vector machine is basically a binary classifier. As a result, our system yields 93.38% classification accuracy averaged over six subjects. The classification accuracy was estimated using 10-fold cross validation. Through our system, we expect to not only develop practical prosthetic devices but to also construct a novel user experience (UX) for smart devices. PMID:25551482

  18. COMMUNICATION: The effects of elevated body temperature on the complexity of the diaphragm EMG signals during maturation

    NASA Astrophysics Data System (ADS)

    Akkurt, David; Akay, Yasemin M.; Akay, Metin

    2009-04-01

    In this paper, we examine the effect of elevated body temperature on the complexity of the diaphragm electromyography (EMGdia), the output of the respiratory neural network--using the approximate entropy method. The diaphragm EMG, EEG, EOG as well as other physiological signals (tracheal pressure, blood pressure and respiratory volume) in chronically instrumented rats were recorded at two postnatal ages: 25-35 days age (juvenile, n = 5) and 36-44 days age (early adult, n = 6) groups during control (36-37 °C), mild elevated body temperature (38 °C) and severe elevated body temperature (39-40 °C). Three to five trials of the recordings were performed at normal body temperature before raising the animal's core temperature by 1-4 °C with an electric heating pad. At the elevated temperature, another 3-5 trials were performed. Finally, the animal was cooled to the original temperature, and trials were again repeated. Complexity values of the diaphragm EMG signal were estimated and evaluated using the approximate entropy method (ApEn) over the ten consecutive breaths. Our results suggested that the mean approximate entropy values for the juvenile age group were 1.01 ± 0.01 (standard error) during control, 0.91 ± 0.02 during mild elevated body temperature and 0.81 ± 0.02 during severe elevated body temperature. For the early adult age group, these values were 0.94 ± 0.01 during control, 0.93 ± 0.01 during mild elevated body temperature and 0.92 ± 0.01 during severe elevated body temperature. Our results show that the complexity values and the durations of the diaphragm EMG (EMGdia) were significantly decreased when the elevated body temperature was shifted from control or mild to severe body temperature (p < 0.05) for the juvenile age group. However, for the early adult age group, an increase in body temperature slightly reduced the complexity measures and the duration of the EMGdia. But, these changes were not statistically significant. These results furthermore

  19. 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.

  20. Segmentation of ECG from Surface EMG Using DWT and EMD: A Comparison Study

    NASA Astrophysics Data System (ADS)

    Shahbakhti, Mohammad; Heydari, Elnaz; Luu, Gia Thien

    2014-10-01

    The electrocardiographic (ECG) signal is a major artifact during recording the surface electromyography (SEMG). Removal of this artifact is one of the important tasks before SEMG analysis for biomedical goals. In this paper, the application of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for elimination of ECG artifact from SEMG is investigated. The focus of this research is to reach the optimized number of decomposed levels using mean power frequency (MPF) by both techniques. In order to implement the proposed methods, ten simulated and three real ECG contaminated SEMG signals have been tested. Signal-to-noise ratio (SNR) and mean square error (MSE) between the filtered and the pure signals are applied as the performance indexes of this research. The obtained results suggest both techniques could remove ECG artifact from SEMG signals fair enough, however, DWT performs much better and faster in real data.

  1. A multi-modal approach for hand motion classification using surface EMG and accelerometers.

    PubMed

    Fougner, A; Scheme, E; Chan, A D C; Englehart, K; Stavdahl, Ø

    2011-01-01

    For decades, electromyography (EMG) has been used for diagnostics, upper-limb prosthesis control, and recently even for more general human-machine interfaces. Current commercial upper limb prostheses usually have only two electrode sites due to cost and space limitations, while researchers often experiment with multiple sites. Micro-machined inertial sensors are gaining popularity in many commercial and research applications where knowledge of the postures and movements of the body is desired. In the present study, we have investigated whether accelerometers, which are relatively cheap, small, robust to noise, and easily integrated in a prosthetic socket; can reduce the need for adding more electrode sites to the prosthesis control system. This was done by adding accelerometers to a multifunction system and also to a simplified system more similar to current commercially available prosthesis controllers, and assessing the resulting changes in classification accuracy. The accelerometer does not provide information on muscle force like EMG electrodes, but the results show that it provides useful supplementary information. Specifically, if one wants to improve a two-site EMG system, one should add an accelerometer affixed to the forearm rather than a third electrode. PMID:22255277

  2. Man-Machine Interface System for Neuromuscular Training and Evaluation Based on EMG and MMG Signals

    PubMed Central

    de la Rosa, Ramon; Alonso, Alonso; Carrera, Albano; Durán, Ramon; Fernández, Patricia

    2010-01-01

    This paper presents the UVa-NTS (University of Valladolid Neuromuscular Training System), a multifunction and portable Neuromuscular Training System. The UVa-NTS is designed to analyze the voluntary control of severe neuromotor handicapped patients, their interactive response, and their adaptation to neuromuscular interface systems, such as neural prostheses or domotic applications. Thus, it is an excellent tool to evaluate the residual muscle capabilities in the handicapped. The UVa-NTS is composed of a custom signal conditioning front-end and a computer. The front-end electronics is described thoroughly as well as the overall features of the custom software implementation. The software system is composed of a set of graphical training tools and a processing core. The UVa-NTS works with two classes of neuromuscular signals: the classic myoelectric signals (MES) and, as a novelty, the myomechanic signals (MMS). In order to evaluate the performance of the processing core, a complete analysis has been done to classify its efficiency and to check that it fulfils with the real-time constraints. Tests were performed both with healthy and selected impaired subjects. The adaptation was achieved rapidly, applying a predefined protocol for the UVa-NTS set of training tools. Fine voluntary control was demonstrated to be reached with the myoelectric signals. And the UVa-NTS demonstrated to provide a satisfactory voluntary control when applying the myomechanic signals. PMID:22163515

  3. A discriminant bispectrum feature for surface electromyogram signal classification.

    PubMed

    Chen, Xinpu; Zhu, Xiangyang; Zhang, Dingguo

    2010-03-01

    This paper presents a discriminant bispectrum (DBS) feature extraction approach to surface electromyogram (sEMG) signal classification for prosthetic control. The proposed feature extraction method involves two steps: (1) the bispectrum matrix integration, and (2) the Fisher linear discriminant (FLD) projection. We compare DBS with other conventional features, such as autoregressive coefficients, root mean square, power spectral distribution and time domain statistics. First, the separability of the features is investigated by the visualization of feature distribution in the FLD subspace and quantitative measurement (Davies-Boulder clustering index). Then four linear and non-linear classifiers are used to evaluate the discriminative powers of the features in terms of classification accuracy (CA). The experimental results show that DBS has better performance than other features for identifying the motion patterns of sEMG signals, and the best CA result of DBS is 99.4%. PMID:19955011

  4. Power spectrum of the rectified EMG: when and why is rectification beneficial for identifying neural connectivity?

    NASA Astrophysics Data System (ADS)

    Negro, Francesco; Keenan, Kevin; Farina, Dario

    2015-06-01

    Objective. The identification of common oscillatory inputs to motor neurons in the electromyographic (EMG) signal power spectrum is often preceded by EMG rectification for enhancing the low-frequency oscillatory components. However, rectification is a nonlinear operator and its influence on the EMG signal spectrum is not fully understood. In this study, we aim at determining when EMG rectification is beneficial in the study of oscillatory inputs to motor neurons. Approach. We provide a full mathematical description of the power spectrum of the rectified EMG signal and the influence of the average shape of the motor unit action potentials on it. We also provide a validation of these theoretical results with both simulated and experimental EMG signals. Main results. Simulations using an advanced computational model and experimental results demonstrated the accuracy of the theoretical derivations on the effect of rectification on the EMG spectrum. These derivations proved that rectification is beneficial when assessing the strength of low-frequency (delta and alpha bands) common synaptic inputs to the motor neurons, when the duration of the action potentials is short, and when the level of cancellation is relatively low. On the other hand, rectification may distort the estimation of common synaptic inputs when studying higher frequencies (beta and gamma), in a way dependent on the duration of the action potentials, and may introduce peaks in the coherence function that do not correspond to physiological shared inputs. Significance. This study clarifies the conditions when rectifying the surface EMG is appropriate for studying neural connectivity.

  5. 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…

  6. Surface EMG during the Push-up plus Exercise on a Stable Support or Swiss Ball: Scapular Stabilizer Muscle Exercise

    PubMed Central

    Seo, Sung-Hwa; Jeon, In-Ho; Cho, Yong-Ho; Lee, Hyun-Gi; Hwang, Yoon-Tae; Jang, Jee-Hun

    2013-01-01

    [Purpose] Scapular stabilizer strengthening exercise is crucial for shoulder rehabilitation. The purpose of this study was to compare two types of push-up plus exercises, on a stable and unstable bases of support, using surface electromyography (EMG), to suggest an effective shoulder rehabilitation program. [Subjects and Methods] Ten healthy men volunteered for this study. All volunteers performed two sets of push-up plus exercise (standard push up and knee push up) on stable and unstable bases of support. The muscle activities of five important scapular stabilizer muscles (upper trapezius, middle trapezius, lower trapezius, serratus anterior, latissimus dorsi) were recorded during the exercise. [Results] The upper trapezius showed greater mean electric activation amplitude in the scapular retraction posture than in the scapular protraction posture, and the serratus anterior showed greater mean electric activation amplitude in the scapular protraction posture than in the scapular retraction posture. The root-mean-square normalized EMG values of the muscles were greater during the exercise performed on the unstable support than those on the stable support. [Conclusion] The standard push-up plus exercise on an unstable base of support helps to increase muscle activity, especially those of the upper/middle trapezius and serratus anterior. PMID:24259864

  7. Modeling of surface myoelectric signals--Part I: Model implementation.

    PubMed

    Merletti, R; Lo Conte, L; Avignone, E; Guglielminotti, P

    1999-07-01

    The relationships between the parameters of active motor units (MU's) and the features of surface electromyography (EMG) signals have been investigated using a mathematical model that represents the surface EMG as a summation of contributions from the single muscle fibers. Each MU has parallel fibers uniformly scattered within a cylindrical volume of specified radius embedded in an anisotropic medium. Two action potentials, each modeled as a current tripole, are generated at the neuromuscular junction, propagate in opposite directions and extinguish at the fiber-tendon endings. The neuromuscular junctions and fiber-tendon endings are uniformly scattered within regions of specified width. Muscle fiber conduction velocity and average fiber length to the right and left of the center of the innervation zone are also specified. The signal produced by MU's with different geometries and conduction velocities are superimposed. Monopolar, single differential and double differential signals are computed from electrodes placed in equally spaced locations on the surface of the muscle and are displayed as functions of any of the model's parameters. Spectral and amplitude variables and conduction velocity are estimated from the surface signals and displayed as functions of any of the model's parameters. The influence of fiber-end effects, electrode misalignment, tissue anisotropy, MU's location and geometry are discussed. Part II of this paper will focus on the simulation and interpretation of experimental signals. PMID:10396899

  8. 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

  9. From cell to movement: to what answers does EMG really contribute?

    PubMed

    Rau, G; Schulte, E; Disselhorst-Klug, C

    2004-10-01

    This paper aims to address some of the possibilities and limitations of EMG technologies available to date. Considerable progress has been achieved in this field during the last 30 years and EMG signals can be easily obtained on different levels beginning at the cell membrane and ending with the global EMG associated with the movement itself. Different aspects from cell to movement have been considered in this paper. Highly selective needle EMG for the detection of the processes at the membrane is discussed as well as high spatial resolution EMG which gives non-invasive access to the acquisition of the single motor unit activity. On the highest level of muscles, an expert system is introduced as a novel approach to support the interpretation of muscular co-ordination as detected by conventional surface EMG. While there is a high potential in the newly developed EMG methodologies, it is a big challenge to utilize these methodologies in order to obtain detailed, repeatable, reliable--and meaningful--results. However, the risk of over- and misinterpretation has to be carefully considered. In this paper, this risk is exemplified in situations dealing with muscle fatigue, conduction velocity and cross-talk. Despite all the new possibilities available, the authors recommend that EMG with its inherent strengths and limitations should still be diligently, but carefully, used. PMID:15301779

  10. Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses

    PubMed Central

    2011-01-01

    Background For high usability, myo-controlled devices require robust classification schemes during dynamic contractions. Therefore, this study investigates the impact of the training data set in the performance of several pattern recognition algorithms during dynamic contractions. Methods A 9 class experiment was designed involving both static and dynamic situations. The performance of various feature extraction methods and classifiers was evaluated in terms of classification accuracy. Results It is shown that, combined with a threshold to detect the onset of the contraction, current pattern recognition algorithms used on static conditions provide relatively high classification accuracy also on dynamic situations. Moreover, the performance of the pattern recognition algorithms tested significantly improved by optimizing the choice of the training set. Finally, the results also showed that rather simple approaches for classification of time domain features provide results comparable to more complex classification methods of wavelet features. Conclusions Non-stationary surface EMG signals recorded during dynamic contractions can be accurately classified for the control of multi-function prostheses. PMID:21554700

  11. Differential effects of type of keyboard playing task and tempo on surface EMG amplitudes of forearm muscles.

    PubMed

    Chong, Hyun Ju; Kim, Soo Ji; Yoo, Ga Eul

    2015-01-01

    Despite increasing interest in keyboard playing as a strategy for repetitive finger exercises in fine motor skill development and hand rehabilitation, comparative analysis of task-specific finger movements relevant to keyboard playing has been less extensive. This study examined, whether there were differences in surface EMG activity levels of forearm muscles associated with different keyboard playing tasks. Results demonstrated higher muscle activity with sequential keyboard playing in a random pattern compared to individuated playing or sequential playing in a successive pattern. Also, the speed of finger movements was found as a factor that affect muscle activity levels, demonstrating that faster tempo elicited significantly greater muscle activity than self-paced tempo. The results inform our understanding of the type of finger movements involved in different types of keyboard playing at different tempi. This helps to consider the efficacy and fatigue level of keyboard playing tasks when being used as an intervention for amateur pianists or individuals with impaired fine motor skills. PMID:26388798

  12. 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

  13. 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

  14. Nonnegative matrix factorization for the identification of EMG finger movements: evaluation using matrix analysis.

    PubMed

    Naik, Ganesh R; Nguyen, Hung T

    2015-03-01

    Surface electromyography (sEMG) is widely used in evaluating the functional status of the hand to assist in hand gesture recognition, prosthetics and rehabilitation applications. The sEMG is a noninvasive, easy to record signal of superficial muscles from the skin surface. Considering the nonstationary characteristics of sEMG, recent feature selection of hand gesture recognition using sEMG signals necessitate designers to use nonnegative matrix factorization (NMF)-based methods. This method exploits both the additive and sparse nature of signals by extracting accurate and reliable measurements of sEMG features using a minimum number of sensors. The testing has been conducted for simple and complex finger flexions using several experiments with artificial neural network classification scheme. It is shown, both by simulation and experimental studies, that the proposed algorithm is able to classify ten finger flexions (five simple and five complex finger flexions) recorded from two sEMG sensors up to 92% (95% for simple and 87% for complex flexions) accuracy. The recognition performances of simple and complex finger flexions are also validated with NMF permutation matrix analysis. PMID:25486650

  15. Effect of interelectrode distance on surface electromyographic signals of vastus intermedius muscle in women and men.

    PubMed

    Tomita, Aya; Ando, Ryosuke; Saito, Akira; Watanabe, Kohei; Akima, Hiroshi

    2015-12-01

    We previously developed a novel technique to record surface electromyography (EMG) of the vastus intermedius (VI) in men. The purpose of the present study was to assess whether this technique can be applied to women in the same way. We measured the subcutaneous fat thickness at the site of electrode placement on VI using ultrasonography. Nine men and ten women performed isometric knee extensions at 25%, 50%, 75%, and 100% of the maximal voluntary contraction. During the tasks, surface EMG signals were recorded from the superficial region of VI with interelectrode distances (IEDs) of 10 mm (IED-10) and 20 mm (IED-20). The subcutaneous fat thickness in women was significantly greater than in men (women: 8.7 ± 2.1 mm; men: 5.6 ± 1.6 mm, p < 0.01). However, the amplitude and frequency of the EMG signal of VI at the different force levels were not affected by IEDs in either sex. These results suggest that surface EMG recording of VI with both IED-10 and IED-20 would be applicable to relatively lean women with a similar sensitivity to that in men. PMID:26603356

  16. 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. PMID:17978424

  17. Computed myography: three-dimensional reconstruction of motor functions from surface EMG data

    NASA Astrophysics Data System (ADS)

    van den Doel, Kees; Ascher, Uri M.; Pai, Dinesh K.

    2008-12-01

    We describe a methodology called computed myography to qualitatively and quantitatively determine the activation level of individual muscles by voltage measurements from an array of voltage sensors on the skin surface. A finite element model for electrostatics simulation is constructed from morphometric data. For the inverse problem, we utilize a generalized Tikhonov regularization. This imposes smoothness on the reconstructed sources inside the muscles and suppresses sources outside the muscles using a penalty term. Results from experiments with simulated and human data are presented for activation reconstructions of three muscles in the upper arm (biceps brachii, bracialis and triceps). This approach potentially offers a new clinical tool to sensitively assess muscle function in patients suffering from neurological disorders (e.g., spinal cord injury), and could more accurately guide advances in the evaluation of specific rehabilitation training regimens.

  18. Analysis of surface EMG activation in hand percussion playing depending on the grasping type and the tempo

    PubMed Central

    Chong, Hyun Ju; Kim, Soo Ji; Lee, Eun Kyoung; Yoo, Ga Eul

    2015-01-01

    Although instrument playing-based training has been repeatedly reported to improve functional hand movements including grasping, the attempts to present quantitative information on physiological mechanism of grasping have been relatively insufficient to determine the type and the intensity of the exercises involved. This study aimed to examine the muscle activation during hand percussion playing depending on the grasping type and the playing tempo. A total of twelve healthy older adults with a mean age of 71.5 years participated in this study. Surface electrodes were placed on three grasping-related muscles: Flexor digitorum superficialis, extensor digitorum, and flexor pollicis brevis. Participants were instructed to play with the egg shaker, paddle drum mallet and clave involving different types of grasp at three different tempi (i.e., 80, 100, and 120 bpm) and sEMG data were collected during each playing. Significantly greater muscle activation was generated with the small sphere type of egg shaker, compared to the handle type of paddle drum mallet and the small cylinder type of clave. Playing at faster tempo also elicited significantly greater muscle activation than at slower tempo. With regard to the rise time of muscle activation, while tempo significantly affected the rise time, the time to peak muscle did not significantly change depending on the grasping type. This study confirmed that grasping pattern and the tempo of movement significantly influence the muscular activation of grasping involved in instrument playing. Based on these results, clinical implication for instrument selection and structured instrument playing would be suggested. PMID:26331139

  19. Analysis of surface EMG activation in hand percussion playing depending on the grasping type and the tempo.

    PubMed

    Chong, Hyun Ju; Kim, Soo Ji; Lee, Eun Kyoung; Yoo, Ga Eul

    2015-08-01

    Although instrument playing-based training has been repeatedly reported to improve functional hand movements including grasping, the attempts to present quantitative information on physiological mechanism of grasping have been relatively insufficient to determine the type and the intensity of the exercises involved. This study aimed to examine the muscle activation during hand percussion playing depending on the grasping type and the playing tempo. A total of twelve healthy older adults with a mean age of 71.5 years participated in this study. Surface electrodes were placed on three grasping-related muscles: Flexor digitorum superficialis, extensor digitorum, and flexor pollicis brevis. Participants were instructed to play with the egg shaker, paddle drum mallet and clave involving different types of grasp at three different tempi (i.e., 80, 100, and 120 bpm) and sEMG data were collected during each playing. Significantly greater muscle activation was generated with the small sphere type of egg shaker, compared to the handle type of paddle drum mallet and the small cylinder type of clave. Playing at faster tempo also elicited significantly greater muscle activation than at slower tempo. With regard to the rise time of muscle activation, while tempo significantly affected the rise time, the time to peak muscle did not significantly change depending on the grasping type. This study confirmed that grasping pattern and the tempo of movement significantly influence the muscular activation of grasping involved in instrument playing. Based on these results, clinical implication for instrument selection and structured instrument playing would be suggested. PMID:26331139

  20. 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

  1. Optimal tracking of a sEMG based force model for a prosthetic hand.

    PubMed

    Potluri, Chandrasekhar; Anugolu, Madhavi; Yihun, Yimesker; Jensen, Alex; Chiu, Steve; Schoen, Marco P; Naidu, D Subbaram

    2011-01-01

    This paper presents a surface electromyographic (sEMG)-based, optimal control strategy for a prosthetic hand. System Identification (SI) is used to obtain the dynamic relation between the sEMG and the corresponding skeletal muscle force. The input sEMG signal is preprocessed using a Half-Gaussian filter and fed to a fusion-based Multiple Input Single Output (MISO) skeletal muscle force model. This MISO system model provides the estimated finger forces to be produced as input to the prosthetic hand. Optimal tracking method has been applied to track the estimated force profile of the Fusion based sEMG-force model. The simulation results show good agreement between reference force profile and the actual force. PMID:22254629

  2. 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%. PMID:27418924

  3. 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

  4. EMG feature assessment for myoelectric pattern recognition and channel selection: a study with incomplete spinal cord injury.

    PubMed

    Liu, Jie; Li, Xiaoyan; Li, Guanglin; Zhou, Ping

    2014-07-01

    Myoelectric pattern recognition with a large number of electromyogram (EMG) channels provides an approach to assessing motor control information available from the recorded muscles. In order to develop a practical myoelectric control system, a feature dependent channel reduction method was developed in this study to determine a small number of EMG channels for myoelectric pattern recognition analysis. The method selects appropriate raw EMG features for classification of different movements, using the minimum Redundancy Maximum Relevance (mRMR) and the Markov random field (MRF) methods to rank a large number of EMG features, respectively. A k-nearest neighbor (KNN) classifier was used to evaluate the performance of the selected features in terms of classification accuracy. The method was tested using 57 channels' surface EMG signals recorded from forearm and hand muscles of individuals with incomplete spinal cord injury (SCI). Our results demonstrate that appropriate selection of a small number of raw EMG features from different recording channels resulted in similar high classification accuracies as achieved by using all the EMG channels or features. Compared with the conventional sequential forward selection (SFS) method, the feature dependent method does not require repeated classifier implementation. It can effectively reduce redundant information not only cross different channels, but also cross different features in the same channel. Such hybrid feature-channel selection from a large number of EMG recording channels can reduce computational cost for implementation of a myoelectric pattern recognition based control system. PMID:24844608

  5. Designing a Low-noise, High-resolution, and Portable Four Channel Acquisition System for Recording Surface Electromyographic Signal

    PubMed Central

    Pashaei, Akbar; Yazdchi, Mohammad Reza; Marateb, Hamid Reza

    2015-01-01

    In current years, the application of biopotential signals has received a lot of attention in literature. One of these signals is an electromyogram (EMG) generated by active muscles. Surface EMG (sEMG) signal is recorded over the skin, as the representative of the muscle activity. Since its amplitude can be as low as 50 μV, it is sensitive to undesirable noise signals such as power-line interferences. This study aims at designing a battery-powered portable four-channel sEMG signal acquisition system. The performance of the proposed system was assessed in terms of the input voltage and current noise, noise distribution, synchronization and input noise level among different channels. The results indicated that the designed system had several inbuilt operational merits such as low referred to input noise (lower than 0.56 μV between 8 Hz and 1000 Hz), considerable elimination of power-line interference and satisfactory recorded signal quality in terms of signal-to-noise ratio. The muscle conduction velocity was also estimated using the proposed system on the brachial biceps muscle during isometric contraction. The estimated values were in then normal ranges. In addition, the system included a modular configuration to increase the number of recording channels up to 96. PMID:26951952

  6. A note on the probability distribution function of the surface electromyogram signal☆

    PubMed Central

    Nazarpour, Kianoush; Al-Timemy, Ali H.; Bugmann, Guido; Jackson, Andrew

    2013-01-01

    The probability density function (PDF) of the surface electromyogram (EMG) signals has been modelled with Gaussian and Laplacian distribution functions. However, a general consensus upon the PDF of the EMG signals is yet to be reached, because not only are there several biological factors that can influence this distribution function, but also different analysis techniques can lead to contradicting results. Here, we recorded the EMG signal at different isometric muscle contraction levels and characterised the probability distribution of the surface EMG signal with two statistical measures: bicoherence and kurtosis. Bicoherence analysis did not help to infer the PDF of measured EMG signals. In contrast, with kurtosis analysis we demonstrated that the EMG PDF at isometric, non-fatiguing, low contraction levels is super-Gaussian. Moreover, kurtosis analysis showed that as the contraction force increases the surface EMG PDF tends to a Gaussian distribution. PMID:23047056

  7. 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

    2014-01-01

    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

  8. 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

  9. EMG activity of the serratus anterior and trapezius muscles during the different phases of the push-up plus exercise on different support surfaces and different hand positions

    PubMed Central

    Gioftsos, George; Arvanitidis, Michail; Tsimouris, Dimitrios; Kanellopoulos, Assimakis; Paras, George; Trigkas, Panagiotis; Sakellari, Vasiliki

    2016-01-01

    [Purpose] The appropriate exercise prescription is crucial for achieving scapular stability and providing successful rehabilitation, and the Push-up Plus (PuP) exercise has an important role in shoulder rehabilitation. Consequently, this study examined the effect of support surface stability, hand positioning, and phase of exercise, on the trapezius and serratus anterior muscle contractions as well as on the EMG ratio of the upper/lower trapezius. [Subjects and Methods] Thirteen healthy male volunteers participated in this study. The subjects performed the PuP exercise on stable and unstable supporting surfaces with three different hand orientations. During the PuP exercise, the muscle activities of the upper (UT) and lower (LT) trapezius, as well as the serratus anterior (SA) were measured and expressed as percentages of maximum voluntary isometric contraction (%MVIC). [Results] The EMG activities of UT and LT were statistically greater during the push-up phase compared to the plus phase of the exercise. The contrary was recorded for the activity of the SA. SA was affected by the support surface as well as by the hand positioning. [Conclusion] The results suggest that different phases of the PuP exercise require different muscle stability actions with corresponding activations of appropriate muscle fibers. A detailed prescription of the required phase of the exercise can more effectively activate the scapula-thoracic musculature. PMID:27512278

  10. The detection of long-range correlations of operation force and sEMG with multifractal detrended fluctuation analysis.

    PubMed

    Li, Fan; Li, Dongxu; Wang, Chunhui; Chen, Shanguang; Lv, Ming; Wang, Miao

    2015-01-01

    This paper explores the application of multifractal detrended fluctuation analysis (MF-DFA) on the nonlinear characteristics of correlation between operation force and surface electromyography (sEMG), which is an applied frontier of human neuromuscular system activity. We established cross-correlation functions between the signal of force and four typical sEMG time-frequency domain index sequences (force-sEMG cross-correlation sequences), and dealt with the sequences with MF-DFA. In addition, we demonstrated that the force-sEMG cross-correlation sequences have strong statistical self-similarity and the fractal characteristic of the signal spectrum is similar to 1/f noise or fractional Brownian motion. PMID:26405873

  11. 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.

  12. Spatio-spectral filters for low-density surface electromyographic signal classification.

    PubMed

    Huang, Gan; Zhang, Zhiguo; Zhang, Dingguo; Zhu, Xiangyang

    2013-05-01

    In this paper, we proposed to utilize a novel spatio-spectral filter, common spatio-spectral pattern (CSSP), to improve the classification accuracy in identifying intended motions based on low-density surface electromyography (EMG). Five able-bodied subjects and a transradial amputee participated in an experiment of eight-task wrist and hand motion recognition. Low-density (six channels) surface EMG signals were collected on forearms. Since surface EMG signals are contaminated by large amount of noises from various sources, the performance of the conventional time-domain feature extraction method is limited. The CSSP method is a classification-oriented optimal spatio-spectral filter, which is capable of separating discriminative information from noise and, thus, leads to better classification accuracy. The substantially improved classification accuracy of the CSSP method over the time-domain and other methods is observed in all five able-bodied subjects and verified via the cross-validation. The CSSP method can also achieve better classification accuracy in the amputee, which shows its potential use for functional prosthetic control. PMID:23385330

  13. Estimation of Optimal Measurement Position of Human Forearm EMG Signal by Discriminant Analysis Based on Wilks' lambda

    NASA Astrophysics Data System (ADS)

    Kiso, Atsushi; Taniguchi, Yu; Seki, Hirokazu

    This paper describes the estimation of the optimal measurement position by discriminant analysis based on Wilks' lambda for myoelectric hand control. In previous studies, for motion discrimination, the myoelectric signals were measured at the same positions. However, the optimal measurement positions of the myoelectric signals for motion discrimination differ depending on the remaining muscles of amputees. Therefore, the purpose of this study is to estimate the optimal and fewer measurement positions for precise motion discrimination of a human forearm. This study proposes a method for estimating the optimal measurement positions by discriminant analysis based on Wilks' lambda, using the myoelectric signals measured at multiple positions. The results of some experiments on the myoelectric hand simulator show the effectiveness of the proposed optimal measurement position estimation method.

  14. EMG (Electromyography) (For Parents)

    MedlinePlus

    ... conditions that might be causing muscle weakness, including muscular dystrophy and nerve disorders. How Is an EMG Done? ... contraction: diseases of the muscle itself (most commonly, muscular dystrophy in children) diseases of the neuromuscular junction , which ...

  15. Nicotine and elevated body temperature reduce the complexity of the genioglossus and diaphragm EMG signals in rats during early maturation

    NASA Astrophysics Data System (ADS)

    Akkurt, David; Akay, Yasemin M.; Akay, Metin

    2009-10-01

    In this paper, we examined the effect of nicotine exposure and increased body temperature on the complexity (dynamics) of the genioglossus muscle (EMGg) and the diaphragm muscle (EMGdia) to explore the effects of nicotine and hyperthermia. Nonlinear dynamical analysis of the EMGdia and EMGg signals was performed using the approximate entropy method on 15 (7 saline- and 8 nicotine-treated) juvenile rats (P25-P35) and 19 (11 saline- and 8 nicotine-treated) young adult rats (P36-P44). The mean complexity values were calculated over the ten consecutive breaths using the approximate entropy method during mild elevated body temperature (38 °C) and severe elevated body temperature (39-40 °C) in two groups. In the first (nicotine) group, rats were treated with single injections of nicotine enough to produce brain levels of nicotine similar to those achieved in human smokers (2.5 (mg kg-1)/day) until the recording day. In the second (control) group, rats were treated with injections of saline, beginning at postnatal 5 days until the recording day. Our results show that warming the rat by 2-3 °C and nicotine exposure significantly decreased the complexity of the EMGdia and EMGg for the juvenile age group. This reduction in the complexity of the EMGdia and EMGg for the nicotine group was much greater than the normal during elevated body temperatures. We speculate that the generalized depressive effects of nicotine exposure and elevated body temperature on the respiratory neural firing rate and the behavior of the central respiratory network could be responsible for the drastic decrease in the complexity of the EMGdia and EMGg signals, the outputs of the respiratory neural network during early maturation.

  16. 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.

  17. Analysis of Muscle Fatigue Progression using Cyclostationary Property of Surface Electromyography Signals.

    PubMed

    Karthick, P A; Venugopal, G; Ramakrishnan, S

    2016-01-01

    Analysis of neuromuscular fatigue finds various applications ranging from clinical studies to biomechanics. Surface electromyography (sEMG) signals are widely used for these studies due to its non-invasiveness. During cyclic dynamic contractions, these signals are nonstationary and cyclostationary. In recent years, several nonstationary methods have been employed for the muscle fatigue analysis. However, cyclostationary based approach is not well established for the assessment of muscle fatigue. In this work, cyclostationarity associated with the biceps brachii muscle fatigue progression is analyzed using sEMG signals and Spectral Correlation Density (SCD) functions. Signals are recorded from fifty healthy adult volunteers during dynamic contractions under a prescribed protocol. These signals are preprocessed and are divided into three segments, namely, non-fatigue, first muscle discomfort and fatigue zones. Then SCD is estimated using fast Fourier transform accumulation method. Further, Cyclic Frequency Spectral Density (CFSD) is calculated from the SCD spectrum. Two features, namely, cyclic frequency spectral area (CFSA) and cyclic frequency spectral entropy (CFSE) are proposed to study the progression of muscle fatigue. Additionally, degree of cyclostationarity (DCS) is computed to quantify the amount of cyclostationarity present in the signals. Results show that there is a progressive increase in cyclostationary during the progression of muscle fatigue. CFSA shows an increasing trend in muscle fatiguing contraction. However, CFSE shows a decreasing trend. It is observed that when the muscle progresses from non-fatigue to fatigue condition, the mean DCS of fifty subjects increases from 0.016 to 0.99. All the extracted features found to be distinct and statistically significant in the three zones of muscle contraction (p < 0.05). It appears that these SCD features could be useful in the automated analysis of sEMG signals for different neuromuscular conditions

  18. Differences in age-related fiber atrophy between vastii muscles of active subjects: a multichannel surface EMG study.

    PubMed

    Boccia, Gennaro; Dardanello, Davide; Coratella, Giuseppe; Rinaldo, Nicoletta; Schena, Federico; Rainoldi, Alberto

    2015-07-01

    The aim of the study was to non-invasively determine if vastus lateralis (VL) and vastus medialis obliquus (VM) muscles are equally affected by age-related fiber atrophy. Multichannel surface electromyography was used since it allows to estimate muscle fiber conduction velocity (CV), which has been demonstrated to be related to the size of recruited muscle fibers. Twelve active elderly men (age 69   ±   4 years) and 12 active young men (age 23   ±   2 years) performed isometric knee extension at 30%, 50%, and 70% of maximal voluntary contraction. Electromyographic signals were recorded from VL and VM muscles of the dominant limb using arrays with eight electrodes and CVs were estimated for each contraction. CV estimates showed a different behavior in the two muscles: in VL at 50% and 70% of maximum voluntary contraction they were greater in young than in elderly; whereas such a difference was not observed in VM. This finding suggest that in active elderly VM seems to be less affected by the age-related fibers atrophy than VL. Hence, the common choice of studying VL as a muscle representative of the whole quadriceps could generate misleading findings. Indeed, it seemed that the sarcopenic ageing effects might be heterogeneous within quadriceps muscle. PMID:26057569

  19. 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

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

    PubMed

    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

  1. 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.

  2. Relation between object properties and EMG during reaching to grasp.

    PubMed

    Fligge, Nadine; Urbanek, Holger; van der Smagt, Patrick

    2013-04-01

    In order to stably grasp an object with an artificial hand, a priori knowledge of the object's properties is a major advantage, especially to ensure subsequent manipulation of the object held by the hand. This is also true for hand prostheses: pre-shaping of the hand while approaching the object, similar to able-bodied, allows the wearer for a much faster and more intuitive way of handling and grasping an object. For hand prostheses, it would be advantageous to obtain this information about object properties from a surface electromyography (sEMG) signal, which is already present and used to control the active prosthetic hand. We describe experiments in which human subjects grasp different objects at different positions while their muscular activity is recorded through eight sEMG electrodes placed on the forearm. Results show that sEMG data, gathered before the hand is in contact with the object, can be used to obtain relevant information on object properties such as size and weight. PMID:23207412

  3. Validation of a portable EMG device to assess muscle activity during free-living situations.

    PubMed

    Walters, T J; Kaschinske, K A; Strath, S J; Swartz, A M; Keenan, K G

    2013-10-01

    Portable amplifiers that record electromyograms (EMGs) for longer than four hours are commonly priced over $20,000 USD. This cost, and the technical challenges associated with recording EMGs during free-living situations, typically restrict EMG use to laboratory settings. A low-cost system (μEMG; OT Bioelecttronica, 100€), using specialized concentric bipolar electrodes, has been developed specifically for free-living situations. The purpose of this study was to validate the μEMG system by comparing EMGs from μEMG with a laboratory-based alternative (Telemyo 900; Noraxon USA, Inc.). Surface EMGs from biceps brachii (BB) and tibialis anterior (TA) of ten subjects were recorded simultaneously with both systems as subjects performed maximal voluntary contractions (MVCs), submaximal contractions at 25%, 50%, and 75% MVC, seven simulated activities of daily living (ADLs), and >60min of simulated free-living inside the laboratory. In general, EMG parameters (e.g., average full-wave rectified EMG amplitude) derived from both systems were not significantly different for all outcome variables, except there were small differences across systems in baseline noise and absolute EMG amplitudes during MVCs. These results suggest that μEMG is a valid approach to the long-term recording of EMG. PMID:23830889

  4. Detection of and Compensation for EMG Disturbances for Powered Lower Limb Prosthesis Control.

    PubMed

    Spanias, John A; Perreault, Eric J; Hargrove, Levi J

    2016-02-01

    Myoelectric pattern recognition algorithms have been proposed for the control of powered lower limb prostheses, but electromyography (EMG) signal disturbances remain an obstacle to clinical implementation. To address this problem, we used a log-likelihood metric to detect simulated EMG disturbances and real disturbances acquired from EMG containing electrode shift. We found that features extracted from disturbed EMG have much lower log likelihoods than those from undisturbed signals and can be detected using a single threshold acquired from the training data. We designed a linear discriminant analysis (LDA) classifier that uses the log likelihood to decide between using a combination of EMG and mechanical sensors and using mechanical sensors only, to predict locomotion modes. When EMG contained disturbances, our classifier detected those disturbances and disregarded EMG data. Our classifier had significantly lower errors than a standard LDA classifier in the presence of EMG disturbances. The log-likelihood classifier had a low false positive threshold, and thus did not perform significantly differently from the standard LDA classifier when EMG did not contain disturbances. The log-likelihood threshold could also be applied to individual EMG channels, enabling specific channels containing EMG disturbances to be appropriately ignored when making locomotion mode predictions. PMID:25826807

  5. Knowledge of electromyography (EMG) in patients undergoing EMG examinations.

    PubMed

    Mondelli, Mauro; Aretini, Alessandro; Greco, Giuseppe

    2014-01-01

    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

  6. Estimation of finger postures to control a maniform device for playing a trumpet using electromyographic signals with external triggers.

    PubMed

    Kobayashi, Yutaro; Fukayama, Osamu; Suzuki, Takafumi; Mabuchi, Kunihiko

    2010-01-01

    Electromyographic (EMG) signals have been used to control active prosthetic arms for amputees. One of the obstacles in making such prosthetic arms is the timed estimation of posture, because EMG signals and muscle movements are not necessarily synchronized. We estimated the finger motions for trumpet players by using both surface EMG (sEMG) and the timing information using body motion. The algorithms consisted of Principal Component Analysis (PCA), and Support Vector Machine (SVM). The results showed that applying the timing information using body motion increases how precisely the motion of the fingers is estimated. PMID:21096921

  7. 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

  8. Non-Linear EMG Parameters for Differential and Early Diagnostics of Parkinson’s Disease

    PubMed Central

    Meigal, Alexander Y.; Rissanen, Saara M.; Tarvainen, Mika P.; Airaksinen, Olavi; Kankaanpää, Markku; Karjalainen, Pasi A.

    2013-01-01

    The pre-clinical diagnostics is essential for management of Parkinson’s disease (PD). Although PD has been studied intensively in the last decades, the pre-clinical indicators of that motor disorder have yet to be established. Several approaches were proposed but the definitive method is still lacking. Here we report on the non-linear characteristics of surface electromyogram (sEMG) and tremor acceleration as a possible diagnostic tool, and, in prospective, as a predictor for PD. Following this approach we calculated such non-linear parameters of sEMG and accelerometer signal as correlation dimension, entropy, and determinism. We found that the non-linear parameters allowed discriminating some 85% of healthy controls from PD patients. Thus, this approach offers considerable potential for developing sEMG-based method for pre-clinical diagnostics of PD. However, non-linear parameters proved to be more reliable for the shaking form of PD, while diagnostics of the rigid form of PD using EMG remains an open question. PMID:24062722

  9. The Effect of Background Muscle Activity on Computerized Detection of sEMG Onset and Offset

    PubMed Central

    Lee, Angela S.; Cholewicki, Jacek; Reeves, N. Peter

    2007-01-01

    The performance of two computerized algorithms for the detection of muscle onset and offset was compared. Standard deviation (SD) method, a commonly used algorithm, and approximated generalized likelihood ratio (AGLR) method, a more recently developed algorithm, were evaluated at different levels of background surface EMG (sEMG) activity. For this purpose, the amplitude ratio between the period of muscle inactivity and activity was varied from 0.125 to 1 in artificially assembled sEMG traces. In addition, 1230 real sEMG signals, obtained from various trunk muscles, were raised to a power of 3 to change the relative amplitude ratio. As the relative level of background activity increased, both the SD and AGLR methods produced longer latencies and detected fewer muscle responses, suggesting that a detection artifact can be introduced if the subject populations being compared have different levels of background muscle activity. Of the two methods, AGLR appears to be the least affected by background activity. However, above the ratio 0.8, results from AGLR are also unreliable particularly in detecting offsets. Average latency artifacts near this ratio were 8 ms for AGLR and 46 ms for SD. PMID:17588589

  10. 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

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

    SciTech Connect

    Chandrasekhar Potluri,; Madhavi Anugolu; Marco P. Schoen; D. Subbaram Naidu

    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, 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.

  12. 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

  13. EMG-based facial gesture recognition through versatile elliptic basis function neural network

    PubMed Central

    2013-01-01

    Background Recently, the recognition of different facial gestures using facial neuromuscular activities has been proposed for human machine interfacing applications. Facial electromyograms (EMGs) analysis is a complicated field in biomedical signal processing where accuracy and low computational cost are significant concerns. In this paper, a very fast versatile elliptic basis function neural network (VEBFNN) was proposed to classify different facial gestures. The effectiveness of different facial EMG time-domain features was also explored to introduce the most discriminating. Methods In this study, EMGs of ten facial gestures were recorded from ten subjects using three pairs of surface electrodes in a bi-polar configuration. The signals were filtered and segmented into distinct portions prior to feature extraction. Ten different time-domain features, namely, Integrated EMG, Mean Absolute Value, Mean Absolute Value Slope, Maximum Peak Value, Root Mean Square, Simple Square Integral, Variance, Mean Value, Wave Length, and Sign Slope Changes were extracted from the EMGs. The statistical relationships between these features were investigated by Mutual Information measure. Then, the feature combinations including two to ten single features were formed based on the feature rankings appointed by Minimum-Redundancy-Maximum-Relevance (MRMR) and Recognition Accuracy (RA) criteria. In the last step, VEBFNN was employed to classify the facial gestures. The effectiveness of single features as well as the feature sets on the system performance was examined by considering the two major metrics, recognition accuracy and training time. Finally, the proposed classifier was assessed and compared with conventional methods support vector machines and multilayer perceptron neural network. Results The average classification results showed that the best performance for recognizing facial gestures among all single/multi-features was achieved by Maximum Peak Value with 87.1% accuracy

  14. Re-examination of the surface EMG activity of the masseter muscle in young adults during chewing of two test foods.

    PubMed

    Karkazis, H C; Kossioni, A E

    1997-03-01

    The purpose of this study was to investigate the effect of the texture of food on the masseter EMG activity during chewing. Fresh raw carrots and non-adhesive chewing gums of similar size and weight were used as representing a hard and a soft food respectively. The mean values for the IEMG activity, the duration of the chewing cycle, the chewing rate and the relative contraction time during chewing were significantly higher for the carrots while no significant difference was found in the chewing burst duration between the two test foods. Finally a strong inverse correlation was found between chewing rate and cycle duration. It was concluded that the texture of food has an obvious effect on EMG activity during chewing and that adjustments to changes in food consistency are made mainly by altering the chewing rate, the duration of the chewing cycle and the IEMG activity. PMID:9131477

  15. Unsupervised Bayesian decomposition of multiunit EMG recordings using Tabu search.

    PubMed

    Ge, Di; Le Carpentier, Eric; Farina, Dario

    2010-03-01

    Intramuscular electromyography (EMG) signals are usually decomposed with semiautomatic procedures that involve the interaction with an expert operator. In this paper, a Bayesian statistical model and a maximum a posteriori (MAP) estimator are used to solve the problem of multiunit EMG decomposition in a fully automatic way. The MAP estimation exploits both the likelihood of the reconstructed EMG signal and some physiological constraints, such as the discharge pattern regularity and the refractory period of muscle fibers, as prior information integrated in a Bayesian framework. A Tabu search is proposed to efficiently tackle the nondeterministic polynomial-time-hard problem of optimization w.r.t the motor unit discharge patterns. The method is fully automatic and was tested on simulated and experimental EMG signals. Compared with the semiautomatic decomposition performed by an expert operator, the proposed method resulted in an accuracy of 90.0% +/- 3.8% when decomposing single-channel intramuscular EMG signals recorded from the abductor digiti minimi muscle at contraction forces of 5% and 10% of the maximal force. The method can also be applied to the automatic identification and classification of spikes from other neural recordings. PMID:19457743

  16. 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. PMID:25227075

  17. A sparse Bayesian learning based scheme for multi-movement recognition using sEMG.

    PubMed

    Ding, Shuai; Wang, Liang

    2016-03-01

    This paper proposed a feature extraction scheme based on sparse representation considering the non-stationary property of surface electromyography (sEMG). Sparse Bayesian learning was introduced to extract the feature with optimal class separability to improve recognition accuracy of multi-movement patterns. The extracted feature, sparse representation coefficients (SRC), represented time-varying characteristics of sEMG effectively because of the compressibility (or weak sparsity) of the signal in some transformed domains. We investigated the effect of the proposed feature by comparing with other fourteen individual features in offline recognition. The results demonstrated the proposed feature revealed important dynamic information in the sEMG signals. The multi-feature sets formed by the SRC and other single feature yielded more superior performance on recognition accuracy, compared with the single features. The best average recognition accuracy of 94.33 % was gained by using SVM classifier with the multi-feature set combining the feature SRC, Williston amplitude (WAMP), wavelength (WL) and the coefficients of the fourth order autoregressive model (ARC4) via multiple kernel learning framework. The proposed feature extraction scheme (known as SRC + WAMP + WL + ARC4) is a promising method for multi-movement recognition with high accuracy. PMID:26577712

  18. 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.

  19. Zebrafish needle EMG: a new tool for high-throughput drug screens

    PubMed Central

    Cho, Sung-Joon; Nam, Tai-Seung; Byun, Donghak; Choi, Seok-Yong; Kim, Myeong-Kyu

    2015-01-01

    Zebrafish models have recently been highlighted as a valuable tool in studying the molecular basis of neuromuscular diseases and developing new pharmacological treatments. Needle electromyography (EMG) is needed not only for validating transgenic zebrafish models with muscular dystrophies (MD), but also for assessing the efficacy of therapeutics. However, performing needle EMG on larval zebrafish has not been feasible due to the lack of proper EMG sensors and systems for such small animals. We introduce a new type of EMG needle electrode to measure intramuscular activities of larval zebrafish, together with a method to hold the animal in position during EMG, without anesthetization. The silicon-based needle electrode was found to be sufficiently strong and sharp to penetrate the skin and muscles of zebrafish larvae, and its shape and performance did not change after multiple insertions. With the use of the proposed needle electrode and measurement system, EMG was successfully performed on zebrafish at 30 days postfertilization (dpf) and at 5 dpf. Burst patterns and spike morphology of the recorded EMG signals were analyzed. The measured single spikes were triphasic with an initial positive deflection, which is typical for motor unit action potentials, with durations of ∼10 ms, whereas the muscle activity was silent during the anesthetized condition. These findings confirmed the capability of this system of detecting EMG signals from very small animals such as 5 dpf zebrafish. The developed EMG sensor and system are expected to become a helpful tool in validating zebrafish MD models and further developing therapeutics. PMID:26180124

  20. Zebrafish needle EMG: a new tool for high-throughput drug screens.

    PubMed

    Cho, Sung-Joon; Nam, Tai-Seung; Byun, Donghak; Choi, Seok-Yong; Kim, Myeong-Kyu; Kim, Sohee

    2015-09-01

    Zebrafish models have recently been highlighted as a valuable tool in studying the molecular basis of neuromuscular diseases and developing new pharmacological treatments. Needle electromyography (EMG) is needed not only for validating transgenic zebrafish models with muscular dystrophies (MD), but also for assessing the efficacy of therapeutics. However, performing needle EMG on larval zebrafish has not been feasible due to the lack of proper EMG sensors and systems for such small animals. We introduce a new type of EMG needle electrode to measure intramuscular activities of larval zebrafish, together with a method to hold the animal in position during EMG, without anesthetization. The silicon-based needle electrode was found to be sufficiently strong and sharp to penetrate the skin and muscles of zebrafish larvae, and its shape and performance did not change after multiple insertions. With the use of the proposed needle electrode and measurement system, EMG was successfully performed on zebrafish at 30 days postfertilization (dpf) and at 5 dpf. Burst patterns and spike morphology of the recorded EMG signals were analyzed. The measured single spikes were triphasic with an initial positive deflection, which is typical for motor unit action potentials, with durations of ∼10 ms, whereas the muscle activity was silent during the anesthetized condition. These findings confirmed the capability of this system of detecting EMG signals from very small animals such as 5 dpf zebrafish. The developed EMG sensor and system are expected to become a helpful tool in validating zebrafish MD models and further developing therapeutics. PMID:26180124

  1. Re-evaluation of EMG-torque relation in chronic stroke using linear electrode array EMG recordings

    PubMed Central

    Bhadane, Minal; Liu, Jie; Rymer, W. Zev; Zhou, Ping; Li, Sheng

    2016-01-01

    The objective was to re-evaluate the controversial reports of EMG-torque relation between impaired and non-impaired sides using linear electrode array EMG recordings. Ten subjects with chronic stroke performed a series of submaximal isometric elbow flexion tasks. A 20-channel linear array was used to record surface EMG of the biceps brachii muscles from both impaired and non-impaired sides. M-wave recordings for bilateral biceps brachii muscles were also made. Distribution of the slope of the EMG-torque relations for the individual channels showed a quasi-symmetrical “M” shaped pattern. The lowest value corresponded to the innervation zone (IZ) location. The highest value from the slope curve for each side was selected for comparison to minimize the effect of electrode placement and IZ asymmetry. The slope was greater on the impaired side in 4 of 10 subjects. There were a weak correlation between slope ratio and strength ratio and a moderate to high correlation between slope ratio and M-wave ratio between two sides. These findings suggest that the EMG-torque relations are likely mediated and influenced by multiple factors. Our findings emphasize the importance of electrode placement and suggest the primary role of peripheral adaptive changes in the EMG-torque relations in chronic stroke. PMID:27349938

  2. Re-evaluation of EMG-torque relation in chronic stroke using linear electrode array EMG recordings.

    PubMed

    Bhadane, Minal; Liu, Jie; Rymer, W Zev; Zhou, Ping; Li, Sheng

    2016-01-01

    The objective was to re-evaluate the controversial reports of EMG-torque relation between impaired and non-impaired sides using linear electrode array EMG recordings. Ten subjects with chronic stroke performed a series of submaximal isometric elbow flexion tasks. A 20-channel linear array was used to record surface EMG of the biceps brachii muscles from both impaired and non-impaired sides. M-wave recordings for bilateral biceps brachii muscles were also made. Distribution of the slope of the EMG-torque relations for the individual channels showed a quasi-symmetrical "M" shaped pattern. The lowest value corresponded to the innervation zone (IZ) location. The highest value from the slope curve for each side was selected for comparison to minimize the effect of electrode placement and IZ asymmetry. The slope was greater on the impaired side in 4 of 10 subjects. There were a weak correlation between slope ratio and strength ratio and a moderate to high correlation between slope ratio and M-wave ratio between two sides. These findings suggest that the EMG-torque relations are likely mediated and influenced by multiple factors. Our findings emphasize the importance of electrode placement and suggest the primary role of peripheral adaptive changes in the EMG-torque relations in chronic stroke. PMID:27349938

  3. 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.

  4. Elbow torques and EMG patterns of flexor muscles during different isometric tasks.

    PubMed

    Caldwell, G E; Van Leemputte, M

    1991-01-01

    This paper examines the torque responses and EMG activity levels in four muscles acting at the elbow joint during different combinations of one- and two- degree of freedom isometric torque production (single and dual tasks, respectively). Flexor and supinator/pronator torques and surface EMG signals from m. biceps brachii, m. brachialis, m. brachioradialis and m. triceps brachii were measured in 16 male subjects while they performed maximal effort isometric contractions of pure flexion, pure supination, pure pronation, combined flexion and supination and combined flexion and pronation. In the single tasks, the torque responses were consistent with task requirements, but the dual task results were surprising in that flexor torque levels were reduced as compared to pure flexion, while supinator/pronator torque levels were as high or higher than in pure supination or pronation. Muscle activity levels varied with task, and could not always explain the differences observed in torque responses. These data are discussed within the framework of subpopulations of task-specific motor units within each muscle. The implications of such task-specific muscle units are related to musculoskeletal modelling and previous EMG - torque relationships found at the elbow. PMID:1748080

  5. Design of a robust EMG sensing interface for pattern classification.

    PubMed

    Huang, He; Zhang, Fan; Sun, Yan L; He, Haibo

    2010-10-01

    Electromyographic (EMG) pattern classification has been widely investigated for neural control of external devices in order to assist with movements of patients with motor deficits. Classification performance deteriorates due to inevitable disturbances to the sensor interface, which significantly challenges the clinical value of this technique. This study aimed to design a sensor fault detection (SFD) module in the sensor interface to provide reliable EMG pattern classification. This module monitored the recorded signals from individual EMG electrodes and performed a self-recovery strategy to recover the classification performance when one or more sensors were disturbed. To evaluate this design, we applied synthetic disturbances to EMG signals collected from leg muscles of able-bodied subjects and a subject with a transfemoral amputation and compared the accuracies for classifying transitions between different locomotion modes with and without the SFD module. The results showed that the SFD module maintained classification performance when one signal was distorted and recovered about 20% of classification accuracy when four signals were distorted simultaneously. The method was simple to implement. Additionally, these outcomes were observed for all subjects, including the leg amputee, which implies the promise of the designed sensor interface for providing a reliable neural-machine interface for artificial legs. PMID:20811091

  6. Design of a robust EMG sensing interface for pattern classification

    NASA Astrophysics Data System (ADS)

    Huang, He; Zhang, Fan; Sun, Yan L.; He, Haibo

    2010-10-01

    Electromyographic (EMG) pattern classification has been widely investigated for neural control of external devices in order to assist with movements of patients with motor deficits. Classification performance deteriorates due to inevitable disturbances to the sensor interface, which significantly challenges the clinical value of this technique. This study aimed to design a sensor fault detection (SFD) module in the sensor interface to provide reliable EMG pattern classification. This module monitored the recorded signals from individual EMG electrodes and performed a self-recovery strategy to recover the classification performance when one or more sensors were disturbed. To evaluate this design, we applied synthetic disturbances to EMG signals collected from leg muscles of able-bodied subjects and a subject with a transfemoral amputation and compared the accuracies for classifying transitions between different locomotion modes with and without the SFD module. The results showed that the SFD module maintained classification performance when one signal was distorted and recovered about 20% of classification accuracy when four signals were distorted simultaneously. The method was simple to implement. Additionally, these outcomes were observed for all subjects, including the leg amputee, which implies the promise of the designed sensor interface for providing a reliable neural-machine interface for artificial legs.

  7. Design of a robust EMG sensing interface for pattern classification

    PubMed Central

    Huang, He; Zhang, Fan; Sun, Yan L.; He, Haibo

    2010-01-01

    Electromyographic (EMG) pattern classification has been widely investigated for neural control of external devices in order to assist with movements of patients with motor deficits. Classification performance deteriorates due to inevitable disturbances to the sensor interface, which significantly challenges the clinical value of this technique. This study aimed to design a sensor fault detection (SFD) module in the sensor interface to provide reliable EMG pattern classification. This module monitored the recorded signals from individual EMG electrodes and performed a self-recovery strategy to recover the classification performance when one or more sensors were disturbed. To evaluate this design, we applied synthetic disturbances to EMG signals collected from leg muscles of able-bodied subjects and a subject with a transfemoral amputation and compared the accuracies for classifying transitions between different locomotion modes with and without the SFD module. The results showed that the SFD module maintained classification performance when one signal was distorted and recovered about 20% of classification accuracy when four signals were distorted simultaneously. The method was simple to implement. Additionally, these outcomes were observed for all subjects, including the leg amputee, which implies the promise of the designed sensor interface for providing a reliable neural-machine interface for artificial legs. PMID:20811091

  8. Proportional EMG control for upper-limb powered exoskeletons.

    PubMed

    Lenzi, T; De Rossi, S M M; Vitiello, N; Carrozza, M C

    2011-01-01

    Electromyography (EMG) has been frequently proposed as the driving signal for controlling powered exoskeletons. Lot of effort has been spent to design accurate algorithms for muscular torque estimation, while very few studies attempted to understand to what extent an accurate torque estimate is indeed necessary to provide effective movement assistance through powered exoskeletons. In this study, we focus on the latter aspect by using a simple and "low-accuracy" torque estimate, an EMG-proportional control, to provide assistance through an elbow exoskeleton. Preliminary results show that subjects adapt almost instantaneously to the assistance provided by the exoskeleton and can reduce their effort while keeping full control of the movement. PMID:22254387

  9. Increased intensity and reduced frequency of EMG signals from feline self-reinnervated ankle extensors during walking do not normalize excessive lengthening.

    PubMed

    Pantall, Annette; Hodson-Tole, Emma F; Gregor, Robert J; Prilutsky, Boris I

    2016-06-01

    Kinematics of cat level walking recover after elimination of length-dependent sensory feedback from the major ankle extensor muscles induced by self-reinnervation. Little is known, however, about changes in locomotor myoelectric activity of self-reinnervated muscles. We examined the myoelectric activity of self-reinnervated muscles and intact synergists to determine the extent to which patterns of muscle activity change as almost normal walking is restored following muscle self-reinnervation. Nerves to soleus (SO) and lateral gastrocnemius (LG) of six adult cats were surgically transected and repaired. Intramuscular myoelectric signals of SO, LG, medial gastrocnemius (MG), and plantaris (PL), muscle fascicle length of SO and MG, and hindlimb mechanics were recorded during level and slope (±27°) walking before and after (10-12 wk postsurgery) self-reinnervation of LG and SO. Mean myoelectric signal intensity and frequency were determined using wavelet analysis. Following SO and LG self-reinnervation, mean myoelectric signal intensity increased and frequency decreased in most conditions for SO and LG as well as for intact synergist MG (P < 0.05). Greater elongation of SO muscle-tendon unit during downslope and unchanged magnitudes of ankle extensor moment during the stance phase in all walking conditions suggested a functional deficiency of ankle extensors after self-reinnervation. Possible effects of morphological reorganization of motor units of ankle extensors and altered sensory and central inputs on the changes in myoelectric activity of self-reinnervated SO and LG are discussed. PMID:26912591

  10. Damage to the cuff of EMG tube at endotracheal intubation by using a lightwand -A case report-

    PubMed Central

    Kim, Hyun-Sook; Park, Keun-Suk; Kang, Mae-Hwa

    2010-01-01

    Electromyogpraphic endotracheal tube (EMG tube) is a new device used to monitor recurrent laryngeal nerve integrity during thyroid surgery. The EMG tube has 2 pairs of electrodes on the surface of silicon-based tube reached to inner space of tube cuff. We experienced an unusual endotracheal tube-related problem from the distinct structural feature of the EMG tube. In this case, we intubated a patient who had difficult airway with the EMG tube using a lightwand. After successful endotracheal intubation, we could not expand the pilot balloon and ventilate the patient effectively. We removed the EMG tube and found that one of electrodes of the EMG tube is bended and made a right angle with the long axis of the tube, and perforated the tube cuff. So we report this case to make anesthesia providers aware that much more attention is needed to use EMG tube during endotracheal intubation. PMID:21286432

  11. 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

  12. Does voluntary hypoventilation during exercise impact EMG activity?

    PubMed

    Kume, Daisuke; Akahoshi, Shogo; Yamagata, Takashi; Wakimoto, Toshihiro; Nagao, Noriki

    2016-01-01

    It has been reported that exercise under hypoxic conditions induces reduced muscle oxygenation, which could be related to enhanced activity on electromyography (EMG). Although it has been demonstrated that exercise under conditions of voluntary hypoventilation (VH) evokes muscle deoxygenation, it is unclear whether VH during exercise impacts EMG. Seven men performed bicycle exercise for 5 min at 65 % of peak oxygen uptake with normal breathing (NB) and VH. Muscle oxygenation; concentration changes in oxyhemoglobin (Oxy-Hb), deoxyhemoglobin (Deoxy-Hb) and total hemoglobin (Total-Hb); and surface EMG in the vastus lateralis muscle were simultaneously measured. In the VH condition, Oxy-Hb was significantly lower and Deoxy-Hb was significantly higher compared to those in the NB condition (P < 0.05 for both), whereas there was no significant difference in Total-Hb between the two conditions. We observed significantly higher values (P < 0.05) on integrated EMG during exercise under VH conditions compared to those under NB conditions. This study suggests that VH during exercise augments EMG activity. PMID:27026846

  13. Evaluation of muscle force classification using shape analysis of the sEMG probability density function: a simulation study.

    PubMed

    Ayachi, F S; Boudaoud, S; Marque, C

    2014-08-01

    In this work, we propose to classify, by simulation, the shape variability (or non-Gaussianity) of the surface electromyogram (sEMG) amplitude probability density function (PDF), according to contraction level, using high-order statistics (HOS) and a recent functional formalism, the core shape modeling (CSM). According to recent studies, based on simulated and/or experimental conditions, the sEMG PDF shape seems to be modified by many factors as: contraction level, fatigue state, muscle anatomy, used instrumentation, and also motor control parameters. For sensitivity evaluation against these several sources (physiological, instrumental, and neural control) of variability, a large-scale simulation (25 muscle anatomies, ten parameter configurations, three electrode arrangements) is performed, by using a recent sEMG-force model and parallel computing, to classify sEMG data from three contraction levels (20, 50, and 80% MVC). A shape clustering algorithm is then launched using five combinations of HOS parameters, the CSM method and compared to amplitude clustering with classical indicators [average rectified value (ARV) and root mean square (RMS)]. From the results screening, it appears that the CSM method obtains, using Laplacian electrode arrangement, the highest classification scores, after ARV and RMS approaches, and followed by one HOS combination. However, when some critical confounding parameters are changed, these scores decrease. These simulation results demonstrate that the shape screening of the sEMG amplitude PDF is a complex task which needs both efficient shape analysis methods and specific signal recording protocol to be properly used for tracking neural drive and muscle activation strategies with varying force contraction in complement to classical amplitude estimators. PMID:24961179

  14. Digitally controlled feedback for DC offset cancellation in a wearable multichannel EMG platform.

    PubMed

    Tomasini, M; Benatti, S; Casamassima, F; Milosevic, B; Fateh, S; Farella, E; Benini, L

    2015-01-01

    Wearable systems capable to capture vital signs allow the development of advanced medical applications. One notable example is the use of surface electromyography (EMG) to gather muscle activation potentials, in principle an easy input for prosthesis control. However, the acquisition of such signals is affected by high variability and ground loop problems. Moreover, the input impedance influenced in time by motion and perspiration determines an offset, which can be orders of magnitude higher than the signal of interest. We propose a wearable device equipped with a digitally controlled Analog Front End (AFE) for biopotentials acquisition with zero-offset. The proposed AFE solution has an internal Digital to Analog Converter (DAC) used to adjust independently the reference of each channel removing any DC offset. The analog integrated circuit is coupled with a microcontroller, which periodically estimates the offset and implements a closed loop feedback on the analog part. The proposed approach was tested on EMG signals acquired from 4 subjects while performing different activities and shows that the system correctly acquires signals with no DC offset. PMID:26736970

  15. Monopolar electromyographic signals recorded by a current amplifier in air and under water without insulation.

    PubMed

    Whitting, John W; von Tscharner, Vinzenz

    2014-12-01

    It was recently proposed that one could use signal current instead of voltage to collect surface electromyography (EMG). With EMG-current, the electrodes remain at the ground potential, thereby eliminating lateral currents. The purpose of this study was to determine whether EMG-currents can be recorded in Tap and Salt water, as well as in air, without electrically shielding the electrodes. It was hypothesized that signals would display consistent information between experimental conditions regarding muscle responses to changes in contraction effort. EMG-currents were recorded from the flexor digitorum muscles as participant's squeezed a pre-inflated blood pressure cuff bladder in each experimental condition at standardized efforts. EMG-current measurements performed underwater showed no loss of signal amplitude when compared to measurements made in air, although some differences in amplitude and spectral components were observed between conditions. However, signal amplitudes and frequencies displayed consistent behavior across contraction effort levels, irrespective of the experimental condition. This new method demonstrates that information regarding muscle activity is comparable between wet and dry conditions when using EMG-current. Considering the difficulties imposed by the need to waterproof traditional bipolar EMG electrodes when underwater, this new methodology is tremendously promising for assessments of muscular function in aquatic environments. PMID:25241214

  16. Rigorous A-Posteriori Assessment of Accuracy in EMG Decomposition

    PubMed Central

    McGill, Kevin C.; Marateb, Hamid R.

    2010-01-01

    If EMG decomposition is to be a useful tool for scientific investigation, it is essential to know that the results are accurate. Because of background noise, waveform variability, motor-unit action potential (MUAP) indistinguishability, and perplexing superpositions, accuracy assessment is not straightforward. This paper presents a rigorous statistical method for assessing decomposition accuracy based only on evidence from the signal itself. The method uses statistical decision theory in a Bayesian framework to integrate all the shape- and firing-time-related information in the signal to compute an objective a-posteriori measure of confidence in the accuracy of each discharge in the decomposition. The assessment is based on the estimated statistical properties of the MUAPs and noise and takes into account the relative likelihood of every other possible decomposition. The method was tested on 3 pairs of real EMG signals containing 4–7 active MUAP trains per signal that had been decomposed by a human expert. It rated 97% of the identified MUAP discharges as accurate to within ±0.5 ms with a confidence level of 99%, and detected 6 decomposition errors. Cross-checking between signal pairs verified all but 2 of these assertions. These results demonstrate that the approach is reliable and practical for real EMG signals. PMID:20639182

  17. 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. PMID:24209927

  18. 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

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

    PubMed

    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

  20. 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.

  1. 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. PMID:26513799

  2. Signaling reactions on membrane surfaces: breaking the law of averages

    NASA Astrophysics Data System (ADS)

    Groves, Jay T.

    Most intracellular signal transduction reactions take place on the membrane surface. The membrane provides much more than just a surface environment on which signaling molecules are concentrated. There is a growing realization that multiple physical and chemical mechanisms allow the membrane to actively participate in the signaling reactions. Using a combination of single molecule imaging and spectroscopic techniques, my research seeks to directly resolve the actual mechanics of signaling reactions on membrane surfaces both in reconstituted systems and in living cells. These observations are revealing new insights into cellular signaling processes as well as some unexpected functional behaviors of proteins on the membrane surface.

  3. Experimentally Induced Stress Validated by EMG Activity

    PubMed Central

    Luijcks, Rosan; Hermens, Hermie J.; Bodar, Lonneke; Vossen, Catherine J.; Os, Jim van.; Lousberg, Richel

    2014-01-01

    Experience of stress may lead to increased electromyography (EMG) activity in specific muscles compared to a non-stressful situation. The main aim of this study was to develop and validate a stress-EMG paradigm in which a single uncontrollable and unpredictable nociceptive stimulus was presented. EMG activity of the trapezius muscles was the response of interest. In addition to linear time effects, non-linear EMG time courses were also examined. Taking into account the hierarchical structure of the dataset, a multilevel random regression model was applied. The stress paradigm, executed in N = 70 subjects, consisted of a 3-minute baseline measurement, a 3-minute pre-stimulus stress period and a 2-minute post-stimulus phase. Subjects were unaware of the precise moment of stimulus delivery and its intensity level. EMG activity during the entire experiment was conform a priori expectations: the pre-stimulus phase showed a significantly higher mean EMG activity level compared to the other two phases, and an immediate EMG response to the stimulus was demonstrated. In addition, the analyses revealed significant non-linear EMG time courses in all three phases. Linear and quadratic EMG time courses were significantly modified by subjective anticipatory stress level, measured just before the start of the stress task. Linking subjective anticipatory stress to EMG stress reactivity revealed that subjects with a high anticipatory stress level responded with more EMG activity during the pre-stimulus stress phase, whereas subjects with a low stress level showed an inverse effect. Results suggest that the stress paradigm presented here is a valid test to quantify individual differences in stress susceptibility. Further studies with this paradigm are required to demonstrate its potential use in mechanistic clinical studies. PMID:24736740

  4. Measuring human locomotor control using EMG and EEG: Current knowledge, limitations and future considerations.

    PubMed

    Enders, Hendrik; Nigg, Benno M

    2016-06-01

    Electrical signals encoding different forms of information can be observed at multiple levels of the human nervous system. Typically, these signals have been recorded in a rather isolated fashion with little overlap between the static recordings of electroencephalography (EEG) commonly used in neuroscience and the typical surface electromyography (EMG) recordings used in biomechanics. However, within the last decade, there has been an emerging need to link the electrical activation patterns of brain areas during movement to the behavior of the musculoskeletal system. This review discusses some of the most recent studies using the EEG and/or EMG to study the neural control of movement and human locomotion as well as studies quantifying the connectivity between brain and muscles. The focus is on rhythmic locomotor-type activities; however, results are discussed within the framework of initial work that has been done in upper and lower limbs during static and dynamic contractions. Limitations and current challenges as well as the possibility and functional interpretation of studying the connectivity between the cortex and skeletal muscles using a measure of coherence are discussed. The manuscript is geared toward scientists interested in the application of EEG in the field of locomotion, sports and exercise. PMID:26238032

  5. Multi-muscle control during bipedal stance: an EMG-EMG analysis approach.

    PubMed

    Danna-Dos-Santos, Alessander; Boonstra, Tjeerd W; Degani, Adriana M; Cardoso, Vinicius S; Magalhaes, Alessandra T; Mochizuki, Luis; Leonard, Charles T

    2014-01-01

    Posture and postural reactions to mechanical perturbations require the harmonic modulation of the activity of multiple muscles. This precision can become suboptimal in the presence of neuromuscular disorders and result in higher fall risk and associated levels of comorbidity. This study was designed to investigate neurophysiological principles related to the generation and distribution of inputs to skeletal muscles previously recognized as a synergistic group. Specifically, we investigated the current hypothesis that correlated neural inputs, as measured by intermuscular coherence, are the mechanism used by the central nervous system to coordinate the formation of postural muscle synergies. This hypothesis was investigated by analyzing the strength and distribution of correlated neural inputs to postural muscles during the execution of a quiet stance task. Nine participants, 4 females and 5 males, mean age 29.2 years old (±6.1 SD), performed the task of standing while holding a 5-kg barbell in front of their bodies at chest level. Subjects were asked to maintain a standing position for 10 s while the activity of three postural muscles was recorded by surface electrodes: soleus (SOL), biceps femoris (BF), and lumbar erector spinae (ERE). EMG-EMG coherence was estimated for three muscle pairs (SOL/BF, SOL/ERE, and BF/ERE). Our choice of studying these muscles was made based on the fact that they have been reported as components of a functional (synergistic) muscle group that emerges during the execution of bipedal stance. In addition, an isometric contraction can be easily induced in this muscle group by simply adding a weight to the body's anterior aspect. The experimental condition elicited a significant increase in muscle activation levels for all three muscles (p < 0.01 for all muscles). EMG-EMG coherence analysis revealed significant coherence within two distinct frequency bands, 0-5 and 5-20 Hz. Significant coherence within the later frequency band was also

  6. 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.

  7. 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.

  8. 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

  9. Novel algorithm for real-time onset detection of surface electromyography in step-tracking wrist movements.

    PubMed

    Kuroda, Yoshihiro; Nisky, Ilana; Uranishi, Yuki; Imura, Masataka; Okamura, Allison M; Oshiro, Osamu

    2013-01-01

    We present a novel algorithm for real-time detection of the onset of surface electromyography signal in step-tracking wrist movements. The method identifies abrupt increase of the quasi-tension signal calculated from sEMG resulting from the step-by-step recruitment of activated motor units. We assessed the performance of our proposed algorithm using both simulated and real sEMG signals, and compared with two existing detection methods. Evaluation with simulated sEMG showed that the detection accuracy of our method is robust to different signal-to-noise ratios, and that it outperforms the existing methods in terms of bias when the noise is large (low SNR). Evaluation with real sEMG analysis also indicated better detection performance compared to existing methods. PMID:24110123

  10. Learning to modulate the partial powers of a single sEMG power spectrum through a novel human-computer interface.

    PubMed

    Skavhaug, Ida-Maria; Lyons, Kenneth R; Nemchuk, Anna; Muroff, Shira D; Joshi, Sanjay S

    2016-06-01

    New human-computer interfaces that use bioelectrical signals as input are allowing study of the flexibility of the human neuromuscular system. We have developed a myoelectric human-computer interface which enables users to navigate a cursor to targets through manipulations of partial powers within a single surface electromyography (sEMG) signal. Users obtain two-dimensional control through simultaneous adjustments of powers in two frequency bands within the sEMG spectrum, creating power profiles corresponding to cursor positions. It is unlikely that these types of bioelectrical manipulations are required during routine muscle contractions. Here, we formally establish the neuromuscular ability to voluntarily modulate single-site sEMG power profiles in a group of naïve subjects under restricted and controlled conditions using a wrist muscle. All subjects used the same pre-selected frequency bands for control and underwent the same training, allowing a description of the average learning progress throughout eight sessions. We show that subjects steadily increased target hit rates from 48% to 71% and exhibited greater control of the cursor's trajectories following practice. Our results point towards an adaptable neuromuscular skill, which may allow humans to utilize single muscle sites as limited general-purpose signal generators. Ultimately, the goal is to translate this neuromuscular ability to practical interfaces for the disabled by using a spared muscle to control external machines. PMID:26874751

  11. 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

  12. Extracting signals robust to electrode number and shift for online simultaneous and proportional myoelectric control by factorization algorithms.

    PubMed

    Muceli, Silvia; Jiang, Ning; Farina, Dario

    2014-05-01

    Previous research proposed the extraction of myoelectric control signals by linear factorization of multi-channel electromyogram (EMG) recordings from forearm muscles. This paper further analyses the theoretical basis for dimensionality reduction in high-density EMG signals from forearm muscles. Moreover, it shows that the factorization of muscular activation patterns in weights and activation signals by non-negative matrix factorization (NMF) is robust with respect to the channel configuration from where the EMG signals are obtained. High-density surface EMG signals were recorded from the forearm muscles of six individuals. Weights and activation signals extracted offline from 10 channel configurations with varying channel numbers (6, 8, 16, 192 channels) were highly similar. Additionally, the method proved to be robust against electrode shifts in both transversal and longitudinal direction with respect to the muscle fibers. In a second experiment, six subjects directly used the activation signals extracted from high-density EMG for online goal-directed control tasks involving simultaneous and proportional control of two degrees-of-freedom of the wrist. The synergy weights for this control task were extracted from a reference configuration and activation signals were calculated online from the reference configuration as well as from the two shifted configurations, simulating electrode shift. Despite the electrode shift, the task completion rate, task completion time, and execution efficiency were generally not statistically different among electrode configurations. Online performances were also mostly similar when using either 6, 8, or 16 EMG channels. The robustness of the method to the number and location of channels, proved both offline and online, indicates that EMG signals recorded from forearm muscles can be approximated as linear instantaneous mixtures of activation signals and justifies the use of linear factorization algorithms for extracting, in a

  13. Estradiol signaling via sequestrable surface receptors.

    PubMed

    Benten, W P; Stephan, C; Lieberherr, M; Wunderlich, F

    2001-04-01

    Estradiol (E(2))-signaling is widely considered to be exclusively mediated through the transcription-regulating intracellular estrogen receptor (ER) alpha and ERbeta. The aim of this study was to investigate transcription-independent E(2)-signaling in mouse IC-21 macrophages. E(2) and E(2)-BSA induce a rapid rise in the intracellular free Ca(2+) concentration ([Ca(2+)](i)) of Fura-2 loaded IC-21 cells as examined by spectrofluorometry. These changes in [Ca(2+)](i) can be inhibited by pertussis toxin, but not by the ER-blockers tamoxifen and raloxifene. The E(2)-signaling initiated at the plasma membrane is mediated through neither ERalpha nor ERbeta, but rather through a novel G protein-coupled membrane E(2)-receptor as revealed by RT-PCR, flow cytometry, and confocal laser scanning microscopy. A special feature of this E(2)-receptor is its sequestration upon agonist stimulation. Sequestration depends on energy and temperature, and it proceeds through a clathrin- and caveolin-independent pathway. PMID:11250949

  14. WHAT IS THE BEST SURFACE EMG MEASURE OF LUMBAR FLEXION-RELAXATION FOR DISTINGUISHING CHRONIC LOW BACK PAIN PATIENTS FROM PAIN-FREE CONTROLS?

    PubMed Central

    Neblett, Randy; Brede, Emily; Mayer, Tom G.; Gatchel, Robert J.

    2012-01-01

    Objectives Lumbar flexion-relaxation (FR) is a well-known phenomenon that can reliably be seen in normal subjects but not in most chronic low back pain (CLBP) patients. The purpose of this study was to determine which surface electromyographic (SEMG) measures of FR best distinguish CLBP patients from pain-free control subjects. Standing SEMG and lumbar flexion range of motion (ROM) were also evaluated. Methods A cohort of 218 CLBP patients, who were admitted to a functional restoration program, received a standardized SEMG and ROM assessment during standing trunk flexion and re-extension. An asymptomatic control group of 30 non-patients received an identical assessment. Both groups were compared on eight separate SEMG and three flexion ROM measures. Results A receiver-operating curve (ROC) analysis was used to determine how well each measure distinguished between the CLBP patients and the pain-free control subjects. All SEMG measures of FR performed acceptably. Between 79% and 82% of patients, and 83% and 100% of controls were correctly classified. Standing SEMG performed less well. Gross flexion ROM was the best single classification measure tested, correctly classifying 88% of patients and 83% of controls. A series of discriminant analyses found that certain combinations of SEMG and ROM performed slightly better than gross ROM alone for correctly classifying the two subject groups. Discussion Because all SEMG measures of FR performed acceptably, the determination of which SEMG measure of FR is “best” is largely dependent on one’s specific purpose Additionally, ROM measures were found to be important components of the FR assessment. PMID:23328325

  15. Generating strain signals under consideration of road surface profiles

    NASA Astrophysics Data System (ADS)

    Putra, T. E.; Abdullah, S.; Schramm, D.; Nuawi, M. Z.; Bruckmann, T.

    2015-08-01

    The current study aimed to develop the mechanism for generating strain signal utilising computer-based simulation. The strain data, caused by the acceleration, were undertaken from a fatigue data acquisition involving car movements. Using a mathematical model, the measured strain signals yielded to acceleration data used to describe the bumpiness of road surfaces. The acceleration signals were considered as an external disturbance on generating strain signals. Based on this comparison, both the actual and simulated strain data have similar pattern. The results are expected to provide new knowledge to generate a strain signal via a simulation.

  16. 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

  17. 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. PMID:27556154

  18. 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

  19. Terrain-Moisture Classification Using GPS Surface-Reflected Signals

    NASA Technical Reports Server (NTRS)

    Grant, Michael S.; Acton, Scott T.; Katzberg, Stephen J.

    2006-01-01

    In this study we present a novel method of land surface classification using surface-reflected GPS signals in combination with digital imagery. Two GPS-derived classification features are merged with visible image data to create terrain-moisture (TM) classes, defined here as visibly identifiable terrain or landcover classes containing a surface/soil moisture component. As compared to using surface imagery alone, classification accuracy is significantly improved for a number of visible classes when adding the GPS-based signal features. Since the strength of the reflected GPS signal is proportional to the amount of moisture in the surface, use of these GPS features provides information about the surface that is not obtainable using visible wavelengths alone. Application areas include hydrology, precision agriculture, and wetlands mapping.

  20. A switching regime model for the EMG-based control of a robot arm.

    PubMed

    Artemiadis, Panagiotis K; Kyriakopoulos, Kostas J

    2011-02-01

    Human-robot control interfaces have received increased attention during the last decades. These interfaces increasingly use signals coming directly from humans since there is a strong necessity for simple and natural control interfaces. In this paper, electromyographic (EMG) signals from the muscles of the human upper limb are used as the control interface between the user and a robot arm. A switching regime model is used to decode the EMG activity of 11 muscles to a continuous representation of arm motion in the 3-D space. The switching regime model is used to overcome the main difficulties of the EMG-based control systems, i.e., the nonlinearity of the relationship between the EMG recordings and the arm motion, as well as the nonstationarity of EMG signals with respect to time. The proposed interface allows the user to control in real time an anthropomorphic robot arm in the 3-D space. The efficiency of the method is assessed through real-time experiments of four persons performing random arm motions. PMID:20403787

  1. Modulation of photoacoustic signal generation from metallic surfaces

    PubMed Central

    Mitcham, Trevor; Homan, Kimberly; Frey, Wolfgang; Chen, Yun-Sheng; Emelianov, Stanislav; Hazle, John

    2013-01-01

    Abstract. The ability to image metallic implants is important for medical applications ranging from diagnosis to therapy. Photoacoustic (PA) imaging has been recently pursued as a means to localize metallic implants in soft tissue. The work presented herein investigates different mechanisms to modulate the PA signal generated by macroscopic metallic surfaces. Wires of five different metals are tested to simulate medical implants/tools, while surface roughness is altered or physical vapor deposition (PVD) coatings are added to change the wires’ overall optical absorption. PA imaging data of the wires are acquired at 970 nm. Results indicate that PA signal generation predominately occurs in a wire’s metallic surface and not its aqueous surroundings. PA signal generation is similar for all metals tested, while addition of PVD coatings offers significant modulations (i.e., 4-dB enhancement and 26-dB reduction achieved) in PA signal generation. Results also suggest that PA signal increases with increasing surface roughness. Different coating and roughness schemes are then successfully utilized to generate spatial PA signal patterns. This work demonstrates the potential of surface modifications to enhance or reduce PA signal generation to permit improved PA imaging of implants/tools (i.e., providing location/orientation information) or to allow PA imaging of surrounding tissue. PMID:23652344

  2. 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. PMID:25706722

  3. 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

  4. A Comparison of a Multi-body Model and 3D Kinematics and EMG ofDouble-leg Circle on Pommel Horse

    PubMed Central

    Qian, Jing-guang; Su, Yang; Song, Ya-wei; Qiang, Ye; Zhang, Songning

    2012-01-01

    The purpose of this study was to establish a multi-segment dynamic model in the LifeMOD to examine kinematics of the center of mass and foot, and muscle forces of selected upper extremity muslces during a double-leg circle (DLC) movement on pommel horse in gymnastics and compared with three-dimensional kinematics of the movement and surface electromyographic (sEMG) activity of the muscles. The DLC movement of one elite male gymnast was collected. The three-dimensional (3D) data was imported in the Lifemod to create a full-body human model. A 16-Channel surface electromyography system was used to collect sEMG signals of middle deltoid, biceps brachii, triceps brachii, latissimusdorsi, and pectoralis major. The 3D center of mass and foot displacement showed a good match with the computer simulated results. The muscle force estimations from the model during the four DLC phases were also generally supported by the integrated sEMG results, suggesting that the model was valid. A potential application of this model is to help identify shortcomings of athletes and help establish appropriate training plans errors in the DLC technique during training. PMID:23487347

  5. A Comparison of a Multi-body Model and 3D Kinematics and EMG ofDouble-leg Circle on Pommel Horse.

    PubMed

    Qian, Jing-Guang; Su, Yang; Song, Ya-Wei; Qiang, Ye; Zhang, Songning

    2012-03-01

    The purpose of this study was to establish a multi-segment dynamic model in the LifeMOD to examine kinematics of the center of mass and foot, and muscle forces of selected upper extremity muslces during a double-leg circle (DLC) movement on pommel horse in gymnastics and compared with three-dimensional kinematics of the movement and surface electromyographic (sEMG) activity of the muscles. The DLC movement of one elite male gymnast was collected. The three-dimensional (3D) data was imported in the Lifemod to create a full-body human model. A 16-Channel surface electromyography system was used to collect sEMG signals of middle deltoid, biceps brachii, triceps brachii, latissimusdorsi, and pectoralis major. The 3D center of mass and foot displacement showed a good match with the computer simulated results. The muscle force estimations from the model during the four DLC phases were also generally supported by the integrated sEMG results, suggesting that the model was valid. A potential application of this model is to help identify shortcomings of athletes and help establish appropriate training plans errors in the DLC technique during training. PMID:23487347

  6. 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.

  7. 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. PMID:24187246

  8. Quantitative analysis of surface electromyography: Biomarkers for convulsive seizures.

    PubMed

    Beniczky, Sándor; Conradsen, Isa; Pressler, Ronit; Wolf, Peter

    2016-08-01

    Muscle activity during seizures is in electroencephalographical (EEG) praxis often considered an irritating artefact. This article discusses ways by surface electromyography (EMG) to turn it into a valuable tool of epileptology. Muscles are in direct synaptic contact with motor neurons. Therefore, EMG signals provide direct information about the electric activity in the motor cortex. Qualitative analysis of EMG has traditionally been a part of the long-term video-EEG recordings. Recent development in quantitative analysis of EMG signals yielded valuable information on the pathomechanisms of convulsive seizures, demonstrating that it was different from maximal voluntary contraction, and different from convulsive psychogenic non-epileptic seizures. Furthermore, the tonic phase of the generalised tonic-clonic seizures (GTCS) proved to have different quantitative features than tonic seizures. The high temporal resolution of EMG allowed detailed characterisation of temporal dynamics of the GTCS, suggesting that the same inhibitory mechanisms that try to prevent the build-up of the seizure activity, contribute to ending the seizure. These findings have clinical implications: the quantitative EMG features provided the pathophysiologic substrate for developing neurophysiologic biomarkers that accurately identify GTCS. This proved to be efficient both for seizure detection and for objective, automated distinction between convulsive and non-convulsive epileptic seizures. PMID:27212115

  9. Microprocessor-based simulator of surface ECG signals

    NASA Astrophysics Data System (ADS)

    Martínez, A. E.; Rossi, E.; Siri, L. Nicola

    2007-11-01

    In this work, a simulator of surface electrocardiogram recorded signals (ECG) is presented. The device, based on a microcontroller and commanded by a personal computer, produces an analog signal resembling actual ECGs, not only in time course and voltage levels, but also in source impedance. The simulator is a useful tool for electrocardiograph calibration and monitoring, to incorporate as well in educational tasks and in clinical environments for early detection of faulty behaviour.

  10. Surface wave multipath signals in near-field microwave imaging.

    PubMed

    Meaney, Paul M; Shubitidze, Fridon; Fanning, Margaret W; Kmiec, Maciej; Epstein, Neil R; Paulsen, Keith D

    2012-01-01

    Microwave imaging techniques are prone to signal corruption from unwanted multipath signals. Near-field systems are especially vulnerable because signals can scatter and reflect from structural objects within or on the boundary of the imaging zone. These issues are further exacerbated when surface waves are generated with the potential of propagating along the transmitting and receiving antenna feed lines and other low-loss paths. In this paper, we analyze the contributions of multi-path signals arising from surface wave effects. Specifically, experiments were conducted with a near-field microwave imaging array positioned at variable heights from the floor of a coupling fluid tank. Antenna arrays with different feed line lengths in the fluid were also evaluated. The results show that surface waves corrupt the received signals over the longest transmission distances across the measurement array. However, the surface wave effects can be eliminated provided the feed line lengths are sufficiently long independently of the distance of the transmitting/receiving antenna tips from the imaging tank floor. Theoretical predictions confirm the experimental observations. PMID:22566992

  11. Surface Wave Multipath Signals in Near-Field Microwave Imaging

    PubMed Central

    Meaney, Paul M.; Shubitidze, Fridon; Fanning, Margaret W.; Kmiec, Maciej; Epstein, Neil R.; Paulsen, Keith D.

    2012-01-01

    Microwave imaging techniques are prone to signal corruption from unwanted multipath signals. Near-field systems are especially vulnerable because signals can scatter and reflect from structural objects within or on the boundary of the imaging zone. These issues are further exacerbated when surface waves are generated with the potential of propagating along the transmitting and receiving antenna feed lines and other low-loss paths. In this paper, we analyze the contributions of multi-path signals arising from surface wave effects. Specifically, experiments were conducted with a near-field microwave imaging array positioned at variable heights from the floor of a coupling fluid tank. Antenna arrays with different feed line lengths in the fluid were also evaluated. The results show that surface waves corrupt the received signals over the longest transmission distances across the measurement array. However, the surface wave effects can be eliminated provided the feed line lengths are sufficiently long independently of the distance of the transmitting/receiving antenna tips from the imaging tank floor. Theoretical predictions confirm the experimental observations. PMID:22566992

  12. Motor unit size in muscular dystrophy, a macro EMG and scanning EMG study.

    PubMed Central

    Hilton-Brown, P; Stålberg, E

    1983-01-01

    Patients with muscular dystrophy were investigated with Macro EMG to study activity from whole individual motor units, and with Scanning EMG to study the distribution of activity within the motor unit. Macro motor unit potentials were normal or only slightly reduced in amplitude. In Scanning EMG the units had unchanged mean length compared with normal, but an uneven distribution of the activity. This was also seen in severely weak muscles. The findings are interpreted to be the result of degenerative and regenerative processes, giving rise to remodelling of the motor unit. Images PMID:6655485

  13. The Recovery of Repeated-Sprint Exercise Is Associated with PCr Resynthesis, while Muscle pH and EMG Amplitude Remain Depressed

    PubMed Central

    Mendez-Villanueva, Alberto; Suriano, Rob; Hamer, Peter; Bishop, David

    2012-01-01

    The physiological equivalents of power output maintenance and recovery during repeated-sprint exercise (RSE) remain to be fully elucidated. In an attempt to improve our understanding of the determinants of RSE performance we therefore aimed to determine its recovery following exhaustive exercise (which affected intramuscular and neural factors) concomitantly with those of intramuscular concentrations of adenosine triphosphate [ATP], phosphocreatine [PCr] and pH values and electromyography (EMG) activity (a proxy for net motor unit activity) changes. Eight young men performed 10, 6-s all-out sprints on a cycle ergometer, interspersed with 30 s of recovery, followed, after 6 min of passive recovery, by five 6-s sprints, again interspersed by 30 s of passive recovery. Biopsies of the vastus lateralis were obtained at rest, immediately after the first 10 sprints and after 6 min of recovery. EMG activity of the vastus lateralis was obtained from surface electrodes throughout exercise. Total work (TW), [ATP], [PCr], pH and EMG amplitude decreased significantly throughout the first ten sprints (P<0.05). After 6 min of recovery, TW during sprint 11 recovered to 86.3±7.7% of sprint 1. ATP and PCr were resynthesized to 92.6±6.0% and 85.3±10.3% of the resting value, respectively, but muscle pH and EMG amplitude remained depressed. PCr resynthesis was correlated with TW done in sprint 11 (r = 0.79, P<0.05) and TW done during sprints 11 to 15 (r = 0.67, P<0.05). There was a ∼2-fold greater decrease in the TW/EMG ratio in the last five sprints (sprint 11 to 15) than in the first five sprints (sprint 1 to 5) resulting in a disproportionate decrease in mechanical power (i.e., TW) in relation to EMG. Thus, we conclude that the inability to produce power output during repeated sprints is mostly mediated by intramuscular fatigue signals probably related with the control of PCr metabolism. PMID:23284836

  14. Hand gesture recognition based on surface electromyography.

    PubMed

    Samadani, Ali-Akbar; Kulic, Dana

    2014-01-01

    Human hands are the most dexterous of human limbs and hand gestures play an important role in non-verbal communication. Underlying electromyograms associated with hand gestures provide a wealth of information based on which varying hand gestures can be recognized. This paper develops an inter-individual hand gesture recognition model based on Hidden Markov models that receives surface electromyography (sEMG) signals as inputs and predicts a corresponding hand gesture. The developed recognition model is tested with a dataset of 10 various hand gestures performed by 25 subjects in a leave-one-subject-out cross validation and an inter-individual recognition rate of 79% was achieved. The promising recognition rate demonstrates the efficacy of the proposed approach for discriminating between gesture-specific sEMG signals and could inform the design of sEMG-controlled prostheses and assistive devices. PMID:25570917

  15. Abnormal force--EMG relations in paretic limbs of hemiparetic human subjects.

    PubMed Central

    Tang, A; Rymer, W Z

    1981-01-01

    The relations between surface EMG and isometric force generated by elbow flexor muscles were compared in normal and paretic limbs of 17 hemiparetic human subjects. Similar analyses were performed on both arms of 11 normal subjects. In almost half of the hemiparetic subjects examined (8/17), the slope of the relation between elbow flexion force and surface EMG, measured over the biceps-brachialis and brachioradialis muscle groups was increased in the paretic limb. A mechanism based on anomalous reductions in mean motor unit discharge rate in paretic muscles is advanced as the most likely cause of the findings. PMID:7299407

  16. 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.

  17. 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.

  18. The Response of Hyperkinesis to EMG Biofeedback.

    ERIC Educational Resources Information Center

    Haight, Maryellen J.; And Others

    A study was conducted involving eight hyperkinetic males (11-15 years old) to determine if Ss receiving electromyography (EMG) biofeedback training would show a reduction in frontalis muscle tension, hyperactivity, and lability, and increases in self-esteem and visual and auditory attention span. Individual 45- and 30-minute relaxation exercises…

  19. Comparison of Conventional Filtering and Independent Component Analysis for Artifact Reduction in Simultaneous Gastric EMG and Magnetogastrography From Porcines

    PubMed Central

    Richards, William O.; Bradshaw, L. Alan

    2010-01-01

    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. PMID:19398400

  20. Iridescent flowers? Contribution of surface structures to optical signaling.

    PubMed

    van der Kooi, Casper J; Wilts, Bodo D; Leertouwer, Hein L; Staal, Marten; Elzenga, J Theo M; Stavenga, Doekele G

    2014-07-01

    The color of natural objects depends on how they are structured and pigmented. In flowers, both the surface structure of the petals and the pigments they contain determine coloration. The aim of the present study was to assess the contribution of structural coloration, including iridescence, to overall floral coloration. We studied the reflection characteristics of flower petals of various plant species with an imaging scatterometer, which allows direct visualization of the angle dependence of the reflected light in the hemisphere above the petal. To separate the light reflected by the flower surface from the light backscattered by the components inside (e.g. the vacuoles), we also investigated surface casts. A survey among angiosperms revealed three different types of floral surface structure, each with distinct reflections. Petals with a smooth and very flat surface had mirror-like reflections and petal surfaces with cones yielded diffuse reflections. Petals with striations yielded diffraction patterns when single cells were illuminated. The iridescent signal, however, vanished when illumination similar to that found in natural conditions was applied. Pigmentary rather than structural coloration determines the optical appearance of flowers. Therefore, the hypothesized signaling by flowers with striated surfaces to attract potential pollinators presently seems untenable. PMID:24713039

  1. Unsupervised learning technique for surface electromyogram denoising from power line interference and baseline wander.

    PubMed

    Niegowski, Maciej; Zivanovic, Miroslav; Gomez, Marisol; Lecumberri, Pablo

    2015-08-01

    We present a novel approach to single-channel power line interference (PLI) and baseline wander (BW) removal from surface electromyograms (EMG). It is based on non-negative matrix factorization (NMF) using a priori knowledge about the interferences. It performs a linear decomposition of the input signal spectrogram into non-negative components, which represent the PLI, BW and EMG spectrogram estimates. They all exhibit very different time-frequency patterns: PLI and BW are both sparse whereas EMG is noise-like. Initialization of the classical NMF algorithm with accurately designed PLI, BW and EMG structures and a carefully adjusted matrix decomposition rank increases the separation performance. The comparative study suggests that the proposed method outperforms two state-of-the-art reference methods. PMID:26737971

  2. 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. PMID:25955989

  3. 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

  4. 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. PMID:20051332

  5. 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. PMID:24760921

  6. Evaluation of Novel EMG Biofeedback for Postural Correction During Computer Use.

    PubMed

    Gaffney, Brecca M; Maluf, Katrina S; Davidson, Bradley S

    2016-06-01

    Postural correction is an effective rehabilitation technique used to treat chronic neck and shoulder pain, and is aimed toward reducing the load on the surrounding muscles by adopting a neutral posture. The objective of this investigation was to evaluate the effectiveness of real-time high-density surface EMG (HDsEMG) biofeedback for postural correction during typing. Twenty healthy participants performed a typing task with two forms of postural feedback: (1) verbal postural coaching and (2) verbal postural coaching plus HDsEMG biofeedback. The interface used activity from two HDsEMG arrays placed over the trapezius designed to shift trapezius muscle activity inferiorly. The center of gravity across both arrays was used to quantify the spatial distribution of trapezius activity. Planar angles taken from upper extremity reflective markers quantified cervicoscapular posture. During the biofeedback condition, trapezius muscle activity was located 12.74 ± 3.73 mm more inferior, the scapula was 2.58 ± 1.18° more adducted and 0.23 ± 0.24° more depressed in comparison to verbal postural coaching alone. The results demonstrate the short-term effectiveness of a real-time HDsEMG biofeedback intervention to achieve postural correction, and may be more effective at creating an inferior shift in trapezius muscle activity in comparison to verbal postural coaching alone. PMID:26718205

  7. 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. PMID:26059538

  8. A Wireless sEMG Recording System and Its Application to Muscle Fatigue Detection

    PubMed Central

    Chang, Kang-Ming; Liu, Shin-Hong; Wu, Xuan-Han

    2012-01-01

    Surface electromyography (sEMG) is an important measurement for monitoring exercise and fitness. Because if its high sampling frequency requirement, wireless transmission of sEMG data is a challenge. In this article a wireless sEMG measurement system with a sampling frequency of 2 KHz is developed based upon a MSP 430 microcontroller and Bluetooth transmission. Standard isotonic and isometric muscle contraction are clearly represented in the receiving user interface. Muscle fatigue detection is an important application of sEMG. Traditional muscle fatigue is detected from the median frequency of the sEMG power spectrum. The regression slope of the linear regression of median frequency is an important muscle fatigue index. A more negative slope value represents a higher muscle fatigue condition. To test the system performance, muscle fatigue detection was examined by having subjects run on a pedaled-multifunctional elliptical trainer for approximately 30 minutes at three loading levels. Ten subjects underwent a total of 60 exercise sessions to provide the experimental data. Results showed that the regression slope gradually decreases as expected, and there is a significant gender difference. PMID:22368481

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

    PubMed Central

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

    2010-01-01

    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. PMID:20051332

  10. Surface sensing and signaling networks in plant pathogenic fungi.

    PubMed

    Kou, Yanjun; Naqvi, Naweed I

    2016-09-01

    Pathogenic fungi have evolved highly varied and remarkable strategies to invade and infect their plant hosts. Typically, such fungal pathogens utilize highly specialized infection structures, morphologies or cell types produced from conidia or ascospores on the cognate host surfaces to gain entry therein. Such diverse infection strategies require intricate coordination in cell signaling and differentiation in phytopathogenic fungi. Here, we present an overview of our current understanding of cell signaling and infection-associated development that primes host penetration in the top ten plant pathogenic fungi, which utilize specific receptors to sense and respond to different surface cues, such as topographic features, hydrophobicity, hardness, plant lipids, phytohormones, and/or secreted enzymes. Subsequently, diverse signaling components such as G proteins, cyclic AMP/Protein Kinase A and MAP kinases are activated to enable the differentiation of infection structures. Recent studies have also provided fascinating insights into the spatio-temporal dynamics and specialized sequestration and trafficking of signaling moieties required for proper development of infection structures in phytopathogenic fungi. Molecular insight in such infection-related morphogenesis and cell signaling holds promise for identifying novel strategies for intervention of fungal diseases in plants. PMID:27133541

  11. Surface electromyogram analysis of the direction of isometric torque generation by the first dorsal interosseous muscle

    NASA Astrophysics Data System (ADS)

    Zhou, Ping; Suresh, Nina L.; Zev Rymer, William

    2011-06-01

    The objective of this study was to determine whether a novel technique using high density surface electromyogram (EMG) recordings can be used to detect the directional dependence of muscle activity in a multifunctional muscle, the first dorsal interosseous (FDI). We used surface EMG recordings with a two-dimensional electrode array to search for inhomogeneous FDI activation patterns with changing torque direction at the metacarpophalangeal joint, the locus of action of the FDI muscle. The interference EMG distribution across the whole FDI muscle was recorded during isometric contraction at the same force magnitude in five different directions in the index finger abduction-flexion plane. The electrode array EMG activity was characterized by contour plots, interpolating the EMG amplitude between electrode sites. Across all subjects the amplitude of the flexion EMG was consistently lower than that of the abduction EMG at the given force. Pattern recognition methods were used to discriminate the isometric muscle contraction tasks with a linear discriminant analysis classifier, based on the extraction of two different feature sets of the surface EMG signal: the time domain (TD) feature set and a combination of autoregressive coefficients and the root mean square amplitude (AR+RMS) as a feature set. We found that high accuracies were obtained in the classification of different directions of the FDI muscle isometric contraction. With a monopolar electrode configuration, the average overall classification accuracy from nine subjects was 94.1 ± 2.3% for the TD feature set and 95.8 ± 1.5% for the AR+RMS feature set. Spatial filtering of the signal with bipolar electrode configuration improved the average overall classification accuracy to 96.7 ± 2.7% for the TD feature set and 98.1 ± 1.6% for the AR+RMS feature set. The distinct EMG contour plots and the high classification accuracies obtained from this study confirm distinct interference EMG pattern distributions as a

  12. Intention-based EMG control for powered exoskeletons.

    PubMed

    Lenzi, T; De Rossi, S M M; Vitiello, N; Carrozza, M C

    2012-08-01

    Electromyographical (EMG) signals have been frequently used to estimate human muscular torques. In the field of human-assistive robotics, these methods provide valuable information to provide effectively support to the user. However, their usability is strongly limited by the necessity of complex user-dependent and session-dependent calibration procedures, which confine their use to the laboratory environment. Nonetheless, an accurate estimate of muscle torque could be unnecessary to provide effective movement assistance to users. The natural ability of human central nervous system of adapting to external disturbances could compensate for a lower accuracy of the torque provided by the robot and maintain the movement accuracy unaltered, while the effort is reduced. In order to explore this possibility, in this paper we study the reaction of ten healthy subjects to the assistance provided through a proportional EMG control applied by an elbow powered exoskeleton. This system gives only a rough estimate of the user muscular torque but does not require any specific calibration. Experimental results clearly show that subjects adapt almost instantaneously to the assistance provided by the robot and can reduce their effort while keeping full control of the movement under different dynamic conditions (i.e., no alterations of movement accuracy are observed). PMID:22588573

  13. 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. PMID:18002368

  14. Muscle synergy control model-tuned EMG driven torque estimation system with a musculo-skeletal model.

    PubMed

    Min, Kyuengbo; Shin, Duk; Lee, Jongho; Kakei, Shinji

    2013-01-01

    Muscle activity is the final signal for motion control from the brain. Based on this biological characteristic, Electromyogram (EMG) signals have been applied to various systems that interface human with external environments such as external devices. In order to use EMG signals as input control signal for this kind of system, the current EMG driven torque estimation models generally employ the mathematical model that estimates the nonlinear transformation function between the input signal and the output torque. However, these models need to estimate too many parameters and this process cause its estimation versatility in various conditions to be poor. Moreover, as these models are designed to estimate the joint torque, the input EMG signals are tuned out of consideration for the physiological synergetic contributions of multiple muscles for motion control. To overcome these problems of the current models, we proposed a new tuning model based on the synergy control mechanism between multiple muscles in the cortico-spinal tract. With this synergetic tuning model, the estimated contribution of multiple muscles for the motion control is applied to tune the EMG signals. Thus, this cortico-spinal control mechanism-based process improves the precision of torque estimation. This system is basically a forward dynamics model that transforms EMG signals into the joint torque. It should be emphasized that this forward dynamics model uses a musculo-skeletal model as a constraint. The musculo-skeletal model is designed with precise musculo-skeletal data, such as origins and insertions of individual muscles or maximum muscle force. Compared with the mathematical model, the proposed model can be a versatile model for the torque estimation in the various conditions and estimates the torque with improved accuracy. In this paper, we also show some preliminary experimental results for the discussion about the proposed model. PMID:24110476

  15. 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

  16. The effectiveness of FES-evoked EMG potentials to assess muscle force and fatigue in individuals with spinal cord injury.

    PubMed

    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

  17. Surface code—biophysical signals for apoptotic cell clearance

    NASA Astrophysics Data System (ADS)

    Biermann, Mona; Maueröder, Christian; Brauner, Jan M.; Chaurio, Ricardo; Janko, Christina; Herrmann, Martin; Muñoz, Luis E.

    2013-12-01

    Apoptotic cell death and the clearance of dying cells play an important and physiological role in embryonic development and normal tissue turnover. In contrast to necrosis, apoptosis proceeds in an anti-inflammatory manner. It is orchestrated by the timed release and/or exposure of so-called ‘find-me’, ‘eat me’ and ‘tolerate me’ signals. Mononuclear phagocytes are attracted by various ‘find-me’ signals, including proteins, nucleotides, and phospholipids released by the dying cell, whereas the involvement of granulocytes is prevented via ‘stay away’ signals. The exposure of anionic phospholipids like phosphatidylserine (PS) by apoptotic cells on the outer leaflet of the plasma membrane is one of the main ‘eat me’ signals. PS is recognized by a number of innate receptors as well as by soluble bridging molecules on the surface of phagocytes. Importantly, phagocytes are able to discriminate between viable and apoptotic cells both exposing PS. Due to cytoskeleton remodeling PS has a higher lateral mobility on the surfaces of apoptotic cells thereby promoting receptor clustering on the phagocyte. PS not only plays an important role in the engulfment process, but also acts as ‘tolerate me’ signal inducing the release of anti-inflammatory cytokines by phagocytes. An efficient and fast clearance of apoptotic cells is required to prevent secondary necrosis and leakage of intracellular danger signals into the surrounding tissue. Failure or prolongation of the clearance process leads to the release of intracellular antigens into the periphery provoking inflammation and development of systemic inflammatory autoimmune disease like systemic lupus erythematosus. Here we review the current findings concerning apoptosis-inducing pathways, important players of apoptotic cell recognition and clearance as well as the role of membrane remodeling in the engulfment of apoptotic cells by phagocytes.

  18. 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. PMID:26841414

  19. Muscle fluid shift does not alter EMG global variables during sustained isometric actions.

    PubMed

    von Walden, Ferdinand; Pozzo, Marco; Elman, Ted; Tesch, Per A

    2008-10-01

    Body fluid redistribution occurs in astronauts traveling in space, potentially altering interstitial water content and hence impedance. This in turn may impact the features of electromyographic (EMG) signals measured to compare in-flight muscle function with pre- and post-flight conditions. Thus, the current study aimed at investigating the influence of similar fluid shifts on EMG spectral variables during muscle contractile activity. Ten men performed sustained isometric actions (120 s) at 20% and 60% of maximum voluntary contraction (MVC) following 1-h rest in the vertical or supine position. From single differential EMG signals, recorded from the soleus (SOL), the medial (MG) and lateral (LG) gastrocnemius muscles, initial value and rate of change over time (slope) of mean power frequency (MNF) and average rectified value (ARV) were assessed. MNF initial value showed dependence on muscle (P<0.01), but was unaffected by body tilt. MNF rate of change increased (P<0.001) with increased force and differed across muscles (P<0.05), but was not influenced (P=0.85) by altered body position. Thus, fluid shift resulting from vertical to supine tilt had no impact on myoelectrical manifestations of muscle fatigue. Furthermore, since such alteration of body fluid distribution resembles that occurring in microgravity, our findings suggest this may not be a methodological limitation, when comparing EMG fatigue indices on Earth versus in space. PMID:17466537

  20. EMG-based neuro-fuzzy control of a 4DOF upper-limb power-assist exoskeleton.

    PubMed

    Kiguchi, Kazuo; Imada, Yasunobu; Liyanage, Manoj

    2007-01-01

    We have been developing a 4DOF exoskeleton robot system in order to assist shoulder vertical motion, shoulder horizontal motion, elbow motion, and forearm motion of physically weak persons such as elderly, injured, or disabled persons. The robot is directly attached to a user's body and activated based on EMG (Electromyogram) signals of the user's muscles, since the EMG signals directly reflect the user's motion intention. A neuro-fuzzy controller has been applied to control the exoskeleton robot system. In this paper, controller adaptation method to user's EMG signals is proposed. A motion indicator is introduced to indicate the motion intention of the user for the controller adaptation. The experimental results show the effectiveness of the proposed method. PMID:18002635

  1. Suppression of EMG activity by subthreshold paired-pulse transcranial magnetic stimulation to the leg motor cortex.

    PubMed

    Roy, François D

    2009-03-01

    Cortical activity driving a voluntary muscle contraction is inhibited by very low-intensity transcranial magnetic stimulation (TMS) and is reflected in the suppression of the average rectified EMG. This approach offers a method to test the contribution of cortical neurons actively involved in a motor task, but requires a large number of stimuli (approximately 100) to suitably depress the average EMG. Here, we investigated whether two pulses of subthreshold TMS at interstimulus intervals (ISIs) ranging between 1 and 12 ms could enhance the amount of EMG suppression in the tibialis anterior muscle compared to a single pulse. Pairs of subthreshold TMS at an ISI of 7 ms produced the maximum EMG suppression that was 42% more than the inhibition elicited using a single pulse. In addition, the signal-to-noise ratio of the TMS-induced suppression was further increased by a second pulse, delivered 7 ms later. The reduction in the EMG at the 7 ms paired-pulse interval occurred without any short-latency excitation suggesting that the two stimuli increased the activation of cortical inhibitory neurons. Subthreshold paired-pulse TMS at ISIs of 1-3 ms was prone to EMG excitation in the period that immediately preceded the inhibition and is consistent with the recruitment of short-interval intracortical facilitation (SICF). We propose that pairs of subthreshold TMS outside the range of SICF with an inter-pulse interval of 7 ms is optimal to inhibit ongoing cortical activity during human motor movement. PMID:19183971

  2. 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

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

    PubMed

    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

  4. 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.

  5. 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

  6. Using evoked EMG as a synthetic force sensor of isometric electrically stimulated muscle.

    PubMed

    Erfanian, A; Chizeck, H J; Hashemi, R M

    1998-02-01

    A method for the estimation of the force generated by electrically stimulated muscle during isometric contraction is developed here. It is based upon measurements of the evoked electromyogram (EMG) [EEMG] signal. Muscle stimulation is provided to the quadriceps muscle of a paralyzed human subject using percutaneous intramuscular electrodes, and EEMG signals are collected using surface electrodes. Through the use of novel signal acquisition and processing techniques, as well as a mathematical model that reflects both the excitation and activation phenomena involved in isometric muscle force generation, accurate prediction of stimulated muscle forces is obtained for large time horizons. This approach yields synthetic muscle force estimates for both unfatigued and fatigued states of the stimulated muscle. In addition, a method is developed that accomplishes automatic recalibration of the model to account for day-to-day changes in pickup electrode mounting as well as other factors contributing to EEMG gain variations. It is demonstrated that the use of the measured EEMG as the input to a predictive model of muscle torque generation is superior to the use of the electrical stimulation signal as the model input. This is because the measured EEMG signal captures all of the neural excitation, whereas stimulation-to-torque models only reflect that portion of the neural excitation that results directly from stimulation. The time-varying properties of the excitation process cannot be captured by existing stimulation-to-torque models, but they are tracked by the EEMG-to-torque models that are developed here. This work represents a promising approach to the real-time estimation of stimulated muscle force in functional neuromuscular stimulation applications. PMID:9473842

  7. 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.

  8. 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

  9. Auditory Evaluation of Sound Signals Radiated by a Vibrating Surface

    NASA Astrophysics Data System (ADS)

    MEUNIER, S.; HABAULT, D.; CANÉVET, G.

    2001-11-01

    This paper presents a combination of vibroacoustic and psychoacoustic studies of sounds radiated by a vibrating structure. The calculated sound field is the sound pressure radiated by a baffled thin-plate structure immersed in a fluid, on the surface of which the acceleration is given. Various configurations are selected for the time and space functions of the acceleration variable, each configuration leading to a particular acoustic signal (a low-frequency tone complex in our case). These signals are then transformed into sound files, which are used as test signals in psychoacoustic experiments for assessing their perceptual attributes and quality. Two experiments were run. In the first one, the unpleasantness of a series of signals at different levels was measured by direct estimation and compared with their calculated loudness and sharpness using Zwicker's model. The same measurements were repeated with the signals set to the same maximum amplitude. In the second experiment, the pleasantness of another series of sounds at equal loudness was measured, as well as dissimilarity and preference on pairs of these sounds. An MDS analysis was run to extract auditory attributes that could account for the perceived differences between sounds and correlate with the estimated pleasantness. The results from the first experiment show that pleasantness is always highly (and negatively) correlated with loudness. The same holds for sharpness, when sounds are played at the same maximum amplitude. The second experiment shows that the perceptual attributes revealed by the MDS analysis are related to pitch and timbre, the latter being highly correlated with pleasantness. Overall, this study confirms the interest of extending vibroacoustic studies to a more complete “psychomechanical” investigation of the whole process of sound generation. It is suggested that such investigations may apply to product sound quality and to active or passive noise control, by providing

  10. Peak and average rectified EMG measures: which method of data reduction should be used for assessing core training exercises?

    PubMed

    Hibbs, A E; Thompson, K G; French, D N; Hodgson, D; Spears, I R

    2011-02-01

    Core strengthening and stability exercises are fundamental for any conditioning training program. Although surface electromyography (sEMG) is used to quantify muscle activity there is a lack of research using this method to investigate the core musculature and core stability. Two types of data reduction are commonly used for sEMG; peak and average rectified EMG methods. Peak EMG has been infrequently reported in the literature with regard to the assessment of core training while even fewer studies have incorporated average rectified EMG data (ARV). The aim of the study was to establish the repeatability of peak and average rectified EMG data during core training exercises and their interrelationship. Ten male highly trained athletes (inter-subject repeatability group; age, 18 ± 1.2 years; height, 176.5 ± 3.2 cm; body mass, 71 ± 4.5 kg) and one female highly trained athlete (intra-subject repeatability group; age; 27 years old; height; 180 cm; weight; 53 kg) performed five maximal voluntary isometric contractions (MVIC) and five core exercises, chosen to represent a range of movement and muscle recruitment patterns. Peak EMG and ARV EMG were calculated for eight core muscles (rectus abdominis, RA; external oblique, EO; internal oblique, IO; multifidis, MF; latissimus dorsi, LD; longissimus, LG; gluteus maximus, GM; rectus femoris, RF) using sEMG. Average coefficient of variation (CV%) for peak EMG across all the exercises and muscles was 45%. This is in comparison to 35% for the ARV method, which was found to be a significant difference (P<0.05), therefore implying that the ARV method is the more reliable measure for these types of exercise. Analysis of the inter-subject and intra-subject CV% values suggest that these exercises and muscles are sufficiently repeatable using sEMG. Five muscles were highly correlated (R>0.70; RA, EO, MF, GM, LG) between peak and ARV EMG suggesting, that for these core muscles, the two methods provide a similar evaluation of muscle