The Muscle Activity Detection from Surface EMG Signal Using the Morphological Filter

2012 ◽  
Vol 195-196 ◽  
pp. 1137-1141
Author(s):  
Qiang Li ◽  
Bo Li
Keyword(s):  
Muscle Activity ◽  
Interpolation Method ◽  
Surface Emg ◽  
Band Pass Filter ◽  
Morphological Filter ◽  
Activity Detection ◽  
Semg Signal ◽  
Pass Filter ◽  
Noise Interference ◽  
Emg Signal

For the recognition of action sEMG signal, the muscle activity detection is the elementary work, and the morphological filter was explored to achieve the target in this paper. To reduce the noise interference in the collected sEMG signal, the band-pass filter and spectrum interpolation method were applied. Based on two structuring elements, the morphological filter was utilized to separate the action signal from the background signal. Then, the amplitude envelope which could indicate the muscle activity was acquired. The experimental results showed that the satisfying muscle activity detection performance could be implemented by the morphological filter.

scholarly journals An Electromyographic Study of the Hip Abductor Muscles as Subjects With a Hip Prosthesis Walked With Different Methods of Using a Cane and Carrying a Load

1999 ◽  
Vol 79 (12) ◽  
pp. 1163-1173 ◽  
Author(s):  
Donald A Neumann
Keyword(s):  
Body Weight ◽  
Muscle Activity ◽  
Hip Prosthesis ◽  
Load Condition ◽  
Surface Emg ◽  
Emg Activity ◽  
Emg Signal ◽  
Hip Abductor ◽  
Load Conditions

Abstract Background and Purpose. Certain methods of carrying handheld loads or using a cane can reduce the demands placed on the hip abductor (HA) muscles and the loads on the underlying prosthetic hip. In certain conditions, unusually large forces from the HA muscles may contribute to premature loosening of a prosthetic hip. The purpose of this study was to examine HA use by measuring the amplitude of the electromyographic (EMG) signal from the HA muscles as subjects carried a load and simultaneously used a cane. Subjects. Twenty-four active subjects (mean age=63.3 years, SD=10.7, range=40–86) with a unilateral prosthetic hip were tested. Methods. The HA muscle surface EMG activity was analyzed as subjects carried loads weighing 5%, 10%, or 15% of body weight held by either their contralateral or ipsilateral arm relative to their prosthetic hip. They simultaneously used a cane with their free hand. Results. The contralateral cane and ipsilateral load conditions produced HA muscle EMG activity that was approximately 40% less than the EMG activity produced while walking without carrying a load or using a cane. Conclusion and Discussion. People who are in danger of premature loosening of their prosthetic hip should, if possible, avoid carrying loads. If a load must be carried, however, then the contralateral cane and ipsilateral load condition appears to minimize the loads placed on the prosthetic hip due to HA muscle activity.

scholarly journals Decomposition of surface EMG signals from cyclic dynamic contractions

2015 ◽  
Vol 113 (6) ◽  
pp. 1941-1951 ◽  
Author(s):  
Carlo J. De Luca ◽  
Shey-Sheen Chang ◽  
Serge H. Roy ◽  
Joshua C. Kline ◽  
S. Hamid Nawab
Keyword(s):  
Motor Units ◽  
Surface Emg ◽  
Machine Learning Algorithms ◽  
Isometric Contractions ◽  
Semg Signal ◽  
Emg Signal ◽  
Verification Methods ◽  
Flexion Extension ◽  
Dynamic Contractions ◽  
Cyclic Contractions

Over the past 3 decades, various algorithms used to decompose the electromyographic (EMG) signal into its constituent motor unit action potentials (MUAPs) have been reported. All are limited to decomposing EMG signals from isometric contraction. In this report, we describe a successful approach to decomposing the surface EMG (sEMG) signal collected from cyclic (repeated concentric and eccentric) dynamic contractions during flexion/extension of the elbow and during gait. The increased signal complexity introduced by the changing shapes of the MUAPs due to relative movement of the electrodes and the lengthening/shortening of muscle fibers was managed by an incremental approach to enhancing our established algorithm for decomposing sEMG signals obtained from isometric contractions. We used machine-learning algorithms and time-varying MUAP shape discrimination to decompose the sEMG signal from an increasingly challenging sequence of pseudostatic and dynamic contractions. The accuracy of the decomposition results was assessed by two verification methods that have been independently evaluated. The firing instances of the motor units had an accuracy of ∼90% with a MUAP train yield as high as 25. Preliminary observations from the performance of motor units during cyclic contractions indicate that during repetitive dynamic contractions, the control of motor units is governed by the same rules as those evidenced during isometric contractions. Modifications in the control properties of motoneuron firings reported by previous studies were not confirmed. Instead, our data demonstrate that the common drive and hierarchical recruitment of motor units are preserved during concentric and eccentric contractions.

scholarly journals A Control System of SEMG Signal Based on Deep Learning

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Deep Learning ◽  
Control System ◽  
Training Model ◽  
Surface Emg ◽  
Cross Combination ◽  
Semg Signal ◽  
Engineering Technology ◽  
Emg Signal ◽  
Successful Design ◽  
Medical Theory

The research of control system based on sEMG signal is a popular field at present. It collects bioelectricity of human body through surface electrode. It has the new characteristic of subject fusion, and it is the combination of engineering technology and medical theory, specifically the application of cross combination of control science and electrophysiology. In this paper, the human surface EMG signal is taken as the research object, and a manipulator control system based on one-dimensional convolutional neural network (CNN) is proposed, and the functions and implementation methods of each part of the system are analyzed. The experimental results show that the recognition accuracy of the training model is 0.973, and the design scheme of EMG signal recognition and classification system with deep learning method is feasible. The successful design of the system provides technical support and theoretical basis for the further study of electrophysiological signals.

FATIGUE CONTRACTION ANALYSIS USING EMPIRICAL MODE DECOMPOSITION AND WAVELET TRANSFORM

2015 ◽  
Vol 77 (6) ◽  
Author(s):  
Rubana Haque Chowdhury ◽  
Mamun Bin Ibne Reaz
Keyword(s):  
Wavelet Transform ◽  
Muscle Fatigue ◽  
Empirical Mode Decomposition ◽  
Surface Emg ◽  
Semg Signal ◽  
Emg Signal ◽  
Mode Decomposition ◽  
Spectral Variable ◽  
Walking Exercise ◽  
The Difference

Muscle fatigue is a long lasting reduction of the ability to contract and it is the condition when produced force is reduced. Walking fast can cause muscle fatigue, which is unhealthy and it is incurable when the level of fatigue is high. Muscle fatigue during walk can be determined using several spectral variables. The amplitude and frequency of the surface EMG signal provide a more accurate reflection of motor unit pattern among these spectral variables. This research reports on the effectiveness of Empirical mode decomposition (EMD) and wavelet transform based filtering method applied to the surface EMG (sEMG) signal as a means of achieving reliable discrimination of the muscle fatigue during human walking exercise. In this research, IAV, RMS and AIF values were used as spectral variable. These spectral variables extensively identifies the difference between fatigue and normal muscle when using EMD method compared with other different wavelet functions (WFs). The result shows that the sEMG amplitude and frequency momentously changes from rest position to maximum contraction position.

Classification of Surface Electromyogram Signals Acquired from the Forearm of a Healthy Volunteer

2016 ◽  
pp. 315-333
Author(s):  
Uvanesh K. ◽  
Suraj Kumar Nayak ◽  
Biswajeet Champaty ◽  
Goutam Thakur ◽  
Biswajit Mohapatra ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Emg ◽  
Acquisition System ◽  
Discrete Wavelet ◽  
Signal Acquisition ◽  
Semg Signal ◽  
Emg Signal ◽  
Flexor Carpi Ulnaris ◽  
Artificial Neural Network Ann

Surface EMG (sEMG) signals from the palmaris longus, flexor carpi radialis and flexor carpi ulnaris muscles were recorded using an in-house developed EMG signal acquisition system. The bandwidth of the acquisition system was 1500 Hz. The extracted sEMG signal was processed using Discrete Wavelet Transform (DWT). The features of the extracted and the wavelet processed signals were determined and were used for probable classification using Artificial Neural Network (ANN). A classification efficiency of more than 90% was achieved using ANN classifiers. The results suggested that the sEMG may be successfully used for designing efficient control system.

scholarly journals Dynamic Gesture Recognition Using Surface EMG Signals Based on Multi-Stream Residual Network

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhiwen Yang ◽  
Du Jiang ◽  
Ying Sun ◽  
Bo Tao ◽  
Xiliang Tong ◽  
...  
Keyword(s):  
Human Computer Interaction ◽  
Gesture Recognition ◽  
Hand Movement ◽  
Surface Emg ◽  
Residual Network ◽  
Semg Signal ◽  
Essential Information ◽  
Emg Signal ◽  
Dynamic Gesture Recognition ◽  
Computer Interaction

Gesture recognition technology is widely used in the flexible and precise control of manipulators in the assisted medical field. Our MResLSTM algorithm can effectively perform dynamic gesture recognition. The result of surface EMG signal decoding is applied to the controller, which can improve the fluency of artificial hand control. Much current gesture recognition research using sEMG has focused on static gestures. In addition, the accuracy of recognition depends on the extraction and selection of features. However, Static gesture research cannot meet the requirements of natural human-computer interaction and dexterous control of manipulators. Therefore, a multi-stream residual network (MResLSTM) is proposed for dynamic hand movement recognition. This study aims to improve the accuracy and stability of dynamic gesture recognition. Simultaneously, it can also advance the research on the smooth control of the Manipulator. We combine the residual model and the convolutional short-term memory model into a unified framework. The architecture extracts spatiotemporal features from two aspects: global and deep, and combines feature fusion to retain essential information. The strategy of pointwise group convolution and channel shuffle is used to reduce the number of network calculations. A dataset is constructed containing six dynamic gestures for model training. The experimental results show that on the same recognition model, the gesture recognition effect of fusion of sEMG signal and acceleration signal is better than that of only using sEMG signal. The proposed approach obtains competitive performance on our dataset with the recognition accuracies of 93.52%, achieving state-of-the-art performance with 89.65% precision on the Ninapro DB1 dataset. Our bionic calculation method is applied to the controller, which can realize the continuity of human-computer interaction and the flexibility of manipulator control.

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