Portable EMG Data Acquisition Module for Upper Limb Prosthesis Application. Pancholi, S. & Joshi, A. IEEE Sensors Journal, 2018. doi abstract bibtex © 2001-2012 IEEE. Electromyography (EMG) signals are gaining popularity to develop the prosthetics. In this paper, an efficient multi-channel EMG signal acquisition system has been proposed for upper limb prosthetic application. Various arm exercises have been performed to obtain EMG signals from five different arm muscles for the validation of developed hardware. The muscle's position has been selected by palpation method. Furthermore, the classification algorithms have been examined for seven different activities. Total 29 subjects have been chosen (25 intact and four Amputees) to acquire the EMG data by these activities. To classify the recorded EMG data set, nine time domain and seven frequency domain features have been extracted. A comparative analysis of different classifiers is presented for different muscle position of electrodes. The signal processing and classification algorithms have been processed in MATLAB 2016a. The accuracy of classification ranges for different classification algorithms from 57.69% to 99.92% for all subjects.
@article{
title = {Portable EMG Data Acquisition Module for Upper Limb Prosthesis Application},
type = {article},
year = {2018},
keywords = {Acquisition,LDA,QDA,SVM,classification,k-NN,prosthetic,sEMG},
volume = {18},
id = {979e07be-7bc9-366d-9279-790b19fee2d0},
created = {2018-09-06T11:22:39.817Z},
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profile_id = {11ae403c-c558-3358-87f9-dadc957bb57d},
last_modified = {2018-09-06T11:22:39.817Z},
read = {false},
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authored = {true},
confirmed = {false},
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abstract = {© 2001-2012 IEEE. Electromyography (EMG) signals are gaining popularity to develop the prosthetics. In this paper, an efficient multi-channel EMG signal acquisition system has been proposed for upper limb prosthetic application. Various arm exercises have been performed to obtain EMG signals from five different arm muscles for the validation of developed hardware. The muscle's position has been selected by palpation method. Furthermore, the classification algorithms have been examined for seven different activities. Total 29 subjects have been chosen (25 intact and four Amputees) to acquire the EMG data by these activities. To classify the recorded EMG data set, nine time domain and seven frequency domain features have been extracted. A comparative analysis of different classifiers is presented for different muscle position of electrodes. The signal processing and classification algorithms have been processed in MATLAB 2016a. The accuracy of classification ranges for different classification algorithms from 57.69% to 99.92% for all subjects.},
bibtype = {article},
author = {Pancholi, S. and Joshi, A.M.},
doi = {10.1109/JSEN.2018.2809458},
journal = {IEEE Sensors Journal},
number = {8}
}
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