Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks
Abstract
:1. Introduction
2. Materials and Methods
2.1. Participants
2.2. Equipment and Software
2.3. Experimental Setup and Data Acquisition
2.4. Data Preprocessing
2.5. Feature Extraction
2.6. Modeling
2.7. Statistical Analysis
3. Results
3.1. Classifier Assessment
3.2. Statistical Comparison of the Performances of the Machine Learning Methods
3.3. Confusion Matrices of ANN-Based Classifiers
3.4. Performance Comparison of ANN-Based Classifiers According to Feature Combinations
3.5. Estimation of Real-Time Performance Using ANN-Based Classifiers
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
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Time-Domain Features | Formula |
---|---|
Root mean square (RMS) | |
Variance (VAR) | |
Mean absolute value (MAV) | |
Slop sign change (SSC) | |
Zero crossing (ZC) | |
Waveform length (WL) |
Subject | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Method | Parameter | #1 | #2 | #3 | #4 | #5 | #6 | #7 | #8 | #9 | #10 |
ANN | Number of hidden layers | 4 | 3 | 4 | 4 | 3 | 4 | 3 | 4 | 4 | 4 |
Number of neurons | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | 1000 | |
Dropout rate | 0.3 | 0.3 | 0.3 | 0.3 | 0.2 | 0.3 | 0.3 | 0.3 | 0.3 | 0.3 | |
Batch normalization | applied | applied | applied | applied | applied | applied | applied | applied | applied | applied | |
SVM | C | 10 | 10 | 100 | 100 | 100 | 100 | 10 | 100 | 100 | 100 |
Gamma | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | |
Kernel | rbf | rbf | rbf | rbf | rbf | rbf | rbf | rbf | rbf | rbf | |
RF | Number of trees | 1000 | 1000 | 1000 | 1000 | 500 | 1000 | 500 | 1000 | 1000 | 1000 |
Class weight | BAL | BAL | none | BAL | none | BAL | none | none | none | none | |
LR | Penalty | L2 | none | none | none | none | L2 | none | none | none | L2 |
C | 1 | 1 | 1 | 0.1 | 1 | 1 | 0.001 | 1 | 1 | 1 | |
Class weight | none | BAL | none | none | none | BAL | None | BAL | none | none | |
Solver | lbfgs | saga | lbfgs | saga | lbfgs | lbfgs | saga | lbfgs | lbfgs | lbfgs |
#1 | #2 | #3 | #4 | #5 | #6 | #7 | #8 | #9 | #10 | Mean | 95% CI | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
ANN | 0.947 | 0.939 | 0.941 | 0.927 | 0.952 | 0.928 | 0.942 | 0.942 | 0.935 | 0.944 | 0.940 | 0.935–0.945 |
SVM | 0.898 | 0.866 | 0.857 | 0.824 | 0.905 | 0.856 | 0.883 | 0.886 | 0.866 | 0.899 | 0.874 | 0.858–0.890 |
RF | 0.818 | 0.804 | 0.817 | 0.779 | 0.878 | 0.791 | 0.861 | 0.865 | 0.849 | 0.847 | 0.831 | 0.809–0.853 |
LR | 0.520 | 0.454 | 0.515 | 0.435 | 0.672 | 0.442 | 0.611 | 0.600 | 0.502 | 0.639 | 0.539 | 0.483–0.595 |
ANN | SVM | RF | |
---|---|---|---|
SVM | 0.003 | - | - |
RF | <0.001 | 0.386 | - |
LR | <0.001 | 0.002 | 0.012 |
Feature Combination | Mean Accuracy ± SD |
---|---|
ZC/SSC/WL | 0.884 ± 0.028 |
ZC/SSC/WL + MAV | 0.926 ± 0.012 |
ZC/SSC/WL + RMS | 0.930 ± 0.011 |
ZC/SSC/WL + VAR | 0.921 ± 0.011 |
ZC/SSC/WL + MAV+ RMS | 0.938 ± 0.011 |
ZC/SSC/WL + MAV + VAR | 0.934 ± 0.009 |
ZC/SSC/WL + RMS + VAR | 0.933 ± 0.011 |
ZC/SSC/WL + MAV + RMS + VAR | 0.940 ± 0.008 |
Reference | Number of Subjects | Feature Types | Number of Features | Number of Gestures (NG) | Number of Channels (NCh) | NG/NCh | Window Length | ML Method | Accuracy |
---|---|---|---|---|---|---|---|---|---|
Palkowski & Redlarski, 2016 [12] | N/A | TD | 6 | 6 (2 W + 2 WH + 2 IF) | 2 | 3 | N/A | SVM | 0.981 |
Fu et al. 2017 [13] | 5 | TD-AR | 65 | 8 (8 IF) | 6 | 1.33 | 125 ms | PNN | 0.922 |
Shi et al., 2018 [14] | 13 | TD | 8 | 4 (WH + 3 IF) | 2 | 2 | 250 ms | KNN | 0.938 |
Sharma & Gupta, 2018 [15] | 4 | TD, FD | 33 | 9 (8 IF + R) | 3 | 3 | 125 ms | SVM | 0.901 |
Qi et al., 2020 [16] | N/A | TD, FD | 64 | 9 (2 WH + 2 W + 4 IF + R) | 16 | 0.56 | N/A | ANN | 0.951 |
Arteaga et al., 2020 [17] | 20 | TD, FD | 24 | 6 (5 IF + WH) | 4 | 1.5 | N/A | KNN | 0.975 |
Fajardo et al., 2021 [18] | N/A | TD, FD, TFD, features extracted by CNN | 198 | 10 (6 W + 3 WH + IF, highest *) | 1 | 10 | 750 ms | CNN | 0.657 |
4 (lowest *) | 4 | 0.952 | |||||||
This study | 10 | TD | 18 | 10 (2 WH + 7 IF + R) | 3 | 3.34 | 250 ms | ANN | 0.940 |
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Lee, K.H.; Min, J.Y.; Byun, S. Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks. Sensors 2022, 22, 225. https://doi.org/10.3390/s22010225
Lee KH, Min JY, Byun S. Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks. Sensors. 2022; 22(1):225. https://doi.org/10.3390/s22010225
Chicago/Turabian StyleLee, Kyung Hyun, Ji Young Min, and Sangwon Byun. 2022. "Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks" Sensors 22, no. 1: 225. https://doi.org/10.3390/s22010225