Using a convolutional neural network features to EMG signals classification with continuous wavelet transformation and LS-SVM .
The various hand EMG signal grasps are classified in this study. Because EMG signals offer
critical information about muscle activity, they are commonly used as input to electro muscular
control systems. Each muscle performs a specific function in each movement. Electromyography is
a medical, healthcare, and human-machine interaction diagnostic technique for acquiring an EMG
signal (MMI). e most important component of the locomotion system is the muscular system.
Accordingly, sensors were developed to detect the movement system and diagnose the
electromyogram. Nowadays, While maintaining a modest size, it has improved and become more
accurate. In this paper, The EMG signals are converted into images using CWT, then the EMG
images features are extracted based on convolutional neural network (CNN) , and finally, the EMG
features are categorized by an LS-SVM classifier in Matlab. The main objective of this study is to
classify grasps into six basic hand movements: (1) cylindrical, (2) palm, (3) lat (4) sphere(5) Tip,
and (6) Hook. Finally, electrophysiological patterns of each movement were extracted by extracting
features from the images using CNN where EMG images are divided into (70 percent ) training and
(30 percent ) validation, and then these features are fed into classification using the least square
support vector machine. It produced an accuracy of 94.81%.
. Khan, M.U., et al. Supervised Machine Learning based Fast Hand Gesture Recognition and
Classification Using Electromyography (EMG) Signals. in 2021 International Conference on Applied and
Engineering Mathematics (ICAEM). 2021. IEEE.
.Chung, E.A. and M.E. Benalcázar. Real-time hand gesture recognition model using deep learning
techniques and EMG signals. in 2019 27th European Signal Processing Conference (EUSIPCO). 2019.
- BAKIRCIOĞLU, K., & Özkurt, N. (2020). Classification of EMG signals using convolution neural
network. International Journal of Applied Mathematics Electronics and Computers, 8(4), 115-119.
- Tuncer, T., S. Dogan, and A. Subasi, Surface EMG signal classification using ternary pattern and
discrete wavelet transform based feature extraction for hand movement recognition. Biomedical Signal
Processing and Control, 2020. 58: p. 101872
-Ouyang, G., et al., Dynamical characteristics of surface EMG signals of hand grasps via recurrence
plot. IEEE journal of biomedical and health informatics, 2013. 18(1): p. 257-265.
- Singhal, D. and A. Kaushik, Different Models for Limb Movement Classification Using Surface
EMG Signals. 2020.
- Rabin, N., et al., Classification of human hand movements based on EMG signals using nonlinear
dimensionality reduction and data fusion techniques. Expert Systems with Applications, 2020. 149: p.
- Khan, S.M., A.A. Khan, and O. Farooq, Pattern recognition of EMG signals for low level grip force
classification. Biomedical Physics & Engineering Express, 2021. 7(6): p. 065012.
-Yavuz, E. and C. Eyupoglu, A cepstrum analysis-based classification method for hand movement
surface EMG signals. Medical & biological engineering & computing, 2019. 57(10): p. 2179-2201.
- Akben, S.B., Low-cost and easy-to-use grasp classification, using a simple 2-channel surface
electromyography (sEMG). Biomed Res, 2017. 28(2): p. 577-582.
- Nishad, A., et al., Automated classification of hand movements using tunable-Q wavelet transform
based filter-bank with surface electromyogram signals. Future Generation Computer Systems, 2019. 93:
- Iqbal, O., S.A. Fattah, and S. Zahin. Hand movement recognition based on singular value
decomposition of surface EMG signal. in 2017 IEEE Region 10 Humanitarian Technology Conference
(R10-HTC). 2017. IEEE
-Subasi, A. and S.M. Qaisar, Surface EMG signal classification using TQWT, Bagging and Boosting
for hand movement recognition. Journal of Ambient Intelligence and Humanized Computing, 2020: p. 1-
- Jia, G., et al., Classification of electromyographic hand gesture signals using machine learning
techniques. Neurocomputing, 2020. 401: p. 236-248.
- Mulimani, M. and S.G. Koolagudi, Segmentation and characterization of acoustic event
spectrograms using singular value decomposition. Expert Systems with Applications, 2019. 120: p. 413-
- Mulimani, M. and S.G. Koolagudi. Acoustic event classification using spectrogram features. in
TENCON 2018-2018 IEEE Region 10 Conference. 2018. IEEE.
[- Sapsanis C, Georgoulas G, Tzes A. EMG based classification of basic hand movements based on
time-frequency features. In: 21st Mediterranean Conference on Control and Automation; 2013. p. 716–
- Zhao, Y., Shen, Y., Zhu, Y., & Yao, J. (2018, November). Forecasting wavelet transformed time
series with attentive neural networks. In 2018 IEEE International Conference on Data Mining
(ICDM) (pp. 1452-1457). IEEE
- Song, C., et al. Deep Reinforcement Learning Apply in Electromyography Data Classification. in
IEEE International Conference on Cyborg and Bionic Systems (CBS). 2018. IEEE.
- J. A. K. Suykens and J. Vandewalle, “Least squares support vector machine classifiers,” Neural
Process. Lett., vol. 9, no. 3, pp. 293–300, 1999.
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