Lightweight deep neural network models for electromyography signal recognition for prosthetic control
نویسندگان
چکیده
In this paper, lightweight deep learning methods are proposed to recognize multichannel electromyography (EMG) signals against varying contraction levels. The classical machine learning, and signal processing namely, linear discriminant analysis (LDA), quadratic (QDA), root mean square (RMS), waveform length (WL) adopted convolutional neural network (CNN), long short-term memory (LSTM). Eight-channel recordings of nine amputees from a publicly available dataset used for training testing the models considering prosthetic control strategies. Six class hand movements with three levels applied WL RMS-based feature extraction. After that, they formed into appropriate input dimensions, classified using LDA, QDA, LDA-CNN, QDA-CNN, LSTM, CNN. Depending on EMG validation approaches (Scheme 1-3), accuracy rates 41.68%, 47.27% yielded by QDA 32- dimensional RMS, features while CNN 2×16 has 82.87% (up 88.10%). effect learnable filters DL approaches, windowing success rate delay time discussed in paper. simulations show that 2D-CNN (accuracy 1.7 ms delay) can be successfully adapted devices.
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ژورنال
عنوان ژورنال: Turkish Journal of Electrical Engineering and Computer Sciences
سال: 2023
ISSN: ['1300-0632', '1303-6203']
DOI: https://doi.org/10.55730/1300-0632.4012