Download PDFOpen PDF in browserEMG-Based Feature Extraction and Classification for Prosthetic Hand Control10 pages•Published: March 22, 2022AbstractIn recent years, real-time control of prosthetic hands has gained a great deal of attention. In particular, real-time analysis of Electromyography (EMG) signals has several challenges to achieve an acceptable accuracy and execution delay. In this paper, we address some of these challenges by improving the accuracy in a shorter signal length. We first introduce a set of new feature extraction functions applying on each level of wavelet decomposition. Then, we propose a postprocessing ap- proach to process the neural network outputs. The experimental results illustrate that the proposed method enhances the accuracy of real-time classification of EMG signals up to 95.5 percent for 800 msec signal length. The proposed postprocessing method achieves higher consistency compared with conventional majority voting and Bayesian fusion methods.Keyphrases: classification, electromyography (emg), feature extraction, neural network In: Hisham Al-Mubaid, Tamer Aldwairi and Oliver Eulenstein (editors). Proceedings of 14th International Conference on Bioinformatics and Computational Biology, vol 83, pages 136-145.
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