A machine learning approach to identify hand actions from single-channel sEMG signals
Author:
Affiliation:
1. Department of Electronics and Communication Engineering , Sri Sai Ram Engineering College , West Tambaram , Chennai , India
2. Rosenheim Technical University of Applied Sciences , Rosenheim , Germany
Abstract
Publisher
Walter de Gruyter GmbH
Subject
Biomedical Engineering
Link
https://www.degruyter.com/document/doi/10.1515/bmt-2021-0072/pdf
Reference38 articles.
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2. Ngeo, JG, Tamei, T, Shibata, T. Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model. J NeuroEng Rehabilitation 2014;11:1–4. https://doi.org/10.1186/1743-0003-11-122.
3. Badawy, A, Alfred, R. Myoelectric prosthetic hand with a proprioceptive feedback system. J King Saud Univ Eng Sci 2020;32:388–95. https://doi.org/10.1016/j.jksues.2019.05.002.
4. Al-Timemy, AH, Bugmann, G, Escudero, J, Outram, N. Classification of finger movements for the dexterous hand prosthesis control with surface electromyography. IEEE J Biomed Health Inf 2013;17:608–18. https://doi.org/10.1109/jbhi.2013.2249590.
5. Garcia, MC, Vieira, TM. Surface electromyography: why, when and how to use it. Rev Andal Med Deporte 2011;4:17–28.
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