Comparing Online Performance of EMG Pattern Recognition with and Without Joint Movements
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Bioengineering,Biophysics,Biotechnology
Link
https://link.springer.com/content/pdf/10.1007/s42235-023-00376-4.pdf
Reference42 articles.
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2. Khushaba, R. N., Al-Timemy, A., Kodagoda, S., & Nazarpour, K. (2016). Combined influence of forearm orientation and muscular contraction on emg pattern recognition. Expert Systems with Applications, 61, 154–161. https://doi.org/10.1016/j.eswa.2016.05.031
3. Phukpattaranont, P., Thongpanja, S., Anam, K., Al-Jumaily, A., & Limsakul, C. (2018). Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal. Medical & Biological Engineering & Computing, 56(12), 2259–2271. https://doi.org/10.1007/s11517-018-1857-5
4. Guo, W. C., Sheng, X. J., Liu, H. H., & Zhu, X. Y. (2017). Toward an enhanced human-machine interface for upper-limb prosthesis control with combined emg and nirs signals. IEEE Transactions on Human-Machine Systems, 47(4), 564–575. https://doi.org/10.1109/thms.2016.2641389
5. Parajuli, N., Sreenivasan, N., Bifulco, P., Cesarelli, M., Savino, S., Niola, V., Esposito, D., Hamilton, T. J., Naik, G. R., Gunawardana, U., & Gargiulo, G. D. (2019). Real-time emg based pattern recognition control for hand prostheses: A review on existing methods, challenges and future implementation. Sensors, 19(20), 4596. https://doi.org/10.3390/s19204596
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