Upper Limb Movement Recognition Utilising EEG and EMG Signals for Rehabilitative Robotics-Reference-Cited by-同舟云学术

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Upper Limb Movement Recognition Utilising EEG and EMG Signals for Rehabilitative Robotics

Published:2023 Issue: Volume: Page:676-695
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:
Short-container-title:

Author:

Wang Zihao,Suppiah Ravi

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-28076-4_49

Reference33 articles.

1. Russo, R.E., Fernández, J.G., Rivera, R.R.: Algorithm of myoelectric signals processing for the control of prosthetic robotic hands. J. Comput. Sci. Technol. 18(01), e04 (2018). https://doi.org/10.24215/16666038.18.e04

2. Nazari, F., Nahavandi, D., Mohajer, N., Khosravi, A.: Human activity recognition from knee angle using machine learning techniques. In: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Melbourne, Australia, Oct. 2021, pp. 295–300. https://doi.org/10.1109/SMC52423.2021.9658988

3. Biswas, D., et al.: Recognizing upper limb movements with wrist worn inertial sensors using k-means clustering classification. Hum. Mov. Sci. 40, 59–76 (2015). https://doi.org/10.1016/j.humov.2014.11.013

4. Fleischer, C., Wege, A., Kondak, K., Hommel, G.: Application of EMG signals for controlling exoskeleton robots. Biomed. Tech. Eng. 51(5), 314–319 (2006). https://doi.org/10.1515/BMT.2006.063

5. Guo, S., Pang, M., Gao, B., Hirata, H., Ishihara, H.: Comparison of sEMG-based feature extraction and motion classification methods for upper-limb movement. Sensors 15(4), 9022–9038 (2015). https://doi.org/10.3390/s150409022

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