Articles | Volume 17, issue 1
https://doi.org/10.5194/ms-17-685-2026
https://doi.org/10.5194/ms-17-685-2026
Research article
 | 
26 Jun 2026
Research article |  | 26 Jun 2026

Research on compliance control strategy of elbow–wrist rehabilitation robot based on information fusion

Hui Bian, Hang Shang, Zihan Li, Yifan Xu, Peixuan Du, Mingzhi Wang, Runyang Liu, and Ze Luo

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Cited articles

Abbas, M., Narayan, J., and Dwivedy, S. K.: Event-triggered adaptive control for upper-extremity therapeutic robot in active-assist mode: A simulation study, P. I. Mech. Eng.-C J. Mec., 238, 4628–4643, https://doi.org/10.1177/09544062231208722, 2023. 
Antuvan, C. W., Bisio, F., Marini, F., Yen, S. C., Cambria, E., and Masia, L.: Role of Muscle Synergies in Real-Time Classification of Upper Limb Motions using Extreme Learning Machines, J. NeuroEng. Rehabil., 13, 15, https://doi.org/10.1186/s12984-016-0183-0, 2016. 
Ao, X. H., Wang, F., Zhao, J., and She, J. H.: Interpretable analysis of feature importance and implicit correlation based on sEMG grayscale images, IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS), Wuhan, PEOPLES R CHINA, 8–11 May, WOS:001031560600017, https://doi.org/10.1109/icps58381.2023.10128002, 2023. 
Ayas, M. S. and Altas, I. H.: Fuzzy logic based adaptive admittance control of a redundantly actuated ankle rehabilitation robot, Control Eng. Pract., 59, 44–54, https://doi.org/10.1016/j.conengprac.2016.11.015, 2017. 
Bao, T. Z., Xie, S. Q., Yang, P. F., Zhou, P., and Zhang, Z. Q.: Toward Robust, Adaptiveand Reliable Upper-Limb Motion Estimation Using Machine Learning and Deep Learning-A Survey in Myoelectric Control, IEEE J. Biomed. Health Inform., 26, 3822–3835, https://doi.org/10.1109/jbhi.2022.3159792, 2022. 
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Short summary
Aiming to address the problems of the large time delay and low compliance of the system caused by the slow speed of motion intention recognition based on the force signal, this paper integrates the force signal and the surface electromyography (sEMG) signal as the input of the elbow–wrist rehabilitation robot, thereby achieving a good balance between fast response and force compliance in the control system.
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