Zhang Bangcheng, Lan Xuteng, Wang Gang, Pang Zaixiang, Zhang Xiyu, Sun Zhongbo
Department of Mechatronical Engineering, Changchun University of Technology, Changchun, China.
Department of Industrial Engineering, Changchun University of Technology, Changchun, China.
Front Neurorobot. 2022 Nov 3;16:1047325. doi: 10.3389/fnbot.2022.1047325. eCollection 2022.
The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilitation training is a hot research topic in human-computer interaction control. Therefore, improving the accuracy of active motion intention recognition is the premise of the human-machine interaction controller design. Furthermore, there are external disturbances (bounded/unbounded disturbances) during rehabilitation training, which seriously threaten the safety of subjects. Thereby, eliminating external disturbances (especially unbounded disturbances) is the difficulty and key to the human-machine interaction control of the upper limb rehabilitation robots. In response to these problems, based on the surface electromyogram signal of the human upper limb, this paper proposes a fuzzy neural network active motion intention recognition method to explore the internal connection between the surface electromyogram signal of the human upper limb and active motion intention, and improve the real-time and accuracy of recognition. Based on this, two types of human-machine interaction controllers, which can be called as zeroing neural network controller and noise-suppressing zeroing neural network controller are designed to establish a safe and comfortable training environment to avoid secondary damage to the affected limb. Numerical experiments verify the feasibility and effectiveness of the proposed theories and methods.
使用上肢康复机器人辅助患肢进行主动康复训练是康复医学领域的必然趋势。特别是，基于主动运动意图的上肢康复机器人控制以辅助受试者进行康复训练是人机交互控制中的一个热门研究课题。因此，提高主动运动意图识别的准确性是人机交互控制器设计的前提。此外，康复训练过程中存在外部干扰（有界/无界干扰），严重威胁受试者的安全。从而，消除外部干扰（尤其是无界干扰）是上肢康复机器人人机交互控制的难点和关键。针对这些问题，本文基于人体上肢表面肌电信号，提出一种模糊神经网络主动运动意图识别方法，以探索人体上肢表面肌电信号与主动运动意图之间的内在联系，提高识别的实时性和准确性。在此基础上，设计了两种人机交互控制器，可称为归零神经网络控制器和抑噪归零神经网络控制器，以建立安全舒适的训练环境，避免对患肢造成二次损伤。数值实验验证了所提理论和方法的可行性和有效性。
