面向智能假肢手臂的生机接口系统与类神经协同控制

Biomechanical Interface System and Neural-like Cooperative Control for the Intelligent Prosthetic Arm

针对肢体残障患者的假肢控制问题,搭建了一种基于s EMG(表面肌电信号)的智能假肢手臂系统,实现手臂残障程度较高患者的手-肘协调控制。首先,基于肌肉协同理论,使用非负矩阵分解(NMF)方法提取肌肉协同作用,并进行手部动作识别以及肘关节的连续运动估计。其次,基于意图识别结果构建“前馈-反馈”控制框架,对受试者进行前馈监督与反馈检测;根据前馈-反馈结果调整期望的控制输入,提高假肢系统的舒适性与鲁棒性。然后,针对手部动作,构建一种自适应调整抓握力度的框架,通过力、位信息交替控制,实现不同刚度、不同形状物体的自适应抓握;对于肘部运动,设计一种基于识别结果的阻抗控制算法,实现手-肘一体化假肢的稳定的人机交互控制。最后,由6名健康受试者、1名手臂残障受试者对以上控制策略进行实验验证,对手臂整体运动实现了较为准确的意图识别,同时也完成了稳定的肘部屈伸以及手部抓取,做到了手-肘的一体化协调控制。最终该套系统在北京2022年冬残奥会实现了应用展示。

To address the problem of prosthetic limb control for patients with physical disability, an sEMG(surface electromyography) based intelligent prosthetic arm system is developed to achieve coordinated hand-elbow control for patients with a higher degree of arm disability. Firstly, the non-negative matrix factorization(NMF) method is applied to extracting muscle synergy based on the muscle synergy theory, and hand movement recognition and continuous motion estimation of the elbow joint are implemented. Secondly, a “feedforward-feedback” control framework is constructed based on the intention recognition results, and feedforward supervision and feedback detection are performed on the subjects to improve the comfort and robustness of the prosthetic system by adjusting the desired control input based on the feedforward-feedback results. Then, an adaptive grip force adjustment framework is constructed for hand movements to achieve adaptive grip of objects of different stiffness and shapes through alternating force and position information control;for elbow movements, an impedance control algorithm based on recognition results is designed to achieve stable human-machine interaction control of the hand-elbow integrated prosthesis. Finally, the above control strategy is experimentally verified by 6 healthy subjects and an arm handicapped subject in order to achieve more accurate intention recognition for the overall arm motion, and the result indicates that the proposed approach can complete stable elbow flexion and extension as well as hand grasping function to achieve coordinated control of the integrated hand-elbow. The system was realized in the Beijing 2022 Winter Paralympic Games for application demonstration.