基于任务表现的机器人辅助康复自适应控制策略

以机器人辅助的上肢协调康复训练为研究对象,提出一种基于任务表现的自适应控制策略,为肢体运动功能障碍患者提供个性化的机器人辅助,旨在提高患者的主动运动参与度,实现高效的康复训练。首先,介绍上肢末端式双边康复平台以及协调训练任务。然后,引入临床运动评估参数与协调训练指标,采用模糊神经网络模型建立多任务指标与机器人导纳控制参数间的映射关系。最后,通过受试者参与的协调训练实验对所提出方法进行了验证,并与相关文献中的人机交互策略进行了对比分析。实验结果表明,本文方法具有较好的任务指标追踪效果和人机交互平稳性,能够自适应为患者提供个性化的机器人辅助,有助于提高受试者的训练积极性。 

A performance-based adaptive control strategy is proposed for robot-assisted upper-limb coordinated rehabilitation training, with aims to provide subject-specific robotic assistance for patients with limb movement disorders and promote their active engagement in effective rehabilitation training. Firstly, an end-effector for upper-limb bilateral rehabilitation and a coordinated training task are presented. Then, kinematic parameters based on clinical scales and coordinated training indicators are introduced, and a fuzzy neural network model is constructed to relate the multiple task measures to the robot admittance parameter. Finally, the coordinated training experiments are conducted with human subjects to validate the proposed method and compare it with the human-robot interactive strategy mentioned in the previous study. Experimental results indicate that the proposed strategy achieves a good ability of tracking desired performance indicators and keeping human-robot interaction smooth, and it can adaptively provide tailored robotic assistance, which enhances subjects' active engagement in training.