多关节上肢康复机器人患者协同肌电控制方法及其临床应用研究

Stroke is the leading cause of disabilities and rehabilitation is important for motor functions recovery to facilitate patients after stroke back to normal activities of daily life. Robot-assisted rehabilitation is a promising new technology in stroke rehabilitation, which provide repetitive, well-controlled assistance for patients and relieve therapists from labour-intensive work. While post-stroke motor recovery depends on active rehabilitation by voluntary participation of patient's paretic motor system as early as possible in order to promote reorganization of brain. However, voluntary residual motor efforts to the affected limb have not been involved enough in most robot-assisted rehabilitation for patients after stroke.In the present project, an innovative wire-driven rehabilitation robotic system is designed, and voluntary intention is involved by using the residual surface electromyography (EMG) signals from eight affected muscles which mainly contribute to the upper limb movement, to control the mechanical assistance provided by the robotic system. EMG signal can create a natural integration between human and machine, and the assistive function of the myoelectrically-controlled robotic system could enable subjects after stroke to perform upper limb movement in larger 3- dimensional working space than through their own voluntary efforts. This study also investigates abnormal mode of EMG activation and consider the effect in the control model, which can improve the quality of movement during myoelectrical control. Finally, a robot-aided therapy experiment is conducted to investigate the therapeutical effect of the myoelectrically- controlled robotic system. This study can validate the feasibility of robot-aided rehabiiltation using myoelectrical control in clinical use.

脑卒中后单侧肢体运动功能障碍(偏瘫)是中国及许多国家致残率最高的因素,给偏瘫后患者的生活质量造成严重的影响。康复机器人技术以其自动控制、不知疲倦等优点成为物理治疗师的有力辅助。然而，目前大多数康复机器人不能根据患者的意识做出实时的调整，容易让患者依赖机器被动完成训练，也缺乏对多关节复合协同运动的训练功能，影响康复效果。本项目提出了一种新颖的康复机器人设计方案，研究以偏瘫患者的患侧肢体的八块肌肉肌电信号为控制源来控制康复机器人的方法，既通过患者的意识参与改善了人机交互性，又通过多通道肌电信号实时连续控制，辅助患者完成三维空间多关节协同运动的训练。研究偏瘫患者肌肉共收缩所导致异常模式的形成机理与识别算法，建立考虑偏瘫患者肌肉共收缩的肌电控制模型，提高训练动作质量。研究康复机器人的设计，增大患者上肢训练空间。最终提高康复机器人辅助康复训练的临床有效性。研究成果可为康复机器人的临床应用提供技术支撑

本项目研究了一种新的可应用于临床上肢康复训练的多关节上肢康复机器人的肌电控制方法，该研究能使康复机器人更好地实现包括肩关节、肘关节的上肢运动康复需求。该研究的主要内容如下：（1）实验平台的搭建：结合人体运动特性，搭建出一个具有多关节多自由度的康复机器人系统，通过控制系统牵引绳子的长度和拉力，能较好地辅助患者完成较复杂的康复训练。（2）肌电与运动学信号的康复评价研究：实现了在康复机器人系统中应用熵对肌电和运动学信号进行评价，有效表征了运动神经单元募集数量变化，在临床上具有较高的定量客观评估价值。（3）实时重力补偿的研究：康复机器人除了能够协助患者患侧上肢完成包括肩关节、肘关节的运动和复合协同运动在内的康复训练；还实现了患侧上肢重力的实时补偿，能帮助患者支撑上臂和前臂，增大了主动运动空间。（4）肌电控制方法的研究：实现了由患者患侧肩肘关节肌肉的肌电信号为控制源的康复机器人的实时控制，实现了更好的人机交互。发表在国内外核心期刊发表论文16篇（其中SCI检索11篇，EI检索5篇），申请专利12个，授权8项，发表会议论文10篇，达到了预定目标。