北京生物医学工程

基于肩部肌肉表面肌电的下肢外骨骼控制系统

Lower limb exoskeleton control system based on shoulder muscle surface electromyography

作者：朱鹏霖苏宗信唐家曦武彦言于诗静唐鹤云

单位：徐州医科大学医学影像学院（江苏徐州 221004） <br />作者简介：唐鹤云，副教授。E-mail：8652172@qq.com。

分类号：R318.04 

出版年·卷·期（页码）：2021·40·5（510-515）

摘要：

目的设计一种基于肩关节屈曲、外展运动主要肌肉表面肌电信号的下肢可穿戴外骨骼控制系统，解决基于下肢肌肉表面肌电信号控制系统信号提取识别困难的问题。方法首先采用表面肌电信号传感器采集患者的三角肌前束、中束以及斜方肌肌电信号。其次通过蓝牙等无线通信技术将信号实时传输至控制板。再次，控制板通过分析采集信号数据特征判断患者运动状态和运动意图，控制大腿相关关节处舵机根据三维步态采集系统得到患者特殊步态数据提供相关扭矩完成运动。最后，受试者穿戴配有控制系统的外骨骼康复机器人后进行3组独立重复实验，验证机器人控制系统实时提取识别肌电信号、控制舵机运作的效果。结果肩关节表面肌肉位置浅表、肌腹面积大且拮抗肌信号干扰小，表面肌电采集容易，控制板对运动意图的识别难度低，平均识别成功率有89.6%，验证了下肢外骨骼康复机器人控制系统有效控制舵机辅助人体完成康复训练的可行性。结论基于肩部肌肉表面肌电信号的下肢外骨骼控制系统降低了信号提取识别难度，下肢外骨骼助行机器人通过提取并识别肩部表面肌电信号进行运动具有可行性。

Objective To design a lower extremity wearable exoskeleton control system based on the surface EMG signals of main muscles of shoulder flexion and abduction.，and to solve the problem of difficult signal extraction and recognition based on lower limb muscle surface EMG signal control system. Methods Firstly, the electrical signals of anterior, middle and trapezius deltoid were collected by surface EMG sensor. Secondly, the signal was transmitted to the control board in real time through Bluetooth and other wireless communication technologies. Thirdly, the control board judged the patient's motion state and motion intention by analyzing the characteristics of the collected signal data, controlled the steering gear at the relevant joints of the thigh, obtained the patient's special gait data according to the three-dimensional gait acquisition system, and provided relevant torque to complete the motion. Finally, after wearing the exoskeleton rehabilitation robot equipped with the control system, the subjects carried out three groups of independent repeated experiments to verify the effect of the robot control system on extracting and identifying EMG signals in real time and controlling the operation of the steering gear. Results The muscle position on the surface of shoulder joint was shallow, the area of muscle abdomen was large, and the interference of antagonistic muscle signal was small. The surface EMG acquisition was easy, and the control board had low difficulty in identifying movement intention. The average recognition success rate was 89.6%. It was verified that the control system of lower limb exoskeleton rehabilitation robot could effectively control the steering gear to assist the human body to complete rehabilitation training. Conclusions The design of lower limb exoskeleton control system based on shoulder muscle surface EMG signal reduces the difficulty of signal extraction and recognition. It is feasible for lower limb exoskeleton walking aid robot to move by extracting and recognizing shoulder surface EMG signal.

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