Discrete Motion Prediction Based on EMG Signals

肌电信号的离散运动预测研究

School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710072, China

Received: 13 July 2018

Abstract

Most of the current rehabilitation equipment is bulky and slow to respond. Therefore, we designed a portable three-degree-of-freedom exoskeleton and discrete-mode control system for this situation. The three degrees of freedom of the exoskeleton robot arm was the wrist swing, the forearm lateral movement, and the elbow rotation. We collected the EMG signals of the biceps and triceps, then filtered the acquired EMG signals and extracted features in order to obtain effective information that reflected the activity intentions. Based on this, a discrete motion control method using the EMG signals to achieve elbow rotation was designed. Experiment suggests that the average pattern recognition accuracy rate can reach more than 90%.

摘要

目前的康复训练设备大都体积庞大，响应迟缓。针对这一现状设计了一套便携式三自由度上肢外骨骼机械臂及其控制系统。该机械臂的3个自由度分别是手掌腕部摆动、前臂侧向运动和肘关节转动。对肱二头肌和肱三头肌的肌电信号进行采集，然后对采集的肌电信号进行滤波，提取特征值，以期获得反映人体运动意图的有效信息。在此基础上设计了利用肌电信号实现肘关节转动的离散运动控制方法。实验表明设计的离散运动控制方法平均模式识别正确率能达到90%以上。