Discrete Motion Prediction Based on EMG Signals

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%.