Developing a Method to Control an Arm-Assist-Suit by Predicting Arm-Trajectory Using Electromyography.

Many researchers have developed assist-suits to support repetitive and strenuous physical labor, but existing suits show unsatisfactory responsiveness and restrict arm motions. Therefore, we propose a method for an arm-assist-suit that synchronizes arm motions by using electromyography (EMG) to predict arm trajectory. EMG is used to measure and record electrical signals while muscles are active. Further, predicted arm-joint motions and estimated arm-joint angles are used for arm trajectory predictions. In this study, we attempted the prediction of elbow-joint motions and the timing of motion changes. Two subjects executed twelve types of elbow-joint movements that had four start and endpoints. We measured seven muscle types with EMG points on the right arm(hand, elbow, and shoulder) a motion capture system, respectively. After processing these data, we applied a multiclass logistic regression, which is a machine-learning technique, to predict elbow-joint motions, namely, rest, flexion, and extension. The precision in elbow joint motion prediction shows a difference between the two subjects for the three motions analyzed. Additionally, the rest prediction accuracy is lower than both flexion and extension for each subject. The prediction of elbow-joint motion change timing does not correlate with the elbow-joint motion predictions, with the timing prediction precision being very low and thus, causing some difficulties. To overcome these difficulties, and improve precision in future work, we plan to apply an independent component analysis to eliminate noise and add or change features.Clinical Relevance- This study aims to establish a benchmark for future research on the improvement of responsiveness and range-of-motion of arm-assist-suits.