Selective Assist Strategy by Using Lightweight Carbon Frame Exoskeleton Robot

Exoskeleton robots need to always actively assist the user's movements otherwise robot just becomes a heavy load for the user. However, estimating diversified movement intentions in a user's daily life is not easy and no algorithm so far has achieved that level of estimation. In this study, we rather focus on estimating and assisting a limited number of selected movements by using an EMG-based movement classification and a newly developed lightweight exoskeleton robot. Our lightweight knee exoskeleton is composed of a carbon fiber frame and highly backdrivable joint driven by a pneumatic artificial muscle. Thus, our robot does not interfere with the user's motions even when the actuator is not activated. As the classification method, we adopted a positive-unlabeled (PU) classifier. Since precisely labeling all the selected data from large-scale daily movements is not practical, we assumed that only part of the selected data was labeled and used a PU classifier that can handle the unlabeled data. To validate our approach, we conducted experiments with five healthy subjects to selectively assist sit-to-stand movements from four possible daily motions. We compared our approach with two classification methods that assume fully labeled data. The results showed that all subject's movements were properly assisted.