In this study, we propose a novel power assist control method for a powered exoskeleton without binding its legs. The proposed method uses motion sensors on the wearer’s torso and legs to estimate his/her motion to enable the powered exoskeleton to assist with the estimated motion. It can detect the start of walking motion quickly because it does not prevent the motion of the wearer’s knees at the beginning of the walk. A nine-axis motion sensor on the wearer’s body is designed to work robustly in very hot and humid spaces, where an electromyograph is not reliable due to the wearer’s sweat. Moreover, the sensor avoids repeated impact during the walk because it is attached to the body of the wearer. Our powered exoskeleton recognizes the motion of the wearer based on a database and accordingly predicts the motion of the powered exoskeleton that supports the wearer. Experiments were conducted to prove the validity of the proposed method.
Dynamic manifold Boltzmann optimization based on self‐supervised learning for human motion estimation
