Hierarchical Motion Segmentation Through sEMG for Continuous Lower Limb Motions

Abstract

Surface electromyograms (sEMG), are records of electrical signals generated by muscles, and have long been used to indicate the motions intended by humans to enable interactions between a robot and a human. To support not only the diverse movements in human daily living but also the task of increasing the human-robot interface and its applications, a new algorithm that can classify continuous lower-limb motion using sEMG signals is proposed herein. By simply constructing the motion hierarchy and probability distribution of sEMG for each motion phase obtained using only kinematic motion data and sEMG data, it is possible to demonstrate higher classification accuracy than with state-of-the-art supervised learning methods that consume much time. Four different experiments were performed involving five participants and the algorithm was verified to distinguish successfully walking, running, jumping and sit-to-stand.