Segmenting humeral submovements using invariant geometric signatures

Discrete submovements are the building blocks of any complex movement. When robots collaborate with humans, extraction of such submovements can be very helpful in applications such as robot-assisted rehabilitation. Our work aims to segment these submovements based on the invariant geometric information embedded in segment kinematics. Moreover, this segmentation is achieved without any explicit kinematic representation. Our work demonstrates the usefulness of this invariant framework in segmenting a variety of humeral movements, which are performed at different speeds across different subjects. Our results indicate that this invariant framework has high computational reliability despite the inherent variability in human motion.