An Improved Flexible Hand Exoskeleton with SEA for Finger Strength Estimation and Progressive Resistance Exercise

Abstract

Hand exoskeletons can recognize user’s intent and provide active resistance training to enhance finger strength in stroke patients. However, achieving fine human-robot interaction (HRI) while maintaining system simplicity for lightweight design remains a key challenge. In this work, we present an improved flexible hand exoskeleton with series elastic actuator (SEA) for hand strength estimation and progressive resistance exercise. The SEA design allows the hand exoskeleton to have backdrivability to improve HRI performance. By combining the flexible linkage with the flex sensor, we propose a novel user interface that is able to sensitively acquire hand motion intent. An Extended Kalman Filter (EKF) based tracking errors estimation is designed to evaluate the finger strength. The results of the finger strength estimation are used to adjust the parameters of the admittance model to provide small or large damping when the user’s finger strength is low or high, achieving active admittance control based progressive resistance exercise. The feasibility has been demonstrated by two sets of experiments, and this work has established a hand exoskeleton solution for finger strength estimation and fine human-robot interaction.