Kinematics-Based Adaptive Assistance of a Semi-Passive Upper-Limb Exoskeleton for Workers in Static and Dynamic Tasks

Abstract

Typical industrial work activities may include a variety of different gestures, entailing the execution of dynamic and static movements. Occupational upper-limb exoskeletons can assist the shoulder complex in both static and dynamic gestures, but the required assistance level may be different according to the tasks. This article presents the design, development, and experimental evaluation of a novel kinematics-based adaptive assistance algorithm for a semi-passive upper-limb exoskeleton. The algorithm uses kinematic signals gathered by onboard sensors to set the assistance amplitude according to the type of movement being executed. Experimental activities were performed to assess the algorithm's performance. Results show that the algorithm can effectively provide different assistance levels according to the type of task being executed, such as the minimum level for more dynamic tasks and the maximum level for the most static activities. Additionally, compared to working without the exoskeleton, the exoskeleton controlled by the proposed adaptive algorithm can reduce the users’ flexor muscular activity in both dynamic and static tasks, respectively by 24 ± 6% and 42 ± 2%. Similar results were reported for extensor muscles, which reduced their activations by 7 ± 3%, and 40 ± 4% in dynamic and static tasks.