Optimizing Exoskeleton Assistance: Muscle Synergy-Based Actuation for Personalized Hip Exoskeleton Control

Abstract

Exoskeleton robots hold promising prospects for rehabilitation training in individuals with weakened muscular conditions. However, achieving improved human–machine interaction and delivering customized assistance remains a challenging task. This paper introduces a muscle synergy-based human-in-the-loop (HIL) optimization framework for hip exoskeletons to offer more personalized torque assistance. Initially, we propose a muscle synergy similarity index to quantify the similarity of synergy while walking with and without the assistance of an exoskeleton. By integrating surface electromyography (sEMG) signals to calculate metrics evaluating muscle synergy and iteratively optimizing assistance parameters in real time, a muscle synergy-based HIL optimized torque configuration is presented and tested on a portable hip exoskeleton. Iterative optimization explores the optimal and suboptimal assistance torque profiles for six healthy volunteers, simultaneously testing zero torque and predefined assistance configurations, and verified the corresponding muscle synergy similarity indices through experimental testing. In our validation experiments, the assistance parameters generated through HIL optimization significantly enhance muscle synergy similarity during walking with exoskeletal assistance, with an optimal average of 0.80 ± 0.04 (mean ± std), marking a 6.3% improvement over prior assistive studies and achieving 96.4% similarity compared with free walking. This demonstrates that the proposed muscle synergy-based HIL optimization can ensure robotic exoskeleton-assisted walking as “natural” as possible.