Electromyography-Based Human-in-the-Loop Bayesian Optimization to Assist Free Leg Swinging

Background/Objectives: The manual tuning of exoskeleton control parameters is tedious and often ineffective for adapting to individual users. Human-in-the-loop (HIL) optimization offers an automated approach, but existing methods typically rely on metabolic cost, which requires prolonged data collection times of at least 60 s. Surface electromyography (EMG) signals, as an alternative, enable faster optimization with reduced data acquisition times. Methods: This study develops a rapid EMG-based HIL Bayesian optimization framework to tune hip exoskeleton controllers for assisting free leg swinging. Eight participants are asked to perform leg swinging at two frequencies with assistance from a hip exoskeleton. EMG signals from four sensors, representing muscle activity during forward and backward swings, are dynamically processed into cost functions. Bayesian optimization with Gaussian processes tunes four controller parameters using an expected improvement acquisition function. Optimization outcomes are validated against no device, zero torque, and general control baselines. Results: Optimization converges within an average of 142 s with a standard deviation of 24 s across all participants. The controller yields muscle activity reductions of 16.1% (p < 0.001) compared to no device, 21.7% (p < 0.001) versus zero torque, and 15.1% (p < 0.001) versus general control. EMG-based tuning is faster than metabolic-cost-based methods and perceived as less effortful, with Borg scale reductions of up to 39.5%. Conclusions: EMG-based HIL optimization significantly enhances controller tuning speed and effectiveness, demonstrating its potential for scalable and user-specific exoskeleton applications.