Simulation-Driven Exoskeleton Control: Predicting Soft Pneumatic Gel Muscle Actuator Assistance to Reduce Metabolic Cost at Different Walking Speeds.

Wearable exoskeletons have emerged as a solution to enhance locomotion in individuals with impairments and/or weakness. Assistive devices with pneumatic gel muscle actuators (PGMs) are promising for daily use due to their high power-to-weight ratio and compliant structure, enabling potentially easy integration into smart garments. The intrinsic properties of PGMs have been studied over the past decade; however, little is known about how to leverage their dynamics to effectively and optimally assist motion. In this study, we modeled hip joint assistance via two PGMs at each user's leg and employed musculoskeletal simulations to predict optimal assistive strategies that reduce metabolic costs during walking at various speeds. Specifically, we implemented a bilevel optimization framework to identify optimal control parameters: stiffness, onset time, and duration, under two control modes: coupled and independent, at three actuator placements: medial, neutral, and lateral, relative to the user's hip joint center. Our results showed that, across walking speeds, PGM actuators with coupled control mode reduced estimated metabolic cost by 5.3-16.0% and with independent control mode by 10.5-17.5%. We also identified that PGM assistance with medial placement with coupled control mode offered the best trade-off between control simplicity and potential metabolic savings at slow walking speeds, which might be particularly useful for enhancing mobility in older adults and in rehabilitation settings. Also, our simulation suggested that neutral placement tended to outperform other actuator placements across speeds in terms of metabolic savings. Future experimental studies may benefit from guiding exoskeleton control as per the predicted assistive strategies in this work.