ESMAC Best Paper 2024: Defining exoskeleton aim matters: Simulating optimal assistive moments with explicit objectives using bilevel optimization.

INTRODUCTION: Musculoskeletal simulations can guide the search for optimal strategies to assist motion and reveal causal relationships between assistive moments and muscle dynamics. Assistive devices such as exoskeletons can complement muscle forces based on various aims, such as minimum muscle effort or maximal force distribution. In this study, we present a simulation framework to systematically identify optimal assistance, formulated as a bilevel optimization in a single inverse simulation scheme that seeks optimal assistive moments that fulfill different assistive device aims.

METHODS: Bilevel optimization of assistive moment was structured as an inner optimization problem to solve the muscle redundancy problem nested within an outer optimization problem that executes the inner problem iteratively, seeking an assistive moment that best satisfies the assistive aim. We used this framework to predict optimal ankle plantarflexion, hip extension, hip flexion, and hip abduction assistance, for three different aims: minimal muscle activations, minimal metabolic rates, and minimal muscle moments. Experimental data from twelve participants walking at preferred speed were used in this study.

RESULTS: We found that the optimal moment trajectory is unique for a given assistive aim; i.e., the assistive aim matters. Differences in the assistive trajectories are explained at the muscle level, and as active and passive force contributions to the net muscle moments and muscle mechanical work. Interestingly, the assistive moments for minimal metabolic rates predicted an assistance period and peak timing similar to those reported from experimental studies.

CONCLUSIONS: Our findings suggest that explicit assistive aim formulation is required to investigate human-device interaction under optimal assistance.