Evaluating Computational Approaches to Metabolic Cost Estimation in Gait Assistance with a Passive Exosuit*

Abstract

Lower limb exoskeletons and exosuits have shown promise in augmenting human physical capabilities, with applications ranging from rehabilitation to performance enhancement. Accurate evaluation of their impact on metabolic energy expenditure is crucial for optimizing design and control strategies. While experimental measurement of metabolic cost via indirect calorimetry provides direct assessment, it is often impractical outside laboratory settings. Computational models offer an alternative, but their effectiveness in predicting metabolic cost changes induced by assistive devices remains underexplored. This study investigates the impact of incorporating different levels of complexity and sensory information, as well as various metabolic cost models, on estimating muscle metabolic cost during walking with a passive biarticular thigh exosuit. We compare three modeling approaches: joint-space dynamics, musculoskeletal simulation with effort minimization, and EMG-informed musculoskeletal simulation, each employing several metabolic models. Results show that EMG-informed musculoskeletal simulation, particularly using the Uchida (2016) metabolic model, provides the highest accuracy in predicting metabolic cost changes. Musculoskeletal simulation with effort minimization also shows promise, offering a viable alternative without the need for EMG data. These findings highlight the potential of computational models in evaluating and optimizing assistive devices.