Simulating Muscle-Level Energetic Cost When Humans Walk With a Passive Biarticular Thigh Exosuit

Vahid Firouzi,Seungmoon Song,O. von Stryk,A. Seyfarth,Maziar Ahmad Sharbafi

Published 2026 in IEEE transactions on neural systems and rehabilitation engineering

ABSTRACT

Passive exosuits offer potential for reducing the metabolic cost of walking, but evaluating their effectiveness is challenging due to difficulties in measuring internal biomechanics and muscle-level energetics. This study investigates the metabolic effects of the BATEX, a passive biarticular thigh exosuit assisting hip and knee flexion/extension. The exosuit features a spring parallel to the rectus femoris (RF) and a spring parallel to the hamstring (HAM). We combined experimental data from ten healthy adults walking on a treadmill under various exosuit conditions (different stiffness and configurations) with EMG-informed musculoskeletal simulations to estimate muscle-level behavior. The simulations aimed to replicate experimental metabolic measures, analyze cost fluctuations across the gait cycle, and elucidate muscle-tendon energetic responses, comparing individuals experiencing reductions (positive responders, PR) versus increases (negative responders, NR) in metabolic cost. Simulation results successfully replicated experimental whole-body metabolic cost trends for most participants (8/10, ${r} \gt {0}.{7}$ , ${p}\lt {0}.{05}$ ), showing comparable overall net reductions primarily driven by significant savings during the swing phase. Combining RF and HAM springs resulted in complex, non-additive interactions. Muscle-level analysis identified both targeted effects on hip/knee flexors/extensors and non-targeted effects on ankle and hip ab/adductor muscles, with responses differing markedly between PR and NR groups. Distinct energy-saving mechanisms were associated with each spring type (RF: reduced activation; HAM: reduced fiber velocity). The differing responses of the PR and NR groups to the same device setup motivate personalization, and our muscle-level insights offer a foundation for future device refinement.

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