Musculoskeletal simulation of load placement and speed effects on gait dynamics and energetics

This study investigates the biomechanical and energetic effects of load placement (ankle vs. back) on human walking and running across a spectrum of speeds, addressing a gap in speed-dependent quantification of muscle and joint adaptations. We used a direct collocation optimal control framework integrated with a musculoskeletal model to simulate gait under varied conditions. Simulations covered walking (1.1, 1.3, 1.5 m/s) and running (2.5, 3.0, 3.5 m/s) with ankle-attached loads (1–5% body weight) and back-mounted loads (10–50% body weight). The simulations revealed distinct adaptation strategies. Ankle loading primarily increased swing-phase muscle activation. Conversely, back loading enhanced stance-phase extensor activity and altered joint kinetics. A key finding is that back loading was associated with a relative improvement in simulated transport economy compared with ankle loading. The metabolic demand associated with ankle loads was comparable to that of substantially heavier back loads across both walking and running. Load placement substantially influenced muscle coordination strategy and relative energetic outcomes during locomotion. These findings provide a mechanistic, simulation‑based framework for understanding trade‑offs in loaded gait by linking load placement and magnitude to phase‑specific changes in kinematics, kinetics, muscle recruitment, joint reaction forces, and energetic cost. The resulting simulation‑derived mappings can serve as virtual pre‑tests to screen load‑carriage designs and to generate testable hypotheses for targeted experimental validation and training prescriptions.