Lower-limb mechanical power accounts for running energy expenditure and enables single-IMU estimation

Abstract

Energy expenditure (EE) during running depends on the interplay between active muscle work and elastic energy storage and return, yet the relative contribution of mechanical power to EE remains debated. Quantifying the relative contributions of segment-level mechanical power can provide a way to address this debate. In this study, we aimed to quantify how segment-level mechanical power contributes to EE during running and to demonstrate that these mechanistic insights support wearable-based EE estimation. Joint dynamics and respiratory gas-based EE were collected from healthy young adults running at multiple speeds. Scale factors were derived to quantitatively link efficiency-weighted segment power to measured EE. The stance leg consistently showed the strongest correlation with EE, and this dominance was preserved across speeds. Including swing-leg hip power further improved accuracy. Scale factors were approximately 0.45, suggesting that active muscle work and elastic energy return contribute comparably to the mechanical power associated with EE. Using a lightweight machine learning model, stance-leg and swing-leg hip joint power were reconstructed from a single sacral IMU, enabling accurate EE prediction. These findings demonstrate that lower-limb mechanical power is a robust predictor of running EE, supporting both the extensibility of biomechanically-informed frameworks and wearable-based EE monitoring.