Objective: Current methods for measuring metabolic energy expenditure (MEE) constrain experiment design and only provide time-averaged values.We propose a novel two-stage predictive model using surrogate learners in the first stage for physiological dynamics and gradient-boosted regression trees g5210t-p90 in the second stage to learn a generalized representation of instantaneous, whole-body MEE.Methods: Kinematic, kinetic, metabolic, and surface electromyograph data were recorded for nine human subjects in level, over-ground walking at 100%, 70%, 85%, 115%, and 130% subject-preferred speeds.We use surrogate learners to encode fundamental information about the time-varying properties of MEE.
A gradient-boosted machine-learning model was then trained on the surrogate functions’ outputs.For robustness, an information-theoretic data selection step was added during model training.The trained model uses joint torques and angular velocities to predict instantaneous, whole-body MEE during walking.Results: The model accurately predicts instantaneous MEE without subject-specific input parameters.
Shapley Additive Explanations were used to investigate energetic features of the learned MEE function and demonstrate alignment with literature.We find Sneakers for Men - Grey - Canvas Mesh Athletic Running Shoes similarities between the model’s MEE predictions, muscle mechanical work rate, and normal ground reaction forces, suggesting a link between MEE and the work required to raise the center of mass.Conclusion: The proposed approach provides an alternative to experimental MEE measurement while balancing the generalizability and complexity trade-off typically imposed on existing computational, predictive models.Significance: Evaluating MEE of human motion can provide insight into underlying biomechanics and inform clinical and engineering practices.