TY - JOUR
T1 - Instantaneous Metabolic Energetics
T2 - Data-Driven Modeling Using Function-Based Surrogates and Gradient Boosting
AU - Buglino, Christopher
AU - Peng, William Z.
AU - Ashlyn, Stacy
AU - Song, Hyunjong
AU - Hillstrom, Howard J.
AU - Kim, Joo H.
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2025
Y1 - 2025
N2 - 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 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 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.
AB - 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 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 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.
KW - Electromyography
KW - gradient boosting
KW - instantaneous metabolic energy expenditure
KW - joint space
KW - machine learning
KW - predictive model
KW - surrogate methods
KW - walking
UR - http://www.scopus.com/inward/record.url?scp=105002397425&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=105002397425&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2025.3555182
DO - 10.1109/ACCESS.2025.3555182
M3 - Article
AN - SCOPUS:105002397425
SN - 2169-3536
VL - 13
SP - 56793
EP - 56807
JO - IEEE Access
JF - IEEE Access
ER -