Instantaneous Metabolic Energetics: Data-Driven Modeling Using Function-Based Surrogates and Gradient Boosting

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...

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Vydáno v:	IEEE access Ročník 13; s. 56793 - 56807
Hlavní autoři:	Buglino, Christopher, Peng, William Z., Ashlyn, Stacy, Song, Hyunjong, Hillstrom, Howard J., Kim, Joo H.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Angular velocity Biological system modeling Biomechanics Computational modeling Data models Design of experiments Electromyography Force gradient boosting Human motion Information theory instantaneous metabolic energy expenditure joint space Kinematics Legged locomotion Machine learning Mathematical models Measurement methods Metabolism Muscles Prediction models predictive model Predictive models Regression analysis surrogate methods Walking
ISSN:	2169-3536, 2169-3536
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Abstract	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.
AbstractList	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.
Author	Buglino, Christopher Ashlyn, Stacy Hillstrom, Howard J. Kim, Joo H. Song, Hyunjong Peng, William Z.
Author_xml	– sequence: 1 givenname: Christopher orcidid: 0009-0004-0958-6041 surname: Buglino fullname: Buglino, Christopher organization: Department of Mechanical and Aerospace Engineering, New York University (NYU), Brooklyn, NY, USA – sequence: 2 givenname: William Z. orcidid: 0000-0002-3612-3315 surname: Peng fullname: Peng, William Z. organization: Department of Mechanical and Aerospace Engineering, New York University (NYU), Brooklyn, NY, USA – sequence: 3 givenname: Stacy surname: Ashlyn fullname: Ashlyn, Stacy organization: Department of Mechanical and Aerospace Engineering, New York University (NYU), Brooklyn, NY, USA – sequence: 4 givenname: Hyunjong orcidid: 0000-0002-3470-8221 surname: Song fullname: Song, Hyunjong organization: Department of Mechanical and Aerospace Engineering, New York University (NYU), Brooklyn, NY, USA – sequence: 5 givenname: Howard J. orcidid: 0000-0002-6440-1917 surname: Hillstrom fullname: Hillstrom, Howard J. organization: Leon Root, MD Motion Analysis Laboratory, Hospital for Special Surgery, New York City, NY, USA – sequence: 6 givenname: Joo H. orcidid: 0000-0003-0305-5405 surname: Kim fullname: Kim, Joo H. email: joo.h.kim@nyu.edu organization: Department of Mechanical and Aerospace Engineering, New York University (NYU), Brooklyn, NY, USA
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SubjectTerms	Angular velocity Biological system modeling Biomechanics Computational modeling Data models Design of experiments Electromyography Force gradient boosting Human motion Information theory instantaneous metabolic energy expenditure joint space Kinematics Legged locomotion Machine learning Mathematical models Measurement methods Metabolism Muscles Prediction models predictive model Predictive models Regression analysis surrogate methods Walking
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Title	Instantaneous Metabolic Energetics: Data-Driven Modeling Using Function-Based Surrogates and Gradient Boosting
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