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

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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Bibliographic Details
Main Authors: Christopher Buglino, William Z. Peng, Stacy Ashlyn, Hyunjong Song, Howard J. Hillstrom, Joo H. Kim
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Electromyography
gradient boosting
instantaneous metabolic energy expenditure
joint space
machine learning
predictive model
Online Access:https://ieeexplore.ieee.org/document/10942381/
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