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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE access Jg. 13; S. 56793 - 56807
Hauptverfasser:	Buglino, Christopher, Peng, William Z., Ashlyn, Stacy, Song, Hyunjong, Hillstrom, Howard J., Kim, Joo H.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!