Dynamic personalized human body energy expenditure: Prediction using time series forecasting LSTM models

•Dynamic modeling of energy expenditure can capture transient thermoregulation and food intake effect.•LSTM networks promise high accuracy of time-series data prediction and capturing patterns.•Adequate accuracy of dynamic EE prediction should be within 5–10 % of MAPE.•The ensemble of CNN-LSTM and L...

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Bibliographic Details

Published in:	Biomedical signal processing and control Vol. 87; p. 105381
Main Authors:	Perez Cortes, Victoria M., Chatterjee, Arnab, Khovalyg, Dolaana
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.01.2024
Subjects:	Dynamic metabolism Human energy expenditure Machine learning Personalized model Recurrent neural networks Time series Wearable sensing Recurrent neural networks Machine learning Time series Human energy expenditure Wearable sensing Dynamic metabolism Personalized model
ISSN:	1746-8094, 1746-8108
Online Access:	Get full text
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