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Deep Learning for Metabolic Rate Estimation from Biosignals: A Comparative Study of Architectures and Signal Selection

arXiv — cs.CV•Thursday, November 13, 2025 at 5:00:00 AM
NeutralArtificial Intelligence
The study titled 'Deep Learning for Metabolic Rate Estimation from Biosignals: A Comparative Study of Architectures and Signal Selection' published on arXiv explores the effectiveness of deep learning in estimating human metabolic rates from physiological signals. It systematically evaluates various neural architectures and signal combinations, revealing that minute ventilation is the most predictive individual signal. The transformer model outperformed others with a root mean square error (RMSE) of 0.87 W/kg across diverse physical activities. Notably, the research indicates that paired and grouped signals, such as those from the Hexoskin smart shirt, can enhance model performance, particularly for faster architectures like CNN and ResNet. The findings underscore the importance of adaptive modeling strategies due to strong inter-individual variability, especially in low-intensity activities where RMSE can drop to 0.29 W/kg. This study not only contributes to the field of energy expend…
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