Estimating Exercise-Induced Fatigue from Thermal Facial Images
Lage Cañellas, Manuel; Álvarez Casado, Constantino; Nguyen, Le; Bordallo López, Miguel (2024-03-18)
IEEE
18.03.2024
M. L. Cañellas, C. Álvarez Casado, L. Nguyen and M. B. López, "Estimating Exercise-Induced Fatigue from Thermal Facial Images," ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Seoul, Korea, Republic of, 2024, pp. 2800-2804, doi: 10.1109/ICASSP48485.2024.10447613.
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202404182834
https://urn.fi/URN:NBN:fi:oulu-202404182834
Tiivistelmä
Abstract
Exercise-induced fatigue resulting from physical activity can be an early indicator of overtraining, illness, or other health issues. In this paper, we present an automated method for estimating exercise-induced fatigue levels through the use of thermal imaging and facial analysis techniques utilizing deep learning models. Leveraging a novel dataset comprising over 400,000 thermal facial images of rested and fatigued users, our results suggest that exercise-induced fatigue levels could be predicted with only one static thermal frame with an average error smaller than 15%. The results emphasize the viability of using thermal imaging in conjunction with deep learning for reliable exercise-induced fatigue estimation.
Exercise-induced fatigue resulting from physical activity can be an early indicator of overtraining, illness, or other health issues. In this paper, we present an automated method for estimating exercise-induced fatigue levels through the use of thermal imaging and facial analysis techniques utilizing deep learning models. Leveraging a novel dataset comprising over 400,000 thermal facial images of rested and fatigued users, our results suggest that exercise-induced fatigue levels could be predicted with only one static thermal frame with an average error smaller than 15%. The results emphasize the viability of using thermal imaging in conjunction with deep learning for reliable exercise-induced fatigue estimation.
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