Estimating Exercise-Induced Fatigue from Thermal Facial Images

Manuel Lage Cañellas, Constantino Álvarez Casado, Nguyen Le Nguyen, Miguel Bordallo López

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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.
Original languageEnglish
Title of host publication2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages2800-2804
Number of pages5
ISBN (Electronic)9798350344851
DOIs
Publication statusPublished - 2024
MoE publication typeA4 Article in a conference publication
Event49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Seoul, Korea, Republic of
Duration: 14 Apr 202419 Apr 2024

Conference

Conference49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024
Country/TerritoryKorea, Republic of
CitySeoul
Period14/04/2419/04/24

Keywords

  • deep learning
  • Fatigue detection
  • thermal imaging

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