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 language | English |
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Title of host publication | 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Pages | 2800-2804 |
Number of pages | 5 |
ISBN (Electronic) | 9798350344851 |
DOIs | |
Publication status | Published - 2024 |
MoE publication type | A4 Article in a conference publication |
Event | 49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Seoul, Korea, Republic of Duration: 14 Apr 2024 → 19 Apr 2024 |
Conference
Conference | 49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 |
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Country/Territory | Korea, Republic of |
City | Seoul |
Period | 14/04/24 → 19/04/24 |
Keywords
- deep learning
- Fatigue detection
- thermal imaging