Estimating exercise-induced fatigue from thermal facial images
Manuel Lage Cañellas Constantino Álvarez Casado Le Nguyen Miguel Bordallo López
Exercise-induced fatigue resulting from physical activity can be an early
indicator of overtraining, illness, or other health issues. In this article, 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.