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.