Estimating exercise-induced fatigue from thermal facial images
This addresses health monitoring for athletes or individuals by providing a non-invasive fatigue detection method, though it appears incremental as it applies existing deep learning techniques to a new dataset.
The paper tackled the problem of estimating exercise-induced fatigue from thermal facial images using deep learning, achieving prediction with an average error smaller than 15% from a single static frame.
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.