# Estimating exercise-induced fatigue from thermal facial images

arXiv: https://arxiv.org/abs/2309.06095 · HTML: https://mlsanity.com/paper/2309.06095/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Lookahead Optimizer: k steps forward, 1 step back](https://mlsanity.com/paper/1907.08610/) (2019)
- [Sample and Computation Redistribution for Efficient Face Detection](https://mlsanity.com/paper/2105.04714/) (2021)
- [Improving Depression estimation from facial videos with face alignment, training optimization and scheduling](https://mlsanity.com/paper/2212.06400/) (2022)

## Abstract (arXiv)

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.