Estimating exercise-induced fatigue from thermal facial images

TL;DR

This paper introduces a deep learning-based method to estimate exercise-induced fatigue from thermal facial images, demonstrating that fatigue levels can be predicted accurately using a single thermal frame.

Contribution

The study presents a novel approach combining thermal imaging and deep learning to estimate fatigue, supported by a large dataset of over 400,000 images.

Findings

Fatigue levels predicted with less than 15% error

Single thermal frame suffices for accurate estimation

Thermal imaging is viable for fatigue assessment

Abstract

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.