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.

翻译：运动性疲劳作为体力活动的结果，可能是过度训练、疾病或其他健康问题的早期指标。本文提出一种自动化方法，通过热成像技术与基于深度学习模型的面部分析技术来评估运动性疲劳水平。基于包含40万余张热成像面部图像（涵盖休息状态与疲劳状态用户）的新型数据集，我们的研究结果表明，仅凭单张静态热成像帧即可预测运动性疲劳水平，平均误差小于15%。该结果凸显了热成像与深度学习相结合用于可靠评估运动性疲劳的可行性。