Estimating exercise-induced fatigue from thermal facial images

2023/09/12 by Manuel Lage Cañellas, Constantino Álvarez Casado, Cañellas, Manuel Lage +5
Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Infrared Thermography in Medicine #Thermoregulation and physiological responses

paper · pdf · doi:10.48550/arxiv.2309.06095

openalex publication_date 2023/09/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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