Detecting exercise-induced fatigue using thermal imaging and deep learning

Fatigue has adverse effects in both physical and cognitive abilities. Hence, automatically detecting exercise-induced fatigue is of importance, especially in order to assist in the planning of effort and resting during exercise sessions. Thermal imaging and facial analysis provide a mean to detect changes in the human body unobtrusively and in variant conditions of pose and illumination. In this context, this paper proposes the automatic detection of exercise-induced fatigue using thermal cameras and facial images, analyzing them using deep convolutional neural networks. Our results indicate that classification of fatigued individuals is possible, obtaining an accuracy that reaches over 80% when utilizing single thermal images.