Detecting exercise-induced fatigue using thermal imaging and deep learning

Miguel Bordallo Lopez, Carlos R del-Blanco, Narciso Garcia

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

3 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publication 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA)
PublisherIEEE Institute of Electrical and Electronic Engineers
Number of pages6
ISBN (Electronic)978-1-5386-1842-4
DOIs
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication

Keywords

  • fatigue detection
  • facial expression
  • deep learning
  • thermal imaging

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    Bordallo Lopez, M., del-Blanco, C. R., & Garcia, N. (2017). Detecting exercise-induced fatigue using thermal imaging and deep learning. In 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) IEEE Institute of Electrical and Electronic Engineers. https://doi.org/10.1109/IPTA.2017.8310151