Estimating exercise-induced fatigue from thermal facial images
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Abstract (arXiv)
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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References · 7
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2022A ConvNet for the 2020scited by 3,579
- 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 903
- 2019On the Variance of the Adaptive Learning Rate and Beyondcited by 772
- 2019Lookahead Optimizer: k steps forward, 1 step backcited by 336
- 2021Sample and Computation Redistribution for Efficient Face Detectioncited by 89
- 2022Improving Depression estimation from facial videos with face alignment, training optimization and schedulingcited by 2
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