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Facial video-based detection of physical fatigue for maximal muscle activity

Facial video-based detection of physical fatigue for maximal muscle activity

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Physical fatigue reveals the health condition of a person at, for example, health checkup, fitness assessment, or rehabilitation training. This study presents an efficient non-contact system for detecting non-localised physical fatigue from maximal muscle activity using facial videos acquired in a realistic environment with natural lighting where subjects were allowed to voluntarily move their head, change their facial expression, and vary their pose. The proposed method utilises a facial feature point tracking method by combining a ‘good feature to track’ and a ‘supervised descent method’ to address the challenges that originate from realistic scenario. A face quality assessment system was also incorporated in the proposed system to reduce erroneous results by discarding low quality faces that occurred in a video sequence due to problems in realistic lighting, head motion, and pose variation. Experimental results show that the proposed system outperforms video-based existing system for physical fatigue detection.

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