Facial Video based Detection of Physical Fatigue for Maximal Muscle Activity

Mohammad Ahsanul Haque, Ramin Irani, Kamal Nasrollahi, Thomas B. Moeslund

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

26 Citationer (Scopus)
927 Downloads (Pure)


Physical fatigue reveals the health condition of a person at for example health checkup, fitness assessment or rehabilitation training. This paper presents an efficient noncontact system for detecting non-localized physi-cal 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 utilizes a facial feature point tracking method by combining a ‘Good feature to track’ and a ‘Supervised descent method’ to address the challenges originates from realistic sce-nario. 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.
TidsskriftIET Computer Vision
Udgave nummer4
Sider (fra-til)323-329
StatusUdgivet - 2016

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