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Abstract

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.
Original languageEnglish
JournalIET Computer Vision
Volume10
Issue number4
Pages (from-to)323-329
ISSN1751-9632
DOIs
Publication statusPublished - 2016

Fingerprint

Muscle
Fatigue of materials
Lighting
Health
Patient rehabilitation

Bibliographical note

This paper is a preprint of a paper accepted by IET Computer Vision Journal and is subject to Institution of Engineering and Technology Copyright. When the final version is published, the copy of record will be available at IET Digital Library

Cite this

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title = "Facial Video based Detection of Physical Fatigue for Maximal Muscle Activity",
abstract = "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.",
author = "Haque, {Mohammad Ahsanul} and Ramin Irani and Kamal Nasrollahi and Moeslund, {Thomas B.}",
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Facial Video based Detection of Physical Fatigue for Maximal Muscle Activity. / Haque, Mohammad Ahsanul; Irani, Ramin; Nasrollahi, Kamal; Moeslund, Thomas B.

In: IET Computer Vision, Vol. 10, No. 4, 2016, p. 323-329.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Facial Video based Detection of Physical Fatigue for Maximal Muscle Activity

AU - Haque, Mohammad Ahsanul

AU - Irani, Ramin

AU - Nasrollahi, Kamal

AU - Moeslund, Thomas B.

N1 - This paper is a preprint of a paper accepted by IET Computer Vision Journal and is subject to Institution of Engineering and Technology Copyright. When the final version is published, the copy of record will be available at IET Digital Library

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

U2 - 10.1049/iet-cvi.2015.0215

DO - 10.1049/iet-cvi.2015.0215

M3 - Journal article

VL - 10

SP - 323

EP - 329

JO - IET Computer Vision

JF - IET Computer Vision

SN - 1751-9632

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ER -