Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/87715
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dc.contributorDepartment of Building and Real Estateen_US
dc.contributorDepartment of Rehabilitation Sciencesen_US
dc.creatorYu, Yen_US
dc.creatorLi, Hen_US
dc.creatorYang, Xen_US
dc.creatorKong, Len_US
dc.creatorLuo, Xen_US
dc.creatorWong, AYLen_US
dc.date.accessioned2020-08-03T06:09:50Z-
dc.date.available2020-08-03T06:09:50Z-
dc.date.issued2019en
dc.identifier.citationAutomation in construction, 2019, v. 103, p. 1-12-
dc.identifier.issn0926-5805en_US
dc.identifier.otherAutomation in construction, 2019, v. 103, p. 1-12-
dc.identifier.otherAutomation in construction, 2019, v. 103, p. 1-12-
dc.identifier.urihttp://hdl.handle.net/10397/87715-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights© 2019 Published by Elsevier B.V.en US
dc.rights© 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.en US
dc.rightsThe following publication Yu, Y., Li, H., Yang, X., Kong, L., Luo, X., & Wong, A. Y. L. (2019). An automatic and non-invasive physical fatigue assessment method for construction workers. Automation in Construction, 103, 1-12 is available at https://dx.doi.org/10.1016/j.autcon.2019.02.020.en US
dc.subjectComputer visionen_US
dc.subjectConstruction workeren_US
dc.subjectDeep learningen_US
dc.subjectErgonomicen_US
dc.subjectMachine learningen_US
dc.subjectOccupational safety and healthen_US
dc.titleAn automatic and non-invasive physical fatigue assessment method for construction workersen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1en_US
dc.identifier.epage12en_US
dc.identifier.volume103en_US
dc.identifier.doi10.1016/j.autcon.2019.02.020en_US
dcterms.abstractThe construction industry around the globe has unsatisfactory occupational health and safety records. One of the major reasons is attributed to high physical demands and hostile working environments. Construction work always requires workers to work for a long duration without sufficient breaks to recover from overexertion and to work under harsh climatic conditions and/or in confined workspaces. Such circumstances can increase the risk of physical fatigue. Traditionally, fatigue monitoring in the construction domain relies on self-reporting or subjective questionnaires. These methods require the manual collection of responses and are impractical for continuous fatigue monitoring. Some researchers have used on-body sensors for fatigue monitoring (such as heart rate monitors and surface electromyography (sEMG) sensors). Although these devices appear to be promising, they are intrusive, requiring sensors to be attached to the worker's body. Such on-body sensors are uncomfortable to wear and could easily cause irritation. Considering the limitations of these methodologies, the current research proposes a novel non-intrusive method to monitor the whole-body physical fatigue with computer vision for construction workers. A computer vision-based 3D motion capture algorithm was developed to model the motion of various body parts using an RGB camera. A fatigue assessment model was developed using the 3D model data from the developed motion capture algorithm and biomechanical analysis. The experiment showed that the proposed physical fatigue assessment method could provide joint-level physical fatigue assessments automatically. Then, a series of experiments demonstrated the potential of the method in assessing the physical fatigue level of different construction task conditions such as site layout and the work-rest schedules.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationAutomation in construction, July 2019, v. 103, p. 1-12en_US
dcterms.isPartOfAutomation in constructionen_US
dcterms.issued2019-07-
dc.identifier.scopus2-s2.0-85062640800-
dc.identifier.eissn1872-7891en_US
dc.description.validate202008 bcrcen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0456-n02, a0829-n36-
dc.identifier.SubFormID2048-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextRGC: 152099/18E||Others: ITP/020/18LPen_US
dc.description.pubStatusPublisheden_US
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