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    Identifying Safety Hazards Using Collective Bodily Responses of Workers

    Source: Journal of Construction Engineering and Management:;2017:;Volume ( 143 ):;issue: 002
    Author:
    Hyunsoo Kim
    ,
    Changbum R. Ahn
    ,
    Kanghyeok Yang
    DOI: 10.1061/(ASCE)CO.1943-7862.0001220
    Publisher: American Society of Civil Engineers
    Abstract: Current construction hazard identification mostly relies on safety managers’ ability to identify hazards using their prior knowledge about them. Consequently, numerous latent hazards remain unidentified, which poses significant risks to construction workers. To advance current hazard identification capabilities, this study examines the feasibility of harnessing and analyzing collective patterns of workers’ bodily responses (balance, gait, etc.) to identify safety hazards on a jobsite. To test the hypothesis that the abnormality of workers’ bodily responses in one location highly correlates with the likelihood of a safety hazard in that location, this project collected data on the bodily responses of 10 subjects who participated in five experiments. These test subjects wore inertial measurement unit (IMU) sensors on their body. Then the collected response data were analyzed using three metrics [average, standard deviation, and Shapiro-Wilk statistic (W)]. The data showed that the normality of workers’ bodily response distributions—represented as a W statistic—highly correlated with hazard locations in every experiment, which implies that workers’ bodily responses in hazardous areas are more irregularly distributed than in nonhazardous areas. This outcome demonstrates an opportunity for utilizing workers’ collective bodily responses to identify safety hazards in diverse construction environments.
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      Identifying Safety Hazards Using Collective Bodily Responses of Workers

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4241281
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    contributor authorHyunsoo Kim
    contributor authorChangbum R. Ahn
    contributor authorKanghyeok Yang
    date accessioned2017-12-16T09:18:39Z
    date available2017-12-16T09:18:39Z
    date issued2017
    identifier other%28ASCE%29CO.1943-7862.0001220.pdf
    identifier urihttp://138.201.223.254:8080/yetl1/handle/yetl/4241281
    description abstractCurrent construction hazard identification mostly relies on safety managers’ ability to identify hazards using their prior knowledge about them. Consequently, numerous latent hazards remain unidentified, which poses significant risks to construction workers. To advance current hazard identification capabilities, this study examines the feasibility of harnessing and analyzing collective patterns of workers’ bodily responses (balance, gait, etc.) to identify safety hazards on a jobsite. To test the hypothesis that the abnormality of workers’ bodily responses in one location highly correlates with the likelihood of a safety hazard in that location, this project collected data on the bodily responses of 10 subjects who participated in five experiments. These test subjects wore inertial measurement unit (IMU) sensors on their body. Then the collected response data were analyzed using three metrics [average, standard deviation, and Shapiro-Wilk statistic (W)]. The data showed that the normality of workers’ bodily response distributions—represented as a W statistic—highly correlated with hazard locations in every experiment, which implies that workers’ bodily responses in hazardous areas are more irregularly distributed than in nonhazardous areas. This outcome demonstrates an opportunity for utilizing workers’ collective bodily responses to identify safety hazards in diverse construction environments.
    publisherAmerican Society of Civil Engineers
    titleIdentifying Safety Hazards Using Collective Bodily Responses of Workers
    typeJournal Paper
    journal volume143
    journal issue2
    journal titleJournal of Construction Engineering and Management
    identifier doi10.1061/(ASCE)CO.1943-7862.0001220
    treeJournal of Construction Engineering and Management:;2017:;Volume ( 143 ):;issue: 002
    contenttypeFulltext
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