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Collective Sensing of Workers' Loss of Body Balance for Slip, Trip, and Fall Hazard Identification: Field Validation Study

DC Field Value Language
dc.contributor.authorLee, Hoonyong-
dc.contributor.authorLee, Gaang-
dc.contributor.authorPark, Seongeun-
dc.contributor.authorLee, SangHyun-
dc.contributor.authorJacobs, Jesse V. V.-
dc.contributor.authorAhn, Changbum-
dc.date.accessioned2023-02-27T04:51:00Z-
dc.date.available2023-02-27T04:51:00Z-
dc.date.created2023-01-05-
dc.date.created2023-01-05-
dc.date.created2023-01-05-
dc.date.issued2023-01-
dc.identifier.citationJournal of Computing in Civil Engineering, Vol.37 No.1, p. 04022052-
dc.identifier.issn0887-3801-
dc.identifier.urihttps://hdl.handle.net/10371/189164-
dc.description.abstractManual hazard identification by safety managers in construction has practical challenges because each manager identifies environmental hazards from their perception, which can leave many potential hazards unidentified and consequently lead to accidents at the site. Previous studies have revealed that workers experience loss of body balance (LOB) when exposed to slip, trip, and fall (STF) hazards. This study extended previous studies to identify STF hazards by LOB measurement and collective sensing (i.e., data aggregation) techniques and assumed that STF hazards would cause multiple workers' LOBs in a given location. First, this study developed an approach to assess each worker's exposure to STF hazards by LOB analysis. A waist-worn inertial measurement unit sensor was used to extract features of waist movements, which were mapped into a single value to measure LOB scores using the Mahalanobis distance (MD) metric. As an individual worker is exposed to STF hazards, the MD values become larger than without exposure to STF hazards. The developed approach provided an unweighted average recall of 89.13% (without exposures: 90.30%, and with exposures: 87.96%) for detecting individual workers' exposures to STF hazards in an actual construction site. Then, an approach was developed to visualize the location of STF hazards by allocating multiple workers' LOB scores into each individual's Global Positioning System (GPS) data points. The results showed the feasibility of the developed approach to identify STF hazards, potentially helping to prevent STF accidents at construction sites.-
dc.language영어-
dc.publisherAmerican Society of Civil Engineers-
dc.titleCollective Sensing of Workers' Loss of Body Balance for Slip, Trip, and Fall Hazard Identification: Field Validation Study-
dc.typeArticle-
dc.identifier.doi10.1061/JCCEE5.CPENG-4938-
dc.citation.journaltitleJournal of Computing in Civil Engineering-
dc.identifier.wosid000886621600006-
dc.identifier.scopusid2-s2.0-85141881062-
dc.citation.number1-
dc.citation.startpage04022052-
dc.citation.volume37-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorAhn, Changbum-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.subject.keywordPlusDETECTION ALGORITHM-
dc.subject.keywordPlusGAIT PATTERNS-
dc.subject.keywordPlusACCELEROMETERS-
dc.subject.keywordPlusINJURIES-
dc.subject.keywordAuthorConstruction safety-
dc.subject.keywordAuthorHazard identification-
dc.subject.keywordAuthorLoss of body balance (LOB) analysis-
dc.subject.keywordAuthorSpatial analysis-
dc.subject.keywordAuthorWearable computing-
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  • College of Engineering
  • Department of Architecture & Architectural Engineering
Research Area Computing in Construction, Management in Construction

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