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Semi-supervised near-miss fall detection for ironworkers with a wearable inertial measurement unit
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yang, Kanghyeok | - |
dc.contributor.author | Ahn, Changbum R. | - |
dc.contributor.author | Vuran, Mehmet C. | - |
dc.contributor.author | Aria, Sepideh S. | - |
dc.date.accessioned | 2024-05-17T08:03:53Z | - |
dc.date.available | 2024-05-17T08:03:53Z | - |
dc.date.created | 2024-05-16 | - |
dc.date.created | 2024-05-16 | - |
dc.date.issued | 2016-08 | - |
dc.identifier.citation | Automation in Construction, Vol.68, pp.194-202 | - |
dc.identifier.issn | 0926-5805 | - |
dc.identifier.uri | https://hdl.handle.net/10371/203277 | - |
dc.description.abstract | Accidental falls (slips, trips, and falls from height) are the leading cause of occupational death and injury in construction. As a proactive accident prevention measure, near miss can provide valuable data about the causes of accidents, but collecting near-miss information is challenging because current data collection systems can largely be affected by retrospective and qualitative decisions of individual workers. In this context, this study aims to develop a method that can automatically detect and document near-miss falls based upon a worker's kinematic data captured from wearable inertial measurement units (WIMUs). A semi-supervised learning algorithm (i.e., one-class support vector machine) was implemented for detecting the near-miss falls in this study. Two experiments were conducted for collecting the near-miss falls of ironworkers, and these data were used to test developed near-miss fall detection approach. This WIMU-based approach will help identify ironworker near-miss falls without disrupting jobsite work and can help prevent fall accidents. | - |
dc.language | 영어 | - |
dc.publisher | Elsevier B.V. | - |
dc.title | Semi-supervised near-miss fall detection for ironworkers with a wearable inertial measurement unit | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.autcon.2016.04.007 | - |
dc.citation.journaltitle | Automation in Construction | - |
dc.identifier.wosid | 000379371100017 | - |
dc.identifier.scopusid | 2-s2.0-84967315988 | - |
dc.citation.endpage | 202 | - |
dc.citation.startpage | 194 | - |
dc.citation.volume | 68 | - |
dc.description.isOpenAccess | Y | - |
dc.contributor.affiliatedAuthor | Ahn, Changbum R. | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | ACCIDENT | - |
dc.subject.keywordPlus | GAIT | - |
dc.subject.keywordPlus | ACCELEROMETERS | - |
dc.subject.keywordPlus | PARAMETERS | - |
dc.subject.keywordPlus | SUPPORT | - |
dc.subject.keywordAuthor | Ironworker | - |
dc.subject.keywordAuthor | Near-miss fall | - |
dc.subject.keywordAuthor | Fall accident | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Anomaly detection | - |
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- College of Engineering
- Department of Architecture & Architectural Engineering
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