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Semi-supervised near-miss fall detection for ironworkers with a wearable inertial measurement unit
Cited 132 time in
Web of Science
Cited 146 time in Scopus
- Authors
- Issue Date
- 2016-08
- Publisher
- Elsevier B.V.
- Citation
- Automation in Construction, Vol.68, pp.194-202
- 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.
- ISSN
- 0926-5805
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Related Researcher
- College of Engineering
- Department of Architecture & Architectural Engineering
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