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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

Yang, Kanghyeok; Ahn, Changbum R.; Vuran, Mehmet C.; Aria, Sepideh S.

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
URI
https://hdl.handle.net/10371/203277
DOI
https://doi.org/10.1016/j.autcon.2016.04.007
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  • College of Engineering
  • Department of Architecture & Architectural Engineering
Research Area Computing in Construction, Management in Construction

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