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Deciphering workers safety attitudes by sensing gait patterns

Cited 0 time in Web of Science Cited 4 time in Scopus
Authors

Sun, Cenfei; Ahn, Changbum R.; Yang, Kanghyeok; Stentz, Terry; Kim, Hyunsoo

Issue Date
2017
Publisher
Springer Verlag
Citation
Lecture Notes in Computer Science, Vol.10287 LNCS, pp.397-405
Abstract
Workers unsafe behaviors are a top cause of safety accidents in construction. In practice, the industry relies on training and education at the group level to correct or prevent unsafe behaviors of workers. However, evidence shows that some individuals were identified to be showing risky behavior repeatedly and have a high rate to be involved in accidents and current safety training approach at the group level may not be effective for those workers. A workers evaluation of a hazard (risk perception) and tendency to take/avoid risks (risk propensity) determines how they respond to a hazard and identifying those workers with biased risk perceptions and high risk propensity can thus provide an opportunity to prevent behavior-based injuries and fatalities in the workplace. However, as risk perception and propensity are influenced not only by inherited personal traits (e.g. locus of control) but also by specific situational factors (e.g. mood and stress level), existing approaches relying on surveys are not sufficient when measuring workers risk perception and propensity continuously in day-to-day operations. In this context, this study examines the potential of ambulatory and continuous gait monitoring in the workplace as a means of identifying workers risk perception and propensity. Two experiments simulating construction work environments were conducted and subjects gait patterns in hazard zones were assessed with inertial measurement unit (IMU) data. The experimental results demonstrate changes in gait patterns at pre-hazard zones for most of the subjects. However, the results fail to identify the relationship between gait pattern changes at pre-hazard zones and risk propensities assessed using the Accident Locus of Control Scale.
ISSN
0302-9743
URI
https://hdl.handle.net/10371/203273
DOI
https://doi.org/10.1007/978-3-319-58466-9_35
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  • College of Engineering
  • Department of Architecture & Architectural Engineering
Research Area Computing in Construction, Management in Construction

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