@inproceedings{d1b8e7a08265400b8cf8d7d48877d566,
title = "Deciphering workers{\textquoteright} safety attitudes by sensing gait patterns",
abstract = "Workers{\textquoteright} 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 worker{\textquoteright}s 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{\textquoteright} 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{\textquoteright} risk perception and propensity. Two experiments simulating construction work environments were conducted and subjects{\textquoteright} 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.",
keywords = "Behavioral adaptation, Gait Abnormality, Human risk propensity, Safety management",
author = "Cenfei Sun and Ahn, {Changbum R.} and Kanghyeok Yang and Terry Stentz and Hyunsoo Kim",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 8th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics, and Risk Management, DHM 2017, held as part of 19th International Conference on Human-Computer Interaction, HCI 2017 ; Conference date: 09-07-2017 Through 14-07-2017",
year = "2017",
doi = "10.1007/978-3-319-58466-9_35",
language = "English (US)",
isbn = "9783319584652",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "397--405",
editor = "Duffy, {Vincent G.}",
booktitle = "Digital Human Modeling",
}