Deciphering workers’ safety attitudes by sensing gait patterns

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

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 worker’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’ 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.

Original languageEnglish (US)
Title of host publicationDigital Human Modeling
Subtitle of host publicationApplications in Health, Safety, Ergonomics, and Risk Management: Health and Safety - 8th International Conference, DHM 2017 Held as Part of HCI International 2017, Proceedings
EditorsVincent G. Duffy
PublisherSpringer Verlag
Pages397-405
Number of pages9
ISBN (Print)9783319584652
DOIs
StatePublished - 2017
Event8th 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 - Vancouver, Canada
Duration: Jul 9 2017Jul 14 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10287 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th 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
CountryCanada
CityVancouver
Period7/9/177/14/17

Keywords

  • Behavioral adaptation
  • Gait Abnormality
  • Human risk propensity
  • Safety management

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Deciphering workers’ safety attitudes by sensing gait patterns'. Together they form a unique fingerprint.

  • Cite this

    Sun, C., Ahn, C. R., Yang, K., Stentz, T., & Kim, H. (2017). Deciphering workers’ safety attitudes by sensing gait patterns. In V. G. Duffy (Ed.), Digital Human Modeling: Applications in Health, Safety, Ergonomics, and Risk Management: Health and Safety - 8th International Conference, DHM 2017 Held as Part of HCI International 2017, Proceedings (pp. 397-405). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10287 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-58466-9_35