Sensing workers gait abnormality for safety hazard identification

Kanghyeok Yang, Changbum R. Ahn, Mehmet C. Vuran, Hyunsoo Kim

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

Ironwork is considered one of the most dangerous construction trades due to its fall-prone working environment. Since safety-hazard identification is fundamental to preventing ironworkers' fall accidents, engineering measures have been applied to eliminate fall hazards or to reduce their associated risks. However, a significant quantity of hazards usually remains unidentified or not well assessed because most current efforts rely on human judgment to identify hazards. To enhance hazard identification efforts, this paper develops a technique for detecting the jobsite safety hazards of ironworkers by analyzing their gait anomalies. Using wearable inertial measurement units (WIMUs) to record kinematic data about ironworkers' gait, this study collected kinematic data while the workers interacted with two types of jobsite hazards. The anomaly level of each gait was modeled using diverse gait-related metrics. Moreover, relationships between safety hazards and worker gait abnormalities were examined through extensive experiment evaluations. The results reveal opportunities for enhancing hazard identification performance by monitoring workers' bodily response.

Original languageEnglish (US)
Pages957-965
Number of pages9
StatePublished - Jan 1 2016
Event33rd International Symposium on Automation and Robotics in Construction, ISARC 2016 - Auburn, United States
Duration: Jul 18 2016Jul 21 2016

Other

Other33rd International Symposium on Automation and Robotics in Construction, ISARC 2016
CountryUnited States
CityAuburn
Period7/18/167/21/16

Fingerprint

abnormality
Hazards
hazard
safety
Kinematics
kinematics
anomaly
Units of measurement
accident
Accidents
engineering
Monitoring
monitoring

Keywords

  • Gait Analysis
  • Hazard Identification
  • Inertial Measurement Units
  • Safety management

ASJC Scopus subject areas

  • Artificial Intelligence
  • Civil and Structural Engineering
  • Human-Computer Interaction
  • Geotechnical Engineering and Engineering Geology

Cite this

Yang, K., Ahn, C. R., Vuran, M. C., & Kim, H. (2016). Sensing workers gait abnormality for safety hazard identification. 957-965. Paper presented at 33rd International Symposium on Automation and Robotics in Construction, ISARC 2016, Auburn, United States.

Sensing workers gait abnormality for safety hazard identification. / Yang, Kanghyeok; Ahn, Changbum R.; Vuran, Mehmet C.; Kim, Hyunsoo.

2016. 957-965 Paper presented at 33rd International Symposium on Automation and Robotics in Construction, ISARC 2016, Auburn, United States.

Research output: Contribution to conferencePaper

Yang, K, Ahn, CR, Vuran, MC & Kim, H 2016, 'Sensing workers gait abnormality for safety hazard identification', Paper presented at 33rd International Symposium on Automation and Robotics in Construction, ISARC 2016, Auburn, United States, 7/18/16 - 7/21/16 pp. 957-965.
Yang K, Ahn CR, Vuran MC, Kim H. Sensing workers gait abnormality for safety hazard identification. 2016. Paper presented at 33rd International Symposium on Automation and Robotics in Construction, ISARC 2016, Auburn, United States.
Yang, Kanghyeok ; Ahn, Changbum R. ; Vuran, Mehmet C. ; Kim, Hyunsoo. / Sensing workers gait abnormality for safety hazard identification. Paper presented at 33rd International Symposium on Automation and Robotics in Construction, ISARC 2016, Auburn, United States.9 p.
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