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Comprehensive Fall-Risk Assessment of Construction Workers Using Inertial Measurement Units: Validation of the Gait-Stability Metric to Assess the Fall Risk of Iron Workers

Cited 79 time in Web of Science Cited 92 time in Scopus
Authors

Jebelli, Houtan; Ahn, Changbum R.; Stentz, Terry L.

Issue Date
2016-05
Publisher
American Society of Civil Engineers
Citation
Journal of Computing in Civil Engineering, Vol.30 No.3, p. 04015034
Abstract
In construction worksites, slips, trips, and falls are major causes of fatal injuries. This fact demonstrates the need for a safety assessment method that provides a comprehensive fall-risk analysis inclusive of the effects of physiological characteristics of construction workers. In this context, this research tests the usefulness of the maximum Lyapunov exponents (Max LE) as a metric to assess construction workers' comprehensive fall risk. Max LE, one of the gait-stability metrics established in clinical settings, estimates how the stability of a construction worker reacts to very small disruptions. In order to validate the use of Max LE, a laboratory experiment that asked a group of subjects to simulate iron workers' walking tasks on an I-beam was designed and conducted. These tasks were designed to showcase various fall-risk profiles: walking with a comfortable walking speed presented a low fall-risk profile; carrying a one-sided load and walking at a faster speed on the I-beam both presented high fall-risk profiles. Inertial measurement unit (IMU) sensors were attached to the right ankle of participants' bodies to collect kinematic data for the calculation of Max LE. The results showed that Max LE offers adequate distinguishing power for characterizing the fall risk of various construction workers' tasks, and the introduced approach to compute the gait stability from IMU sensor data captured from human bodies could provide a valuable analysis of the safety-related risks present in construction workers' motions. (C) 2015 American Society of Civil Engineers.
ISSN
0887-3801
URI
https://hdl.handle.net/10371/203278
DOI
https://doi.org/10.1061/(ASCE)CP.1943-5487.0000511
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  • College of Engineering
  • Department of Architecture & Architectural Engineering
Research Area Computing in Construction, Management in Construction

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