3D Visualization-Based Ergonomic Risk Assessment and Work Modification Framework and Its Validation for a Lifting Task
Publication: Journal of Construction Engineering and Management
Volume 144, Issue 1
Abstract
The construction manufacturing industry in North America has a disproportionately high number of lost-time injuries because of the high physical demand of the labor-intensive tasks it involves. It is thus essential to investigate the physical demands of body movement in the construction manufacturing workplace to proactively identify worker exposure to ergonomic risk. This paper proposes a methodology to use three-dimensional (3D) skeletal modeling to imitate human body movement in an actual construction manufacturing plant for ergonomic risk assessment of a workstation. The inputs for the creation of an accurate and reliable 3D model are also identified. Through 3D modeling, continuous human body motion data can be obtained (such as joint coordinates and joint angles) for risk assessment analysis using existing risk assessment algorithms. The presented framework enables risk evaluation by detecting awkward body postures and evaluating the handled force/load and frequency that cause ergonomic risks during manufacturing operations. The results of the analysis are expected to facilitate the development of modified work to the workstation, which will potentially reduce injuries and workers’ compensation insurance costs in the long term for construction manufacturers. The proposed framework can also be expanded to evaluate workstations in the design phase without the need for physical imitation by human subjects. In this paper, the proposed 3D visualization-based ergonomic risk assessment methodology is validated through an optical marker-based motion capture experiment for a lifting task in order to prove the feasibility and reliability of the framework. It is also compared to the traditional manual observation method. Three subjects are selected to conduct the experiment and three levels of comparison are completed: joint angles comparison, risk rating comparison for body segments, and Rapid Entire Body Assessment/Rapid Upper Limb Assessment (REBA/RULA) total risk rating and risk level comparison.
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Data Availability Statement
Data generated or analyzed during the study are available from the corresponding author by request. Information about the Journal’s data sharing policy can be found here: http://ascelibrary.org/doi/10.1061/%28ASCE%29CO.1943-7862.0001263.
Acknowledgments
This study was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) through the Industrial Research Chair (IRC) in the Industrialization of Building Construction (File No. IRCPJ 419145-15), Engage Grant EGP470391, and Discovery Grants RGPIN 402046, RGPIN 436051, and RGPIN-2017-06721. The authors would also like to acknowledge the support from their industry partner, All Weather Windows, as well as the contributions of Amin Komeili and Justin Lewicke, who assisted with and participated in the experiment. And special thanks are extended to the Glenrose Rehabilitation Hospital for allowing the authors to complete the experiment in their motion capture lab.
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Published In
Journal of Construction Engineering and Management
Volume 144 • Issue 1 • January 2018
Copyright
©2017 American Society of Civil Engineers.
History
Received: Feb 16, 2017
Accepted: Jun 29, 2017
Published online: Nov 10, 2017
Published in print: Jan 1, 2018
Discussion open until: Apr 10, 2018
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