Posture-related data collection methods for construction workers: A review


Construction workers' posture-related data is closely connected with their safety, health, and productivity performance. The importance of posture-related data has drawn the attention of researchers in construction management and other fields. Accordingly, many data collection methods have been developed and applied to collect posture-related data. Despite the importance of workers' posture-related data, there lacks a review of previous data collection methods in the construction industry. This paper fills the research gap by reviewing previous methods to collect posture-related data for construction workers via 1) summarizing working principles and applications of posture-related data collection in construction management, which demonstrates the extensive use of motion sensors and Red-Green-Blue (RGB) cameras in posture-related data collection, 2) comparing the above methods based on data quality and feasibility on construction sites, which reveals the reason why motion sensors and RGB cameras have been prevalent in previous studies, 3) revealing research gaps of posture-related data collection tools and applications, and providing possible future research directions.

Publication DOI:
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Engineering Systems and Supply Chain Management
Additional Information: © 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Uncontrolled Keywords: Behavior-based safety (BBS),Computer vision,Construction worker,Deep learning,Motion sensor,Occupational safety and health (OSH),Pose estimation,Control and Systems Engineering,Civil and Structural Engineering,Building and Construction
Publication ISSN: 0926-5805
Last Modified: 11 Jun 2024 07:25
Date Deposited: 17 Mar 2021 09:24
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Related URLs: https://www.sci ... 1183?via%3Dihub (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Review article
Published Date: 2021-04-01
Published Online Date: 2021-01-22
Accepted Date: 2020-12-15
Authors: Yu, Yantao
Umer, Waleed
Yang, Xincong
Antwi-Afari, Maxwell Fordjour (ORCID Profile 0000-0002-6812-7839)

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