Journal
AUTOMATION IN CONSTRUCTION
Volume 124, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.autcon.2020.103538
Keywords
Behavior-based safety (BBS); Computer vision; Construction worker; Deep learning; Motion sensor; Occupational safety and health (OSH); Pose estimation
Funding
- Research Grants Council of Hong Kong [PolyU 15210720, PolyU 152047/19E]
- Innovation and Technology Commission of Hong Kong [ITP/020/18LP]
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Posture-related data of construction workers is crucial for their safety, health, and productivity, and methods such as motion sensors and RGB cameras are widely used for data collection and analysis. However, there is a lack of review on previous data collection methods in the construction industry, highlighting a research gap that needs to be addressed in the future.
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.
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