Assessing Workers' Operational Postures via Egocentric Camera Mapping

Construction tasks involve extensive and repetitive physically demanding activities, which results in a higher risk of work-related musculoskeletal disorders (WMSDs) compared to other industries. Thus, assessing construction workers' postural ergonomics during construction occupational tasks is critical to evaluating workers' postural stresses and reducing the potential risk of WMSDs. Recent developments in machine learning-based computer vision methods have attracted an increasing attention of construction researchers as it is an effective tool for assessing postural ergonomics. However, existing computer vision-based ergonomic assessments in the construction research field are still mainly based on multiple-view motion tracking systems or fixed-position stand-alone cameras. These types of motion-tracking methods are limited by resource-expensive and serious occlusion issues. As an alternative approach, this paper proposes an economical and scalable machine learning enabled egocentric postural ergonomic assessment (EPEA) system that integrates the fisheye camera mounted to the hard hat to identify and assess workers' postural ergonomics via a convolutional 3D pose estimation neural network. We tested the EPEA with multiple construction operational tasks. Results show that the EPEA can correctly identify users' key joint parts and classify different postures. The results confirmed the usability and feasibility of the proposed system, and it has the potential to help construction workers to identify the potential WMSDs risks during repetitive and forceful construction works.