Central Research Institute of Building and Construction Co., Ltd., MCC Group, Shenzhen 518088, China.
School of Civil Engineering, Harbin Institute of Technology, Harbin 150090, China.
Sensors (Basel). 2024 Sep 24;24(19):6187. doi: 10.3390/s24196187.
Work-related musculoskeletal disorders (WMSDs) represent a significant health challenge for workers in construction environments, often arising from prolonged exposure to ergonomic risks associated with manual labor, awkward postures, and repetitive motions. These conditions not only lead to diminished worker productivity but also incur substantial economic costs for employers and healthcare systems alike. Thus, there is an urgent need for effective tools to assess and mitigate these ergonomic risks. This study proposes a novel monocular 3D multi-person pose estimation method designed to enhance ergonomic risk assessments in construction environments. Leveraging advanced computer vision and deep learning techniques, this approach accurately captures and analyzes the spatial dynamics of workers' postures, with a focus on detecting extreme knee flexion, a critical indicator of work-related musculoskeletal disorders (WMSDs). A pilot study conducted on an actual construction site demonstrated the method's feasibility and effectiveness, achieving an accurate detection rate for extreme flexion incidents that closely aligned with supervisory observations and worker self-reports. The proposed monocular approach enables universal applicability and enhances ergonomic analysis through 3D pose estimation and group pose recognition for timely interventions. Future efforts will focus on improving robustness and integration with health monitoring to reduce WMSDs and promote worker health.
工作相关肌肉骨骼疾病（WMSD）是建筑环境中工人面临的一项重大健康挑战，通常源于长时间暴露在与体力劳动、姿势别扭和重复动作相关的人体工程学风险中。这些情况不仅导致工人生产力下降，还给雇主和医疗保健系统带来巨大的经济成本。因此，迫切需要有效的工具来评估和减轻这些人体工程学风险。本研究提出了一种新颖的单目 3D 多人姿态估计方法，旨在增强建筑环境中的人体工程学风险评估。该方法利用先进的计算机视觉和深度学习技术，准确捕捉和分析工人姿态的空间动态，重点是检测关键的工作相关肌肉骨骼疾病（WMSD）指标——极度膝关节弯曲。在实际建筑工地进行的试点研究证明了该方法的可行性和有效性，其对极度弯曲事件的准确检测率与监督观察和工人自我报告高度一致。所提出的单目方法通过 3D 姿态估计和群体姿态识别实现了普遍适用性，并增强了人体工程学分析，以便及时进行干预。未来的工作将集中于提高鲁棒性和与健康监测的集成度，以减少 WMSD 并促进工人健康。
