Work-related musculoskeletal disorders pose significant health risks to construction workers, making it essential to monitor their postures and identify physical exposure to mitigate these risks. This study presents a novel framework for real-time ergonomic risk assessment of workers in construction environments. Specifically, this study develops a lightweight human pose estimation (HPE) model with a residual log-likelihood estimation head and adopts pose-tracking technology to enable real-time recognition of workers’ three-dimensional (3D) postures. In particular, this study proposes a novel co-learning method that enables the HPE model to learn two-dimensional (2D) and 3D features from multi-dimension datasets simultaneously, substantially enhancing the model's ability to capture 3D postures from 2D images. The proposed framework facilitates real-time ergonomic risk assessment, reducing potential risks to construction workers and offering promising practical applications.

中文翻译：

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使用共同学习驱动的 3D 人体姿势估计模型对施工中的实时人体工程学风险进行评估

与工作相关的肌肉骨骼疾病对建筑工人构成重大健康风险，因此必须监测他们的姿势并确定身体暴露情况以减轻这些风险。这项研究提出了一种新颖的框架，用于对建筑环境中的工人进行实时人体工程学风险评估。具体来说，本研究开发了一种具有残差对数似然估计头的轻量级人体姿势估计（HPE）模型，并采用姿势跟踪技术来实时识别工人的三维（3D）姿势。特别是，本研究提出了一种新颖的协同学习方法，使 HPE 模型能够同时从多维数据集中学习二维 (2D) 和 3D 特征，从而大大增强模型从 2D 图像捕获 3D 姿势的能力。所提出的框架有利于实时人体工程学风险评估，减少建筑工人的潜在风险并提供有前景的实际应用。