Construction workers are at high risk of work-related musculoskeletal disorders (WMSDs). Although the existing observational assessment methods are easy to use, when it comes to a more in-depth statistical analysis of the dynamic characteristics of the worker's operation, the sample data to be processed turn out to be large, the labor cost high, and the analysis easily affected by the prejudice of the evaluator. This study examines a novel WMSD prediction method based on the dynamic characteristics of the working posture, which comprises three artificial intelligence algorithms in series. In this method, the posture detector identifies the limb angles and state in the working video, the posture risk evaluator evaluates the risk level of the working posture frame by frame, and the task risk predictor predicts the risk level of the current work process. The collected video data of common tasks of construction workers and the MPII Human Pose dataset were used for training and evaluation of the algorithms. The method achieved 87.0% accuracy of the joint point recognition. The micro-averaged accuracy, recall, and F1-score (harmonic average of accuracy and recall) reached 96.7%, 96.0%, and 96.6%, respectively. The results showed that the proposed method has great potential for real-time risk assessment. It can output all of the changes of the limb angles of workers in the work process frame by frame and predict the risk level of the whole work process.

建筑工人患与工作相关的肌肉骨骼疾病 (WMSD) 的风险很高。现有的观察性评估方法虽然简单易用，但在对工人作业动态特征进行更深入的统计分析时，处理的样本数据量大，人工成本高，分析容易受到评价者偏见的影响。本研究研究了一种基于工作姿势动态特性的新型 WMSD 预测方法，该方法包括三个串联的人工智能算法。在该方法中，姿势检测器识别工作视频中的肢体角度和状态，姿势风险评估器逐帧评估工作姿势的风险等级，任务风险预测器预测当前工作流程的风险级别。收集到的建筑工人常见任务的视频数据和 MPII 人体姿势数据集用于算法的训练和评估。该方法实现了87.0%的关节点识别准确率。微平均准确率、召回率和 F1-score（准确率和召回率的谐波平均值）分别达到 96.7%、96.0% 和 96.6%。结果表明，所提出的方法在实时风险评估方面具有很大的潜力。它可以逐帧输出工作过程中工人肢体角度的所有变化，预测整个工作过程的风险等级。该方法实现了87.0%的关节点识别准确率。微平均准确率、召回率和 F1-score（准确率和召回率的谐波平均值）分别达到 96.7%、96.0% 和 96.6%。结果表明，所提出的方法在实时风险评估方面具有很大的潜力。它可以逐帧输出工作过程中工人肢体角度的所有变化，预测整个工作过程的风险等级。该方法实现了87.0%的关节点识别准确率。微平均准确率、召回率和 F1-score（准确率和召回率的谐波平均值）分别达到 96.7%、96.0% 和 96.6%。结果表明，所提出的方法在实时风险评估方面具有很大的潜力。它可以逐帧输出工作过程中工人肢体角度的所有变化，预测整个工作过程的风险等级。