Chen Haozhi, Liu Peiran, Zhou Guoyang, Lu Ming-Lun, Yu Denny
Purdue University, West Lafayette, IN, USA.
National Institute for Occupational Safety and Health, Cincinnati, OH, USA.
Appl Ergon. 2025 Sep;127:104513. doi: 10.1016/j.apergo.2025.104513. Epub 2025 Apr 1.
Work-related injuries from overexertion, particularly lifting, are a major concern in occupational safety. Traditional assessment tools, such as the Revised NIOSH Lifting Equation (RNLE), require significant training and practice for deployment. This study presents an approach that integrates tactile gloves with computer vision (CV) to enhance the assessment of lifting-related injury risks, addressing the limitations of existing single-modality methods. Thirty-one participants performed 2747 lifting tasks across three lifting risk categories (LI < 1, 1 ≤ LI ≤ 2, LI > 2). Features including hand pressure measured by tactile gloves during each lift and 3D body poses estimated using CV algorithms from video recordings were combined and used to develop prediction models. The Convolutional Neural Network (CNN) model achieved an overall accuracy of 89 % in predicting the three lifting risk categories. The results highlight the potential for a real-time, non-intrusive risk assessment tool to assist ergonomic practitioners in mitigating musculoskeletal injury risks in workplace environments.
过度用力导致的工伤，尤其是搬运重物造成的工伤，是职业安全领域的一个主要问题。传统的评估工具，如修订后的美国国家职业安全与健康研究所搬运方程（RNLE），在部署时需要大量的培训和实践。本研究提出了一种将触觉手套与计算机视觉（CV）相结合的方法，以加强对与搬运相关的受伤风险的评估，解决现有单一模式方法的局限性。31名参与者完成了2747项搬运任务，涵盖三种搬运风险类别（LI < 1、1 ≤ LI ≤ 2、LI > 2）。将每次搬运过程中通过触觉手套测量的手部压力以及使用CV算法从视频记录中估计的三维身体姿势等特征进行组合，并用于开发预测模型。卷积神经网络（CNN）模型在预测这三种搬运风险类别时的总体准确率达到了89%。研究结果凸显了一种实时、非侵入性风险评估工具在协助人体工程学从业者降低工作场所环境中肌肉骨骼损伤风险方面的潜力。
