当前位置: X-MOL 学术Adv. Eng. Inform. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Deep learning-based classification of work-related physical load levels in construction
Advanced Engineering Informatics ( IF 8.8 ) Pub Date : 2020-05-12 , DOI: 10.1016/j.aei.2020.101104
Kanghyeok Yang , Changbum R. Ahn , Hyunsoo Kim

Work-related musculoskeletal disorders (WMSDs) are the leading cause of the nonfatal injuries for construction workers, and a worker’s overexertion is a major source of such WMSDs. Pushing, pulling, and carrying movements—which are all activities largely associated with physical loads—account for 35% of WMSDs. However, most previous studies have focused on the identification of non-ergonomic postures, and there has been limited effort expended on measuring a worker’s exposures to the physical loads caused by materials or tools during construction tasks. With the advantage of using a wearable inertial measurement sensor to monitor a worker’s bodily movements, this study investigates the feasibility of identifying various physical loading conditions by analyzing a worker’s lower body movements. In the experiment with laboratory settings, workers performed a load carrying task by moving concrete bricks. A bidirectional long short-term memory algorithm is employed to classify physical load levels; this approach achieved 74.6 to 98.6% accuracy and 0.59 to 0.99 F-score in classification. The results demonstrate the feasibility of the proposed approach in identifying the states of physical loads. The findings of this study contribute to the literature on classifying ergonomically at-risk workers and on preventing WMSDs in high physical demand occupations, thereby helping enhance the health and safety of the construction workplace.



中文翻译:

基于深度学习的建筑中与工作相关的物理负荷水平的分类

与工作有关的肌肉骨骼疾病(WMSD)是建筑工人非致命伤害的主要原因,而工人的过度劳累是此类WMSD的主要来源。推,拉和搬运运动(所有这些活动都与物理负荷有关)占WMSD的35%。但是,以前的大多数研究都集中在识别非人机工程学的姿势上,并且在测量工人在施工任务期间承受的由材料或工具引起的物理载荷的暴露方面投入的精力有限。利用可穿戴式惯性测量传感器来监控工人的身体运动的优势,本研究调查了通过分析工人的下半身运动来识别各种身体负荷状况的可行性。在实验室设置的实验中,工人通过移动混凝土砖块来执行承重任务。采用双向长时短期记忆算法对物理负载水平进行分类。这种方法在分类中达到了74.6%至98.6%的准确度以及0.59至0.99 F分。结果证明了该方法在识别物理载荷状态中的可行性。这项研究的发现为有关在人体工程学上处于危险中的工人进行分类以及在高体力需求职业中预防WMSD的文献做出了贡献,从而有助于提高建筑工作场所的健康和安全性。结果证明了该方法在识别物理载荷状态中的可行性。这项研究的发现为有关在人体工程学上处于危险中的工人进行分类以及在高体力需求职业中预防WMSD的文献做出了贡献,从而有助于提高建筑工作场所的健康和安全性。结果证明了该方法在识别物理载荷状态中的可行性。这项研究的发现为有关在人体工程学上处于危险中的工人进行分类以及在高体力需求职业中预防WMSD的文献做出了贡献,从而有助于提高建筑工作场所的健康和安全性。

更新日期:2020-05-12
down
wechat
bug