Construction Activity Recognition and Ergonomic Risk Assessment Using a Wearable Insole Pressure System
Construction Activity Recognition and Ergonomic Risk Assessment Using a Wearable Insole Pressure System
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DOI：
10.1061/(asce)co.1943-7862.0001849
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发表时间：
2020-07-01
影响因子：
5.1
通讯作者：
Xing, Xuejiao
中科院分区：
文献类型：
--
作者：
Antwi-Afari, Maxwell Fordjour;Li, Heng;Xing, Xuejiao
与过度劳累相关的建筑活动被认为是建筑工人中与工作相关的肌肉骨骼疾病（WMSDs）的主要原因。然而，很少有研究集中在自动识别过度劳累相关的建筑工人的活动，以及评估人体工程学的风险水平，这可能有助于尽量减少WMSD。因此，本研究探讨了使用可穿戴鞋垫压力系统捕获的加速度和足底压力分布数据的可行性，用于自动识别与过度劳累相关的建筑工人活动和评估人体工程学风险水平。所提出的方法进行了测试，在实验室环境中模拟过度劳累相关的施工活动。五种类型的监督机器学习分类器的分类准确性进行了评估，不同的窗口大小，调查分类性能，并进一步估计身体强度，活动持续时间和频率信息。交叉验证的结果表明，随机森林分类器与2.56-s的窗口大小达到最好的分类准确率为98.3%和灵敏度超过95.8%的每一类活动使用的最佳功能的组合数据集。此外，相应人体工程学风险水平的估计也在同一风险水平内。这些发现可能有助于开发一种非侵入性可穿戴鞋垫压力系统，用于连续监测和自动活动识别，这可以帮助研究人员和安全管理人员识别和评估与过度劳累相关的建筑活动，以最大限度地减少建筑工人中WMSD风险的发展。
Overexertion-related construction activities are identified as a leading cause of work-related musculoskeletal disorders (WMSDs) among construction workers. However, few studies have focused on the automated recognition of overexertion-related construction workers' activities as well as assessing ergonomic risk levels, which may help to minimize WMSDs. Therefore, this study examined the feasibility of using acceleration and foot plantar pressure distribution data captured by a wearable insole pressure system for automated recognition of overexertion-related construction workers' activities and for assessing ergonomic risk levels. The proposed approach was tested by simulating overexertion-related construction activities in a laboratory setting. The classification accuracy of five types of supervised machine learning classifiers was evaluated with different window sizes to investigate classification performance and further estimate physical intensity, activity duration, and frequency information. Cross-validation results showed that the Random Forest classifier with a 2.56-s window size achieved the best classification accuracy of 98.3% and a sensitivity of more than 95.8% for each category of activities using the best features of combined data set. Furthermore, the estimation of corresponding ergonomic risk levels was within the same level of risk. The findings may help to develop a noninvasive wearable insole pressure system for the continuous monitoring and automated activity recognition, which could assist researchers and safety managers in identifying and assessing overexertion-related construction activities for minimizing the development of WMSDs' risks among construction workers.