A novel vision-based real-time method for evaluating postural risk factors associated with musculoskeletal disorders
A novel vision-based real-time method for evaluating postural risk factors associated with musculoskeletal disorders
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DOI：
10.1016/j.apergo.2020.103138
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发表时间：
2020-09-01
影响因子：
3.2
通讯作者：
Xu, Xu
中科院分区：
文献类型：
--
作者：
Li, Li;Martin, Tara;Xu, Xu
工作相关性肌肉骨骼疾病（MSD）的实时风险评估一直是一个具有挑战性的研究问题。以前的方法，如使用深度摄像机，其视觉范围有限，可穿戴传感器可能会对工人造成侵扰，这两种方法对于长期运行的现场应用都不太可行。本文研究了一种基于深度学习的快速上肢评估算法（RSESS）的新型端到端实现。该算法以正常RGB图像为输入，输出Rounds动作等级，它是Rounds总得分的进一步划分。在实验室中收集的提升姿势和来自Human 3.6（公共人体姿势数据集）的姿势数据用于训练和评估算法。总的来说，该算法实现了93%的准确度和每秒29帧的效率，用于检测Rectangle动作水平。结果还表明，使用数据增强（一种使训练数据多样化的策略）可以显着提高模型的鲁棒性。所提出的方法表明，其高潜力的实时现场风险评估，以预防与工作有关的MSD。演示视频可以在https://github.com/LLDavid/RULA_2DImage上找到。
Real-time risk assessment for work-related musculoskeletal disorders (MSD) has been a challenging research problem. Previous methods such as using depth cameras suffered from limited visual range and wearable sensors could cause intrusiveness to the workers, both of which are less feasible for long-run on-site applications. This document examines a novel end-to-end implementation of a deep learning-based algorithm for rapid upper limb assessment (RULA). The algorithm takes normal RGB images as input and outputs the RULA action level, which is a further division of RULA grand score. Lifting postures collected in laboratory and posture data from Human 3.6 (a public human pose dataset) were used for training and evaluating the algorithm. Overall, the algorithm achieved 93% accuracy and 29 frames per second efficiency for detecting the RULA action level. The results also indicate that using data augmentation (a strategy to diversify the training data) can significantly improve the robustness of the model. The proposed method demonstrates its high potential for real-time on-site risk assessment for the prevention of work-related MSD. A demo video can be found at https://github.com/LLDavid/RULA_2DImage.