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An Evaluation of Posture Recognition Based on Intelligent Rapid Entire Body Assessment System for Determining Musculoskeletal Disorders

机译：基于智能快速全身评估系统的肌肉骨骼障碍评价肌肉骨骼障碍的姿态识别

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Determining the potential risks of musculoskeletal disorders through working postures in a workplace is expensive and time-consuming. A novel intelligent rapid entire body assessment (REBA) system based on convolutional pose machines (CPM), entitled the Quick Capture system, was applied to determine the risk levels. The aim of the study was to validate the feasibility and reliability of the CPM-based REBA system through a simulation experiment. The reliability was calculated from the differences of motion angles between the CPM-based REBA and a motion capture system. Results show the data collected by the Quick Capture system were consistent with those of the motion capture system; the average of root mean squared error (RMSE) was 4.77 and the average of Spearman’s rho (ρ) correlation coefficient in the different 12 postures was 0.915. For feasibility evaluation, the linear weighted Cohen’s kappa between the REBA score obtained by the Quick Capture system and those from the three experts were used. The result shows good agreement, with an average proportion agreement index (P0) of 0.952 and kappa of 0.738. The Quick Capture system does not only accurately analyze working posture, but also accurately determines risk level of musculoskeletal disorders. This study suggested that the Quick Capture system could be applied for a rapid and real-time on-site assessment.
机译：通过工作场所中的工作姿势确定肌肉骨骼疾病的潜在风险是昂贵且耗时的。基于卷积姿势机（CPM）的新型智能快速全身评估（Reba）系统，标题为快速捕获系统，以确定风险水平。该研究的目的是通过仿真实验验证基于CPM的Reba系统的可行性和可靠性。根据基于CPM的REBA和运动捕获系统之间的运动角度的差异计算可靠性。结果显示快速捕获系统收集的数据与运动捕获系统的数据一致;根均方误差（RMSE）的平均值为4.77，并且矛盾的rho（ρ）相关系数的平均不同12姿势为0.915。对于可行性评估，使用快速捕获系统获得的Reba评分与三个专家的雷伯格之间的线性加权科恩的κ。结果表明，良好的一致性，平均比例协议指数（P0）为0.952和κ0.738。快速捕获系统不仅可以准确地分析工作姿势，还可以准确地确定肌肉骨骼障碍的风险水平。本研究表明，快速捕获系统可以应用于快速和实时的现场评估。

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