Automation of Workplace Lifting Hazard Assessment for Musculoskeletal Injury Prevention
Automation of Workplace Lifting Hazard Assessment for Musculoskeletal Injury Prevention
复制标题
自动化工作场所举升危险评估以预防肌肉骨骼损伤
DOI：
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发表时间：
2014
影响因子：
1.3
通讯作者：
Margaret Hughes
中科院分区：
文献类型：
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作者：
J. Spector;Max Lieblich;S. Bao;K. McQuade;Margaret Hughes
现有的方法实际评估肌肉骨骼暴露，如姿势和重复在工作场所设置有局限性。我们的目标是自动估计修订后的美国国家职业安全与健康研究所（NIOSH）提升方程中的参数，这是一种标准的手动观察工具，用于评估与工作场所设置中的提升相关的背部损伤风险，使用深度相机（Microsoft Kinect）和骨架算法技术。（大约22，000帧，来自六个受试者）在实验室环境中使用Kinect记录的同时提起和其他运动（微软公司，雷德蒙，华盛顿，美国）和标准光学运动捕捉系统（Qualysis，Qualysis Motion Capture Systems，Qualysis AB，Sweden）组装。开发了误差校正回归模型，以提高从Kinect骨架估计的NIOSH提升方程参数的准确性。使用具有Huber损失函数的梯度增强回归树对Kinect-Qualysis误差进行建模。除了一个受试者之外，所有受试者的数据都对模型进行了训练，并对排除的受试者进行了测试。最后，模型进行了测试，在三个提升试验不参与生成的模型构建dataset. ResultsError-correction的主题似乎产生的NIOSH提升方程参数的估计是更准确的比那些来自单独的Microsoft Kinect算法。我们的误差校正模型大大降低了参数误差的方差。总的来说，Kinect低估了参数，建模减少了这种偏差，特别是对于更有偏差的估计。使用的原始Kinect骨架模型往往会导致错误的高安全建议的重量限制的负载，而纠错模型给了更保守的，保护estimation.ConclusionsOur结果表明，它可能会产生合理的估计的姿势和时间元素的任务，如任务频率在一个自动化的方式，虽然这些发现应该在一个更大的研究中得到证实。需要进一步开展工作，纳入部队评估，解决工作场所的可行性挑战。我们预计这种方法最终可用于进行大规模肌肉骨骼暴露评估，不仅用于研究，还可在工作方法改进活动和员工培训期间向工人和雇主提供实时反馈。
ObjectivesExisting methods for practically evaluating musculoskeletal exposures such as posture and repetition in workplace settings have limitations. We aimed to automate the estimation of parameters in the revised United States National Institute for Occupational Safety and Health (NIOSH) lifting equation, a standard manual observational tool used to evaluate back injury risk related to lifting in workplace settings, using depth camera (Microsoft Kinect) and skeleton algorithm technology.MethodsA large dataset (approximately 22,000 frames, derived from six subjects) of simultaneous lifting and other motions recorded in a laboratory setting using the Kinect (Microsoft Corporation, Redmond, Washington, United States) and a standard optical motion capture system (Qualysis, Qualysis Motion Capture Systems, Qualysis AB, Sweden) was assembled. Error-correction regression models were developed to improve the accuracy of NIOSH lifting equation parameters estimated from the Kinect skeleton. Kinect-Qualysis errors were modelled using gradient boosted regression trees with a Huber loss function. Models were trained on data from all but one subject and tested on the excluded subject. Finally, models were tested on three lifting trials performed by subjects not involved in the generation of the model-building dataset.ResultsError-correction appears to produce estimates for NIOSH lifting equation parameters that are more accurate than those derived from the Microsoft Kinect algorithm alone. Our error-correction models substantially decreased the variance of parameter errors. In general, the Kinect underestimated parameters, and modelling reduced this bias, particularly for more biased estimates. Use of the raw Kinect skeleton model tended to result in falsely high safe recommended weight limits of loads, whereas error-corrected models gave more conservative, protective estimates.ConclusionsOur results suggest that it may be possible to produce reasonable estimates of posture and temporal elements of tasks such as task frequency in an automated fashion, although these findings should be confirmed in a larger study. Further work is needed to incorporate force assessments and address workplace feasibility challenges. We anticipate that this approach could ultimately be used to perform large-scale musculoskeletal exposure assessment not only for research but also to provide real-time feedback to workers and employers during work method improvement activities and employee training.