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Fusing imperfect experimental data for risk assessment of musculoskeletal disorders in construction using canonical polyadic decomposition

机译：使用规范多adic分解融合肌肉骨骼障碍风险评估的不完美实验数据

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摘要

Field or laboratory data collected for work-related musculoskeletal disorder (WMSD) risk assessment in construction often becomes unreliable as a large amount of data go missing due to technology-induced errors, instrument failures or sometimes at random. Missing data can adversely affect the assessment conclusions. This study proposes a method that applies Canonical Polyadic Decomposition (CPD) tensor decomposition to fuse multiple sparse risk-related datasets and fill in missing data by leveraging the correlation among multiple risk indicators within those datasets. Two knee WMSD risk-related datasets-3D knee rotation (kinematics) and electromyography (EMG) of five knee postural muscles-collected from previous studies were used for the validation and demonstration of the proposed method. The analysis results revealed that for a large portion of missing values (40%), the proposed method can generate a fused dataset that provides reliable risk assessment results highly consistent (70%-87%) with those obtained from the original experimental datasets. This signified the usefulness of the proposed method for use in WMSD risk assessment studies when data collection is affected by a significant amount of missing data, which will facilitate reliable assessment of WMSD risks among construction workers. In the future, findings of this study will be implemented to explore whether, and to what extent, the fused dataset outperforms the datasets with missing values by comparing consistencies of the risk assessment results obtained from these datasets for further investigation of the fusion performance.
机译：由于技术引起的错误，仪器故障或有时随机缺少，为施工相关肌肉骨骼障碍（WMSD）风险评估的领域或实验室数据常常变得不可靠。缺少数据可能对评估结论产生不利影响。本研究提出了一种应用规范的多adic分解（CPD）张量分解来熔断多个稀疏风险相关的数据集并通过利用这些数据集中的多个风险指标之间的相关性来填充缺失的数据。两个膝关节WMSD风险相关的数据集 - 3D膝关节旋转（运动学）和肌电图（EMG）的五膝姿势肌肉从以往的研究中收集的验证和示范用于验证和示范。分析结果表明，对于大部分缺失值（40％），所提出的方法可以产生融合数据集，提供可靠的风险评估结果高度一致（70％-87％），其中从原始实验数据集获得。这意味着当数据收集受到大量缺失数据的影响时，这一提出了在WMSD风险评估研究中使用的方法的有用性，这将促进建筑工人之间对WMSD风险的可靠评估。将来，将实施本研究的结果，以探索融合数据集是否始终以缺失值的比较，通过比较从这些数据集获得的风险评估结果的常规来实现具有缺失值的数据集以进一步调查融合性能。

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