Abstract：
There are some inherent problems with the use of observation methods in the ergonomic assessment of working posture, namely the stability and precision of the measurements. This study aims to use a machine learning (ML) approach to avoid the subjectivity bias of observational methods in ergonomic assessments and further identify risk patterns for work-related musculoskeletal disorders (WMSDs) among sewing machine operators.
We proposed a decision tree analysis scheme for ergonomic assessment in working postures (DTAS-EAWP). First, DTAS-EAWP used computer vision-based technology to detect the body movement angles from the on-site working videos to generate a dataset of risk scores through the criteria of Rapid Entire Body Assessment (REBA) for sewing machine operators. Second, data mining techniques (WEKA) using the C4.5 algorithm were used to construct a representative decision tree (RDT) with paths of various risk levels, and attribute importance analysis was performed to determine the critical body segments for WMSDs.
DTAS-EAWP was able to recognize 11,211 samples of continuous working postures in sewing machine operation and calculate the corresponding final REBA scores. A total of 13 decision rules were constructed in the RDT, with over 95% prediction accuracy and 83% path coverage, to depict the possible risk tendency in the working postures. Through RDT and attribute importance analysis, it was identified that the lower arm and the upper arms exhibited as critical segments that significantly increased the risk levels for WMSDs.
This study demonstrates that ML approach with computer vision-based estimation and DT analysis are feasible for comprehensively exploring the decision rules in ergonomic assessment of working postures for risk prediction of WMSDs in sewing machine operators.
This DTAS-EAWP can be applied in manufacturing industries to automatically analyze working postures and identify risk patterns of WMSDs, leading to the development of effectively preventive interventions.
We proposed a decision tree analysis scheme for ergonomic assessment in working postures (DTAS-EAWP). First, DTAS-EAWP used computer vision-based technology to detect the body movement angles from the on-site working videos to generate a dataset of risk scores through the criteria of Rapid Entire Body Assessment (REBA) for sewing machine operators. Second, data mining techniques (WEKA) using the C4.5 algorithm were used to construct a representative decision tree (RDT) with paths of various risk levels, and attribute importance analysis was performed to determine the critical body segments for WMSDs.
DTAS-EAWP was able to recognize 11,211 samples of continuous working postures in sewing machine operation and calculate the corresponding final REBA scores. A total of 13 decision rules were constructed in the RDT, with over 95% prediction accuracy and 83% path coverage, to depict the possible risk tendency in the working postures. Through RDT and attribute importance analysis, it was identified that the lower arm and the upper arms exhibited as critical segments that significantly increased the risk levels for WMSDs.
This study demonstrates that ML approach with computer vision-based estimation and DT analysis are feasible for comprehensively exploring the decision rules in ergonomic assessment of working postures for risk prediction of WMSDs in sewing machine operators.
This DTAS-EAWP can be applied in manufacturing industries to automatically analyze working postures and identify risk patterns of WMSDs, leading to the development of effectively preventive interventions.
摘要：
背景：在对工作姿势进行人体工程学评估时，使用观察方法存在一些固有的问题，即测量的稳定性和精度。这项研究旨在使用机器学习（ML）方法来避免人体工程学评估中观察方法的主观性偏见，并进一步确定缝纫机操作员与工作相关的肌肉骨骼疾病（WMSDs）的风险模式。
方法：我们提出了一种用于工作姿势中人体工程学评估的决策树分析方案（DTAS-EAWP）。首先,DTAS-EAWP使用基于计算机视觉的技术从现场工作视频中检测身体运动角度，通过缝纫机操作员的快速全身评估（REBA）标准生成风险评分数据集。第二,使用C4.5算法的数据挖掘技术（WEKA）用于构建具有各种风险级别路径的代表性决策树（RDT），并进行了属性重要性分析，以确定WMSD的关键身体部分。
结果：DTAS-EAWP能够识别缝纫机操作中连续工作姿势的11,211个样本，并计算出相应的最终REBA分数。在RDT中总共构建了13条决策规则，预测准确率超过95%，路径覆盖率达到83%，描述工作姿势中可能存在的风险倾向。通过RDT和属性重要性分析,经鉴定,下臂和上臂表现为显著增加WMSDs风险水平的关键节段.
结论：这项研究表明，基于计算机视觉的估计和数字孪生分析的ML方法对于全面探索工效学评估中的决策规则是可行的，以预测缝纫机操作员的WMSDs的风险。
结论：此DTAS-EAWP可应用于制造业，以自动分析工作姿势并识别WMSD的风险模式，导致有效预防干预措施的发展。
方法：我们提出了一种用于工作姿势中人体工程学评估的决策树分析方案（DTAS-EAWP）。首先,DTAS-EAWP使用基于计算机视觉的技术从现场工作视频中检测身体运动角度，通过缝纫机操作员的快速全身评估（REBA）标准生成风险评分数据集。第二,使用C4.5算法的数据挖掘技术（WEKA）用于构建具有各种风险级别路径的代表性决策树（RDT），并进行了属性重要性分析，以确定WMSD的关键身体部分。
结果：DTAS-EAWP能够识别缝纫机操作中连续工作姿势的11,211个样本，并计算出相应的最终REBA分数。在RDT中总共构建了13条决策规则，预测准确率超过95%，路径覆盖率达到83%，描述工作姿势中可能存在的风险倾向。通过RDT和属性重要性分析,经鉴定,下臂和上臂表现为显著增加WMSDs风险水平的关键节段.
结论：这项研究表明，基于计算机视觉的估计和数字孪生分析的ML方法对于全面探索工效学评估中的决策规则是可行的，以预测缝纫机操作员的WMSDs的风险。
结论：此DTAS-EAWP可应用于制造业，以自动分析工作姿势并识别WMSD的风险模式，导致有效预防干预措施的发展。
