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Predicting shoulder musculoskeletal discomfort from worker anthropometry and workplane design in a repetitive task

机译:在重复任务中预测工人人类学测量仪和工作平面设计的肩膀肌肉骨骼不适

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A study was conducted to determine the effects of biodemographic, anthropometric, workplace and kinematic factors upon shoulder musculoskeletal discomfort (SMD). The study was performed in two phases. In the first phase the experiment was focused on gathering workplace, anthropometric and biodemographic factors, in addition to discomfort data. The second phase was conducted to gather the kinematic factors. Participants performed a mail-sorting task and at periodic intervals rated the perceived discomfort using a questionnaire. Four duty cycles (i.e., repetition rates) and five mailbox locations were evaluated. SMD was the dependent variable examined in this experiment. A total of sixteen independent variables were considered in this experiment. These variables were selected from four categories as follows: biodemographic (age and gender), anthropometric (height, weight, forward reach, midshoulder height, upper arm length and elbow to grip length), workplace (duty cycle, number of repetitions and mailbox location) and kinematic variables (movement time, elbow flexion, shoulder forward flexion, shoulder horizontal flexion and abduction/adduction angles). By using multiple regression techniques and statistical variable elimination methods it was determined that the best model to predict SMD included ten variables. The results show that from all the ergonomic variables evaluated, the kinematic variables played an important role in the percentage of the total variance explained by the regression models.
机译:进行了一项研究,以确定肩部肌肉骨骼不适(SMD)对肩部肌肉骨骼不适的效果。该研究分两期进行。在第一阶段,除了不适数据之外,实验还集中在收集工作场所,人体计量和生物血迹因子。进行第二阶段以收集运动因素。参与者执行了邮件排序任务,周期性间隔使用调查问卷评估了感知的不适。评估四个占空比(即重复率)和五个邮箱位置。 SMD是在该实验中检查的依赖变量。在该实验中考虑了总共十六个独立变量。这些变量选自四类:生物术(年龄和性别),人类测量(高度,重量,前达,中长臂高度,上臂长度和弯头到握持长度),工作场所(占空比,重复次数和邮箱位置)和运动变量(运动时间,肘部屈曲,肩正屈曲,肩部水平屈曲和绑架/内收角)。通过使用多元回归技术和统计变量消除方法,确定预测SMD的最佳模型包括十个变量。结果表明,来自评估的所有符合人体工程学变量,运动变量在回归模型解释的总方差的百分比中发挥着重要作用。

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