用于动态订单拣选过程中上肢疲劳分析的功能方差分析。

Setareh Kazemi Kheiri, Zahra Vahedi, Hongyue Sun, Fadel M Megahed, Lora A Cavuoto
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引用次数: 0

摘要

职业应用从事重复性和动态性工作的仓库工人普遍患有肌肉骨骼疾病。为了预防此类伤害,必须找出在这些重复性活动中影响上肢疲劳的因素。我们的研究表明,任务因素(即瓶子质量和拣选速度)对上肢疲劳有显著影响。在大多数情况下,疲劳指标是一个函数变量,这意味着疲劳得分或测量值是一条随时间变化的曲线,可以建立一个函数模型。在本研究中,我们证明了功能数据分析工具,如功能方差分析(FANOVA),在明确任务因素如何导致上肢疲劳发展方面比传统方法更有效。此外,由于工人之间存在固有差异,可能会影响他们的疲劳发展过程,因此可以采用聚类方法来解决数据异质性问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Functional ANOVA for Upper Extremity Fatigue Analysis during Dynamic Order Picking.

OCCUPATIONAL APPLICATIONSMusculoskeletal disorders are prevalent among warehouse workers who engage in repetitive and dynamic tasks. To prevent such injuries, it is vital to identify the factors that influence fatigue in the upper extremities during these repetitive activities. Our study reveals that task factors, namely the bottle mass and picking rate, significantly influence upper extremity fatigue. In most cases, the fatigue indicator is a functional variable, meaning that the fatigue score or measurement is a curve captured over time, which could be modeled as a function. In this study, we demonstrate that functional data analysis tools, such as functional analysis of variance (FANOVA), prove more effective than traditional methods in specifying how task factors contribute to the development of fatigue in the upper extremities. Furthermore, since there are inherent differences among workers that could affect their fatigue development process, the data heterogeneity could be tackled by employing clustering methods.

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