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汽车手动装配的体力疲劳评估方法:基于 ARE 平台的脑氧饱和度实验。

A Physical Fatigue Evaluation Method for Automotive Manual Assembly: An Experiment of Cerebral Oxygenation with ARE Platform.

机构信息

Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK.

School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China.

出版信息

Sensors (Basel). 2023 Nov 26;23(23):9410. doi: 10.3390/s23239410.

Abstract

Due to the complexity of the automobile manufacturing process, some flexible and delicate assembly work relies on manual operations. However, high-frequency and high-load repetitive operations make assembly workers prone to physical fatigue. This study proposes a method for evaluating human physical fatigue for the manual assembly of automobiles with methods: NIOSH (National Institute for Occupational Safety and Health), OWAS (Ovako Working Posture Analysis System) and RULA (Rapid Upper Limb Assessment). The cerebral oxygenation signal is selected as an objective physiological index reflecting the human fatigue level to verify the proposed physical fatigue evaluation method. Taking auto seat assembly and automobile manual assembly as an example, 18 group experiments were carried out with the ARE platform (Augmented Reality-based Ergonomic Platform). Furthermore, predictions of metabolic energy expenditure were performed for experiments in Tecnomatix Jack. Finally, it is concluded that the proposed physical fatigue evaluation method can reflect the human physical fatigue level and is more accurate than the evaluation of metabolic energy consumption in Tecnomatix Jack because of the immersion that comes with the AR devices and the precision that comes with motion capture devices.

摘要

由于汽车制造过程的复杂性,一些灵活而精细的装配工作依赖于人工操作。然而,高频和高负荷的重复操作使装配工人容易出现身体疲劳。本研究提出了一种利用 NIOSH(美国职业安全与健康研究所)、OWAS(Ovako 工作姿势分析系统)和 RULA(快速上肢评估)方法评估汽车手动装配中人体体力疲劳的方法。选择脑氧信号作为反映人体疲劳水平的客观生理指标,验证所提出的体力疲劳评估方法。利用基于增强现实的人体工程学平台(ARE 平台),以汽车座椅装配和汽车手动装配为例进行了 18 组实验。此外,还在 Tecnomatix Jack 中对实验进行了代谢能量消耗预测。最后得出结论,所提出的体力疲劳评估方法可以反映人体体力疲劳水平,并且由于 AR 设备的沉浸感和运动捕捉设备的精度,比 Tecnomatix Jack 中的代谢能量消耗评估更准确。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db29/10708740/49f57d3e1e34/sensors-23-09410-g001.jpg

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