Omić S, Brkić V K Spasojevic, Golubović T A, Brkić A D, Klarin M M
Ministry of Education, Science and Technological Development - Republic of Serbia, Belgrade, Serbia.
Industrial Engineering Department, Faculty of Mechanical Engineering, University of Belgrade, Belgrade, Serbia.
Work. 2017;56(2):257-265. doi: 10.3233/WOR-172482.
There are recent studies using new industrial workers' anthropometric data in different countries, but for Serbia such data are not available.
This study is the first anthropometric study of Serbian metal industry workers in the country, whose labor force is increasingly employed both on local and international markets. The metal industry is one of Serbia's most important economic sectors.
To this end, we collected the basic static anthropometric dimensions of 122 industrial workers and used principal components analysis (PCA) to obtain multivariate anthropometric models. To confirm the results, the dimensions of an additional 50 workers were collected. The PCA methodology was also compared with the percentile method.
Comparing both data samples, we found that 96% of the participants are within the tolerance ellipsoid. According to this study, multivariate modeling covers a larger extent of the intended population proportion compared to percentiles.
The results of this research are useful for the designers of metal industry workstations. This information can be used in dimensioning the workplace, thus increasing job satisfaction, reducing the risk of injuries and fatalities, and consequently increasing productivity and safety.
近期不同国家有研究使用了新产业工人的人体测量数据，但塞尔维亚尚无此类数据。
本研究是该国对塞尔维亚金属行业工人的首次人体测量研究，该国劳动力在本地和国际市场的就业人数日益增加。金属行业是塞尔维亚最重要的经济部门之一。
为此，我们收集了122名产业工人的基本静态人体测量尺寸，并使用主成分分析（PCA）来获得多变量人体测量模型。为了验证结果，又收集了另外50名工人的尺寸。还将主成分分析方法与百分位数法进行了比较。
比较两个数据样本，我们发现96%的参与者在公差椭球范围内。根据本研究，与百分位数相比，多变量建模涵盖的目标人群比例范围更大。
本研究结果对金属行业工作站的设计者有用。这些信息可用于确定工作场所的尺寸，从而提高工作满意度、降低受伤和死亡风险，进而提高生产率和安全性。
