期刊
ERGONOMICS
卷 -, 期 -, 页码 -出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00140139.2023.2284672
关键词
Ergonomics; multivariate analysis; Gaussian copula; iterative proportional fitting; nearest neighbour approach
This study introduces a synthesis and validation algorithm for creating virtual copies of existing datasets, with the aim of overcoming restrictions on publishing anthropometric datasets. The algorithm was applied to a detailed anthropometric dataset from a public-health study in Germany and was found to produce statistically identical results to the original dataset. The weighted virtual dataset can be freely published and used for ergonomic designing, showing its significance in research for Germany and other regions.
For the German working-age population no publicly available and detailed anthropometric raw dataset exists, although several studies have collected anthropometric datasets. Unfortunately, the publication of raw data may be restricted by data usage regulations. This study presents a synthesis and validation algorithm to create a virtual copy of an already existing dataset. A detailed anthropometric dataset from a regional epidemiological public-health study in Germany was used for the synthesis and validation algorithm. Results revealed only minor deviations within the validation process. Compared to the original dataset, the virtual dataset was statistically almost identical. In a next step, the virtual dataset was weighted to approximate nationally representative values. In summary, the computed unweighted and weighted virtual data can be published without restrictions and used for ergonomic designing. Furthermore, the synthesis and validation algorithm is suitable for the generation of virtual copies and can be applied to other detailed anthropometric datasets. Data usage regulations may restrict the publication of anthropometric datasets. A synthesis and validation algorithm was developed which can be applied to existing anthropometric datasets to create a virtual copy that is almost identical and can be published. In the current study this algorithm was used for data from Germany.
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