An empirical description of the dispersion of 5th and 95th percentiles in worldwide anthropometric data applied to estimating accommodation with unknown correlation values

Anthropometric accommodation percentage comparison for univariate and multivariate representative Mexican hand models

BACKGROUND: Adopting awkward postures at work has a great impact on productivity and work-related musculoskeletal disorders. Considering anthropometric data in the design of products and workplaces can diminish this impact. The traditional univariate-percentile-approach is one of the most implemented in the anthropometric analysis, even though it has proved limitations in comparison with multivariate-approaches. OBJECTIVE: To develop univariate and multivariate hand models considering four anthropometric dimensions, and to theoretically compare the univariate and multivariate accommodation percentages. METHODS: Univariate percentile models corresponding to the database of real subject nearest-neighbors to the 5th and 95th percentiles were obtained for the male and female population. Two multivariate approaches were implemented on the central 90% of both populations: 2D principal component analysis and archetypal analysis. The accommodation percentage for each family of models was obtained based on the population that simultaneously fit all the anthropometric dimensions. The goodness-of-fit and McNemar’s tests were performed to statistically analyze the accommodation percentages. RESULTS: Eight human hand models were obtained via Principal Component Analysis while two, three, four, and eight Archetypal Analysis models (male-population) and two, three, six, and eight Archetypal Analysis models (female-population) were selected after a root-sum-of-squares analysis for k = 1, ...  ,10 archetypes. CONCLUSION: The results showed that the Principal Component Analysis models obtained a higher accommodation level, followed by the Archetypal Analysis and percentile models (male population). In the case of the female population, models obtained by multivariate-Archetypal Analysis (k = 8) obtained a higher accommodation percentage.

An anthropometric study of Serbian metal industry workers

BACKGROUND

There are recent studies using new industrial workers' anthropometric data in different countries, but for Serbia such data are not available.

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.