Adaptive regression model for synthesizing anthropometric population data

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Brolin, ErikHögskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,Chalmers University of Technology, Gothenburg, Sweden,User Centred Product Design,Chalmers tekniska högskola (author)

Adaptive regression model for synthesizing anthropometric population data

Article/chapterEnglish2017

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Elsevier,2017
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LIBRIS-ID:oai:DiVA.org:his-13505
https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-13505URI
https://doi.org/10.1016/j.ergon.2017.03.008DOI
https://research.chalmers.se/publication/249674URI

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Language:English
Summary in:English

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Subject category:ref swepub-contenttype
Subject category:art swepub-publicationtype

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© 2017 Elsevier
This paper presents the development of an adaptive linear regression model for synthesizing of missing anthropometric population data based on a flexible set of known predictive data. The method is based on a conditional regression model and includes use of principal component analysis, to reduce effects of multicollinearity between selected predictive measurements, and incorporation of a stochastic component, using the partial correlation coefficients between predicted measurements. In addition, skewness of the distributions of the dependent variables is considered when incorporating the stochastic components. Results from the study show that the proposed regression models for synthesizing population data give valid results with small errors of the compared percentile values. However, higher accuracy was not achieved when the number of measurements used as independent variables was increased compared to using only stature and weight as independent variables. This indicates problems with multicollinearity that principal component regression were not able to overcome. Descriptive statistics such as mean and standard deviation values together with correlation coefficients is sufficient to perform the conditional regression procedure. However, to incorporate a stochastic component when using principal component regression requires raw data on an individual level.Relevance to industryWhen developing products, workplaces or systems, it is of great importance to consider the anthropometric diversity of the intended users. The proposed regression model offers a procedure that gives valid results, maintains the correlation between the measurements that are predicted and is adaptable regarding which, and number of, predictive measurements that are selected.

Subject headings and genre

TEKNIK OCH TEKNOLOGIER Maskinteknik Produktionsteknik, arbetsvetenskap och ergonomi hsv//swe
ENGINEERING AND TECHNOLOGY Mechanical Engineering Production Engineering, Human Work Science and Ergonomics hsv//eng
NATURVETENSKAP Matematik hsv//swe
NATURAL SCIENCES Mathematics hsv//eng
Anthropometry
Regression
Multivariate
Conditional
PCA
Variance
User Centred Product Design
Användarcentrerad produktdesign
INF202 Virtual Ergonomics
INF202 Virtual Ergonomics

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Högberg, DanHögskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,User Centred Product Design,University of Skövde(Swepub:his)hogd (author)
Hanson, LarsHögskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,Chalmers University of Technology, Gothenburg, Sweden / Industrial Development, Scania CV, Södertälje, Sweden,User Centred Product Design,Chalmers tekniska högskola(Swepub:cth)hansonl (author)
Örtengren, Roland,1942Department of Product and Production Development, Chalmers University of Technology, Gothenburg, Sweden,Chalmers tekniska högskola,Chalmers University of Technology(Swepub:cth)orten (author)
Högskolan i SkövdeInstitutionen för ingenjörsvetenskap (creator_code:org_t)

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In:International Journal of Industrial Ergonomics: Elsevier59, s. 46-530169-81411872-8219

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By the author/editor: Brolin, Erik; Högberg, Dan; Hanson, Lars; Örtengren, Rolan ...

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ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Mechanical Engin ...; and Production Engin ...

NATURAL SCIENCES: NATURAL SCIENCES; and Mathematics

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By the university: University of Skövde; Chalmers University of Technology

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