his.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Adaptive regression model for prediction of anthropometric data
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. Department of Product and Production Development, Chalmers University of Technology, Gothenburg, Sweden. (Användarcentrerad produktdesign, User Centred Product Design)ORCID-id: 0000-0002-0125-0832
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. (Användarcentrerad produktdesign, User Centred Product Design)ORCID-id: 0000-0003-4596-3815
Högskolan i Skövde, Institutionen för ingenjörsvetenskap. Högskolan i Skövde, Forskningscentrum för Virtuella system. Industrial Development, Scania, Scania CV, Södertälje, Sweden / Department of Product and Production Development, Chalmers University of Technology, Gothenburg, Sweden. (Användarcentrerad produktdesign, User Centred Product Design)ORCID-id: 0000-0002-7232-9353
Department of Product and Production Development, Chalmers University of Technology, Gothenburg, Sweden.
2017 (engelsk)Inngår i: International Journal of Human Factors Modelling and Simulation, ISSN 1742-5549, Vol. 5, nr 4, s. 285-305Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

This paper presents and evaluates an adaptive linear regression model for the prediction of unknown anthropometric data based on a flexible set of known predictive data. The method is based on conditional regression and includes use of principal component analysis to reduce effects of multicollinearity between the predictive variables. Results from the study show that the proposed adaptive regression model produces more accurate predictions compared to a flat regression model based on stature and weight, and also compared to a hierarchical regression model, that uses geometric and statistical relationships between body measurements to create specific linear regression equations in a hierarchical structure. An additional evaluation shows that the accuracy of the adaptive regression model increases logarithmically with the sample size. Apart from the sample size, the accuracy of the regression model is affected by the number of, and on which measurements that are, variables in the predictive dataset.

sted, utgiver, år, opplag, sider
InderScience Publishers, 2017. Vol. 5, nr 4, s. 285-305
Emneord [en]
anthropometry, multivariate, regression, conditional, PCA, capability, digital human modelling, DHM
HSV kategori
Forskningsprogram
Användarcentrerad produktdesign; INF202 Virtual Ergonomics
Identifikatorer
URN: urn:nbn:se:his:diva-14201DOI: 10.1504/IJHFMS.2017.10008080OAI: oai:DiVA.org:his-14201DiVA, id: diva2:1147344
Prosjekter
CROMMVirtual Driver ErgonomicsTilgjengelig fra: 2017-10-05 Laget: 2017-10-05 Sist oppdatert: 2019-02-14bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekst

Personposter BETA

Brolin, ErikHögberg, DanHanson, Lars

Søk i DiVA

Av forfatter/redaktør
Brolin, ErikHögberg, DanHanson, Lars
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 244 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf