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  • 1.
    Brolin, Erik
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Chalmers University of Technology, Gothenburg, Sweden.
    Högberg, Dan
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Hanson, Lars
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Chalmers University of Technology, Gothenburg, Sweden / Industrial Development, Scania CV, Södertälje, Sweden.
    Örtengren, Roland
    Department of Product and Production Development, Chalmers University of Technology, Gothenburg, Sweden.
    Adaptive regression model for synthesizing anthropometric population data2017In: International Journal of Industrial Ergonomics, ISSN 0169-8141, E-ISSN 1872-8219, Vol. 59, p. 46-53Article in journal (Refereed)
    Abstract [en]

    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 industry

    When 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.

  • 2.
    Hanson, Lars
    et al.
    Division of Ergonomics, Department of Design Sciences, Lund University, P.O. Box 118, SE-221 00 Lund, Sweden / Saab Automobile AB, SE-461 80 Trollhättan, Sweden.
    Blomé, Mikael
    Division of Ergonomics, Department of Design Sciences, Lund University, P.O. Box 118, SE-221 00 Lund, Sweden.
    Dukic, Tania
    National Institute for Working Life/West, P.O. Box 8850, SE-402 72 Gothenburg, Sweden / Division of Human Factors Engineering, Department of Product and Production Development, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden.
    Högberg, Dan
    University of Skövde, School of Technology and Society.
    Guide and documentation system to support digital human modeling applications2006In: International Journal of Industrial Ergonomics, ISSN 0169-8141, E-ISSN 1872-8219, Vol. 36, no 1, p. 17-24Article in journal (Refereed)
    Abstract [en]

    Car developers use digital human modeling tools to analyze and visualize car interiors in relation to human characteristics before the vehicles are actually constructed. Developers, reviewers and users of human simulation tools often claim that such tools can reduce development time and costs. In car industry companies today, human simulation tools are used by a single or a few experts in an informal working process with insufficient documentation. To prepare for extensive, effective and efficient use of human simulation modeling tools in industry with several users within a company, the aim of this study was to design and evaluate a digital guide and documentation system to support digital human modeling applications. A participative design approach was used in developing the guide, involving human simulation tool users and managers within the General Motors Group. The system consists of two major parts: a usage guide and database. The usage guide is divided into three sections considering the professionals involved: (1) initiation of human–vehicle interaction analysis, (2) preparation and running of the digital human tool and (3) recommendation formulation and closure. The guide was connected to a database with search and print capabilities for previous and ongoing human simulation analyses. Sixteen subjects from industry and university settings evaluated the support system. Results showed that the users appreciated the guide and documentation system, in particular, the database for storing human simulation work. The guide was perceived as being especially useful for guidance in large analyses, whereas for smaller ones the subjects felt the formalized guide was too lengthy and time consuming. The use of the formalized guide is likely to reduce differences in results, within and between tool users. The support system guides the simulation tool user through an acknowledged process; it documents, stores and keeps track of ongoing and previous analyses, and facilitates the reuse of studies.

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