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Statistical Posture Prediction of Vehicle Occupants in Digital Human Modelling Tools
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (User Centred Product Design (UCPD))ORCID iD: 0000-0002-0125-0832
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (User Centred Product Design (UCPD))ORCID iD: 0000-0003-4596-3815
Ergonomics, Customer Experience Centre, Volvo Cars, Gothenburg, Sweden.
2020 (English)In: Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Posture, Motion and Health: 11th International Conference, DHM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part I / [ed] Vincent G. Duffy, Cham: Springer, 2020, p. 3-17Conference paper, Published paper (Refereed)
Abstract [en]

When considering vehicle interior ergonomics in the automotive design and development process, it is important to be able to realistically predict the initial, more static, seated body postures of the vehicle occupants. This paper demonstrates how published statistical posture prediction models can be implemented into a digital human modelling (DHM) tool to evaluate and improve the overall posture prediction functionality in the tool. The posture prediction functionality uses two different posture prediction models in a sequence, in addition to the DHM tool´s functionality to optimize postures. The developed posture prediction functionality is demonstrated and visualized with a group of 30 digital human models, so called manikins, by using accurate car geometry in two different use case scenarios where the sizes of the adjustment ranges for the steering wheel and seat are altered. The results illustrate that it is possible to implement previously published posture prediction models in a DHM tool. The results also indicate that, depending on how the implemented functionality is used, different results will be obtained. Having access to a digital tool that can predict and visualize likely future vehicle occupants’ postures, for a family of manikins, enables designers and developers to consider and evaluate the human-product interaction and fit, in a consistent and transparent manner. © 2020, Springer Nature Switzerland AG.

Place, publisher, year, edition, pages
Cham: Springer, 2020. p. 3-17
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 12198
Keywords [en]
Digital human modelling, Posture prediction, Vehicle ergonomics, Automobile manufacture, Digital devices, Ergonomics, Forecasting, Health, Health risks, Human computer interaction, Risk management, Safety engineering, Vehicles, Automotive designs, Digital human models, Product interaction, Steering wheel, Use case scenario, Vehicle occupants, Predictive analytics
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
User Centred Product Design
Identifiers
URN: urn:nbn:se:his:diva-18893DOI: 10.1007/978-3-030-49904-4_1Scopus ID: 2-s2.0-85088750749ISBN: 978-3-030-49903-7 (print)ISBN: 978-3-030-49904-4 (electronic)OAI: oai:DiVA.org:his-18893DiVA, id: diva2:1457231
Conference
11th International Conference, DHM 2020, Held as Part of the 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020
Projects
Virtual Driver Ergonomics
Part of project
Synergy Virtual Ergonomics (SVE), Knowledge FoundationADOPTIVE – Automated Design & Optimisation of Vehicle Ergonomics, Knowledge Foundation
Funder
Knowledge Foundation
Note

This work has been made possible with the support from The Knowledge Foundation and the associated INFINIT research environment at the University of Skövde (projects: Virtual Driver Ergonomics, Synergy Virtual Ergonomics and ADOPTIVE), in Sweden, and SAFER - Vehicle and Traffic Safety Centre at Chalmers, Sweden, and by the participating organizations. This support is gratefully acknowledged.

Available from: 2020-08-11 Created: 2020-08-11 Last updated: 2022-04-19Bibliographically approved

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Brolin, ErikHögberg, Dan

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