Article: A case study of digital human modelling assisted occupant packaging design: comparing driving posture and position prediction methods Journal: International Journal of Human Factors and Ergonomics (IJHFE) 2025 Vol.12 No.5 pp.27 - 57 Abstract: Accurately predicting driving postures and positions is crucial for occupant packaging design to accommodate diverse drivers. However, this is challenging due to individual variability and limited access to user data. Digital human modelling (DHM) tools enable posture prediction in virtual environments. This paper presents a case study comparing two driving prediction methods: a statistical prediction method (SPM) and an optimisation prediction method (OPM). Both were evaluated using data from two car models with different seat heights, involving 199 participants whose seat, eye-point, and steering wheel positions were measured. Results showed SPM was more accurate for vertical positioning, whereas OPM for fore-aft positioning. The effectiveness of each method varied by car model, with SPM aligning better in the higher-seated vehicle and OPM performing better in the lower-seated vehicle. These findings highlight the practical, context-specific performance of posture prediction methods. Methodological insights guide the improvement of DHM tool use in occupant packaging. Inderscience Publishers - linking academia, business and industry through research

Title: A case study of digital human modelling assisted occupant packaging design: comparing driving posture and position prediction methods

Authors: Estela Perez Luque; Erik Brolin; Pernilla Nurbo; Maurice Lamb; Dan Högberg

Addresses: School of Engineering Science, University of Skövde, Skövde, Sweden ' School of Engineering Science, University of Skövde, Skövde, Sweden ' Ergonomics, Volvo Cars, Gothenburg, Sweden ' School of Informatics, University of Skövde, Skövde, Sweden ' School of Engineering Science, University of Skövde, Skövde, Sweden

Abstract: Accurately predicting driving postures and positions is crucial for occupant packaging design to accommodate diverse drivers. However, this is challenging due to individual variability and limited access to user data. Digital human modelling (DHM) tools enable posture prediction in virtual environments. This paper presents a case study comparing two driving prediction methods: a statistical prediction method (SPM) and an optimisation prediction method (OPM). Both were evaluated using data from two car models with different seat heights, involving 199 participants whose seat, eye-point, and steering wheel positions were measured. Results showed SPM was more accurate for vertical positioning, whereas OPM for fore-aft positioning. The effectiveness of each method varied by car model, with SPM aligning better in the higher-seated vehicle and OPM performing better in the lower-seated vehicle. These findings highlight the practical, context-specific performance of posture prediction methods. Methodological insights guide the improvement of DHM tool use in occupant packaging.

Keywords: occupant packaging design; digital human modelling; DHM; driving posture; seated posture prediction; driver position; statistical regression; optimisation method; ergonomic simulation; H-point prediction; eye-point prediction; steering wheel position; vehicle interior design; automotive ergonomics.

DOI: 10.1504/IJHFE.2025.150419

International Journal of Human Factors and Ergonomics, 2025 Vol.12 No.5, pp.27 - 57

Received: 31 May 2025
Accepted: 03 Oct 2025
Published online: 12 Dec 2025 *

Title: A case study of digital human modelling assisted occupant packaging design: comparing driving posture and position prediction methods

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