Computational prediction of headrest interaction and support forces in adjustable seating

BackgroundModern vehicle seats often include adjustable features that let passengers customize their seating positions to improve their experience. One key feature is the headrest, which can be adjusted to accommodate passengers of different sizes and preferences. However, limited studies have quantitatively examined human-seat interaction in the head-neck region, accounting for different anthropometric characteristics and backrest seat configurations.ObjectiveThe research objective is to develop a computational approach to predict human-headrest interaction, enabling a quantitative assessment of headrest configurations.MethodThis study developed a computational framework based on multibody dynamics to simulate human-seat interaction, including solutions for sitting posture kinematics and for predicting headrest-supporting force across various seating conditions. Seating tests were conducted with 25 subjects to validate the proposed approach.ResultsThe model showed a strong correlation with experimental measurements of headrest supporting force (ρ = 0.872) at the user-selected headrest position. The results support the application of the proposed model in predicting head-headrest interaction and the external loading conditions of the head-neck region.ConclusionsA multibody human-seat interaction model was developed and validated. It predicts headrest supporting forces consistent with experimental measurement. The framework enables efficient analysis of reclined headrest interactions and supports rapid evaluation and customization of designs across seating conditions and users.