Development of statistical geometric models for prediction of a driver's hip and eye locations

• Published in: International journal of industrial ergonomics Vol. 72; pp. 320 - 329
• Main Authors: Park, Jangwoon, Jung, Kihyo, Lee, Baekhee, Choi, Younggeun, Yang, Xiaopeng, Lee, Seunghoon, You, Heecheon
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.07.2019; Elsevier BV
• Subjects: Angles (geometry); Anthropometry; Automobile drivers; Automotive engineering; Computer simulation; Driving posture; Eye location; Eye movements; Females; Hip; Hip joint; Hip location; Human motion; Interior design; Males; Mathematical models; Motion capture; Multiple regression analysis; Occupant packing layout design; Packaging; Performance prediction; Position (location); Prediction models; Statistical analysis; Statistical geometric model; Statistical prediction; Occupant packing layout design; Driving posture; Hip location; Eye location; Statistical geometric model
• ISSN: 0169-8141; 1872-8219
• DOI: 10.1016/j.ergon.2019.06.011

The Society of Automotive Engineers (SAE) J1517 and J941 models of a driver-selected seat position and a driver's eye location mainly rely on their statistical linear relationships with seat configuration and package variables. Although the SAE models are useful for vehicle interior design, their prediction performance was not provided. The present study was intended to develop accurate prediction models of a driver's hip location (HL) and eye location (EL) based on their statistical geometric relationships with anthropometric dimensions and driving postures. A driving simulation experiment was conducted with 40 Korean drivers (20 males and 20 females) in a seating buck reconfigurable to various package conditions. The anthropometric measurements, HLs, ELs, and joint angles of the participants were collected using an anthropometer, a motion capture system, and a digital human model simulation program. Two types (full model and simplified model) of statistical geometric models (SGMs) for HL and EL prediction were developed by multiple regression analysis of the anthropometric measurements and driving postures on the HLs and ELs. The average adjusted R2 and RMSE of the SGMs were .82 (± .06) and 25.7 (±3.3) mm, respectively. The SGMs showed accurate and stable prediction performance because the SGMs additionally incorporated the geometric relationships of HL and EL with anthropometric dimensions and joint angles. The SGMs would be useful to predict the HLs and ELs of drivers with various body sizes and joint angles in occupant packaging. •The present study developed models for predicting a driver's hip and eye locations.•The relationships between joint locations, body sizes, and angles were quantified.•The average adj. R2 and RMSE of the models were .82 (± .06) and 25.7 (±3.3) mm.•The developed models are of use to predict the hip and eye locations of population.