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Development of Statistical Models for Predicting a Driver’s Hip and Eye Locations

机译:开发用于预测驾驶员的臀部和眼睛位置的统计模型

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Regression equations for estimation of a driver’s hip location (HL) and eye location (EL) using the driver’santhropometric and posture variables have been developed for US drivers. However, those equations arelimited to US drivers and do not include seat adjustment variables (e.g., cushion angle) that may affect adriver’s HL and EL. The present study developed statistical models for prediction of a driver’s HL and ELusing seat configurations including (1) fore-aft seat position, (2) seat height, (3) seat back recline angle, and(4) seat cushion angle. Driving postures of 23 Korean drivers (10 females and 13 males) were measured ina seating buck after adjusting seat configurations according to their preferences. The seat configurations,HLs, ELs, and joint angles of the participants were collected by a motion capture system. HL and ELprediction models based on the seat configurations and driving postures were developed by stepwiseregression. The proposed models showed high accuracy (adj. R~2 = .83 ± .13, RMSE = 19.1 ± 4.2 mm) inprediction of HL and EL. The performance difference between the seat configuration- and posture-basedmodels was not statistically significant. The proposed seat configuration-based models can be used foraccurate estimation of a driver’s HL and EL for occupant packaging layout design.
机译:已经为美国驾驶员开发了使用驾驶员的人体测量学和姿势变量来估计驾驶员的髋部位置(HL)和眼睛位置(EL)的回归方程。但是,这些方程式仅限于美国驾驶员,并且不包括可能影响驾驶员的HL和EL的座椅调节变量(例如,坐垫角度)。本研究开发了用于预测驾驶员的HL和ELusing座椅配置的统计模型,其中包括(1)前后座椅位置,(2)座椅高度,(3)座椅后倾角度和(4)座椅坐垫角度。在根据自己的喜好调整座椅配置后,在座椅升降器中测量了23名韩国驾驶员(10名女性和13名男性)的驾驶姿势。参与者的座椅配置,HL,EL和关节角度通过运动捕捉系统收集。通过逐步回归开发了基于座椅配置和驾驶姿势的HL和ELprediction模型。所提出的模型对HL和EL的预测具有很高的精度(调整R〜2 = .83±.13,RMSE = 19.1±4.2 mm)。基于座椅配置和基于姿势的模型之间的性能差异在统计学上不显着。提议的基于座椅配置的模型可用于准确估算驾驶员的HL和EL,以进行乘员包装布局设计。

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