TY  - JOUR
PY  - 2015//
TI  - Statistical models for predicting automobile driving postures for men and women including effects of age
JO  - Human factors
A1  - Park, Jangwoon
A1  - Ebert, Sheila M.
A1  - Reed, Matthew P.
A1  - Hallman, Jason J.
SP  - 261
EP  - 278
VL  - 58
IS  - 2
N2  - BACKGROUND: Previously published statistical models of driving posture have been effective for vehicle design but have not taken into account the effects of age. <br><br>OBJECTIVE: The present study developed new statistical models for predicting driving posture. <br><br>METHODS: Driving postures of 90 U.S. drivers with a wide range of age and body size were measured in laboratory mockup in nine package conditions. Posture-prediction models for female and male drivers were separately developed by employing a stepwise regression technique using age, body dimensions, vehicle package conditions, and two-way interactions, among other variables. <br><br>RESULTS: Driving posture was significantly associated with age, and the effects of other variables depended on age. A set of posture-prediction models is presented for women and men. The results are compared with a previously developed model. <br><br>CONCLUSION: The present study is the first study of driver posture to include a large cohort of older drivers and the first to report a significant effect of age. APPLICATION: The posture-prediction models can be used to position computational human models or crash-test dummies for vehicle design and assessment.<p />  <p>Language: en</p>
LA  - en
SN  - 0018-7208
UR  - http://dx.doi.org/10.1177/0018720815610249
ID  - ref1
ER  -