A statistical model including age to predict passenger postures in the rear seats of automobiles

Abstract

Few statistical models of rear seat passenger posture have been published, and none has taken into account the effects of occupant age. This study developed new statistical models for predicting passenger postures in the rear seats of automobiles. Postures of 89 adults with a wide range of age and body size were measured in a laboratory mock-up in seven seat configurations. Posture-prediction models for female and male passengers were separately developed by stepwise regression using age, body dimensions, seat configurations and two-way interactions as potential predictors. Passenger posture was significantly associated with age and the effects of other two-way interaction variables depended on age. A set of posture-prediction models are presented for women and men, and the prediction results are compared with previously published models. This study is the first study of passenger posture to include a large cohort of older passengers and the first to report a significant effect of age for adults. The presented models can be used to position computational and physical human models for vehicle design and assessment.

Practitioner Summary:

The significant effects of age, body dimensions and seat configuration on rear seat passenger posture were identified. The models can be used to accurately position computational human models or crash test dummies for older passengers in known rear seat configurations.

Keywords:

• Passenger posture
• rear seat
• hip location
• eye location
• age

Acknowledgments

This research was sponsored by the Toyota Collaborative Safety Research Center. We thank our collaborators at the CSRC who contributed significantly to this work, including Chuck Gulash, Megan Mackenzie, Jason Hallman and Palani Palaniappan. Many people at UMTRI contributed to the success of this project, including Brian Eby, Charlie Bradley, Steven Thomas and Stewart Simonett, who developed the mock-ups and fixtures. Laura Malik and Jamie Moore led the data collection, assisted by numerous student research assistants, including Alexis Baker, Olivia DeTroyer, Tiffany Fredrick, Mollie Pozolo, Rachel Palmer, Sarah Scholten and Lindsay Youngren. These students were assisted in data processing and scan landmark extraction by Christian Calyore, David Hayashi, Danielle Hedden, Jordan MacDonald, Huibin Hu, Ryan Warner and Mikhail Wise.

Disclosure statement

No potential conflict of interest was reported by the authors.