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Dive into the research topics where Jason J. Hallman is active.

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Featured researches published by Jason J. Hallman.


Human Factors | 2016

Statistical Models for Predicting Automobile Driving Postures for Men and Women Including Effects of Age

Jangwoon Park; Sheila M. Ebert; Matthew P. Reed; Jason J. Hallman

Background: Previously published statistical models of driving posture have been effective for vehicle design but have not taken into account the effects of age. Objective: The present study developed new statistical models for predicting driving posture. 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. 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. 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.


Ergonomics | 2016

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

Jangwoon Park; Sheila M. Ebert; Matthew P. Reed; Jason J. Hallman

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.


Journal of Safety Research | 2017

Evaluating an intervention to improve belt fit for adult occupants: Promoting positive beliefs

Lisa Buckley; Monica L.H. Jones; Sheila M. Ebert; Matthew P. Reed; Jason J. Hallman

INTRODUCTION Seat belt use provides significant public health benefit, however, most public awareness campaigns have generally focused on seat belt use rather than encouraging adults to improve seat belt fit with belt placement. This study provides an evaluation of a video-based intervention to improve adult belt fit assessing whether a video-based intervention can target beliefs and knowledge of seat belt placement and be perceived as relevant by the target audience. METHOD An intervention group of 29 adults (15 women and 14 men) and a comparison group of 99 adults (41 women and 47 men) participated. RESULTS The intervention group had significantly more favorable beliefs around belt fit than the comparison group related to Health Belief Model constructs of higher self-efficacy, greater benefits, and fewer barriers. The intervention group was also significantly better at accurately drawing belt fit than the comparison group. The video intervention was described as relevant, interesting, and the intervention group favored the provision of a diverse sample of models in the intervention. CONCLUSIONS Overall, the study provides insight into relevant target beliefs for an intervention focused on belt fit and suggests that a brief video-based intervention in the style of a public service announcement may be effective in promoting positive beliefs and knowledge around belt fit. Future efforts should confirm these findings with a larger sample size spanning multiple geographic and demographic areas. PRACTICAL APPLICATIONS These findings can help better inform intervention initiatives to improve occupant belt fit.


Traffic Injury Prevention | 2018

Comparison of three-point belt fit between humans and ATDs in rear seats

Jangwoon Park; Sheila M. Ebert; Matthew P. Reed; Jason J. Hallman

ABSTRACT Objective: The anthropomorphic test devices (ATDs) in the Hybrid III family are widely used as human surrogates to test the crash performance of vehicles. A previous study demonstrated that passenger belt fit in rear seats was affected by high body mass index (BMI) and to a lesser extent by increased age. Specifically, the lap belt was worn higher and more forward as BMI and age increased. The objective of this study was to compare passenger belt fit to the belt fit achieved when installing the small female and midsize male Hybrid III adult ATDs using standard procedures. Methods: The ATDs were installed using standardized procedures in the same conditions previously used with volunteers. Belt fit was measured using methods analogous to those used for the volunteers. Comparative human belt fit values were obtained by using regression analysis with the volunteer data to calculate the mean expected belt fit for people the same size as the ATDs. Results: For the small female ATD, the upper edge of the lap belt was on average 59 mm forward and 11 mm above the anterior–superior iliac spine (ASIS) landmark on the ATD pelvis bone. In contrast, the belt position for similar size passengers was 17 mm forward and 22 mm above the ASIS. For the midsize male ATD, the belt was 34 mm forward and 10 mm above the ASIS. For similar size passengers, the position was 38 mm forward and 44 mm above the ASIS. For context, the belt width in this study was 38 mm. Discussion: The results suggest that the lap belt fit obtained by ATDs is more idealized but more repeatable compared to that achieved by similar size passengers. Future standardization efforts should consider investigating whether new belt-positioning procedures with ATDs may improve the biofidelity of ATD response.


Stapp car crash journal | 2013

Effects of Driver Characteristics on Seat Belt Fit

Matthew P. Reed; Sheila M. Ebert; Jason J. Hallman


Stapp car crash journal | 2016

Development, Evaluation, and Sensitivity Analysis of Parametric Finite Element Whole-Body Human Models in Side Impacts

Eunjoo Hwang; Jingwen Hu; Cong Chen; Katelyn F. Klein; Carl S. Miller; Matthew P. Reed; Jonathan D. Rupp; Jason J. Hallman


SAE Technical Paper Series (Society of Automotive Engineers) | 2016

Rapid Development of Diverse Human Body Models for Crash Simulations through Mesh Morphing

Eunjoo Hwang; Jason J. Hallman; Katelyn F. Klein; Jonathan D. Rupp; Matthew P. Reed; Jingwen Hu


Procedia Manufacturing | 2015

Development of an Optimization Method for Locating the Pelvis in an Automobile Seat

Jangwoon Park; Sheila M. Ebert; Matthew P. Reed; Jason J. Hallman


Stapp car crash journal | 2015

Evaluation of the Kinematic Responses and Potential Injury Mechanisms of the Jejunum during Seatbelt Loading.

Meghan K. Howes; Warren N. Hardy; Amanda M. Agnew; Jason J. Hallman


Journal of Safety Research | 2018

Evaluating an intervention to improve belt fit for adult occupants

Monica L.H. Jones; Lisa Buckley; Sheila M. Ebert; Matthew P. Reed; Jason J. Hallman

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Jingwen Hu

University of Michigan

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