As automation transforms drivers into passengers, the deployment of automated vehicles has the potential to greatly increase the incidence of motion sickness. A study was conducted to quantify motion sickness response of front-seat passengers performing ecologically relevant passenger activities during conditions consistent with driving on public roadways. Fifty-two adults with a large range of self-reported levels of motion sickness susceptibility and age participated in data collection on a closed test track in a passenger sedan. Motion sickness ratings increased with task vs. no-task, and moderate vs. low acceleration test conditions. Increased motion sickness susceptibility was associated with higher motion sickness ratings. In comparison to older participants (age >60), younger participants (age <60) experienced increased motion sickness. This is the first in-vehicle study that systematically compared normative passenger activities and acceleration magnitudes typical of normative driving conditions on motion sickness response for a large, diverse sample of passengers, enabling the exploration of the effects of covariates. Practitioner Summary: The data demonstrate that a relatively large range of motion sickness response can be expected to result from passengers performing visual tasks in passenger vehicles. Measurement and modelling efforts should seek to elucidate relationships among the factors contributing to motion sickness for the purpose of informing and prioritizing future countermeasures for AVs. Key Aspect of Research (character limit): Motion sickness may be an important barrier to widespread adoption of automated vehicles ©UMTRI

Objective: To define static, dynamic, and cognitive fit and their interactions as they pertain to exosystems and to document open research needs in using these fit characteristics to inform exosystem design. Background: Initial exosystem sizing and fit evaluations are currently based on scalar anthropometric dimensions and subjective assessments. As fit depends on ongoing interactions related to task setting and user, attempts to tailor equipment have limitations when optimizing for this limited fit definition. Method: A targeted literature review was conducted to inform a conceptual framework defining three characteristics of exosystem fit: static, dynamic, and cognitive. Details are provided on the importance of differentiating fit characteristics for developing exosystems. Results: Static fit considers alignment between human and equipment and requires understanding anthropometric characteristics of target users and geometric equipment features. Dynamic fit assesses how the human and equipment move and interact with each other, with a focus on the relative alignment between the two systems. Cognitive fit considers the stages of human-information processing, including somatosensation, executive function, and motor selection. Human cognitive capabilities should remain available to process task- and stimulus-related information in the presence of an exosystem. Dynamic and cognitive fit are operationalized in a task-specific manner, while static fit can be considered for predefined postures. Conclusion: A deeper understanding of how an exosystem fits an individual is needed to ensure good human–system performance. Development of methods for evaluating different fit characteristics is necessary. Application: Methods are presented to inform exosystem evaluation across physical and cognitive characteristics.

Objective: Highly reclined postures may be common among passengers in future automated vehicles. A laboratory study was conducted to address the need for posture and belt fit in these seating configurations. Methods: In a laboratory vehicle mockup, the postures of 24 men and women with a wide range of body size were measured in a typical front vehicle seat at seat back angles of 23°, 33°, 43°, and 53°. Data were gathered with and without a sitter-adjusted headrest. Posture was characterized by the locations of skeletal joint centers estimated from digitized surface landmarks. Results: Regression analysis demonstrated that the pelvis rotated rearward and lumbar spine flexion decreased with increasing recline. The lap portion of the 3-point belt was more rearward relative to the pelvis in more-reclined postures, and the torso portion crossed the clavicle closer to the midline of the body. Regression equations were developed to predict posture and belt fit variables as a function of passenger characteristics, seat back angle, and the use of the headrest. Conclusions: Spine posture changes as the torso reclines in an automotive seat, and belt fit is altered by the change in posture. The results can be used to accurately position crash test dummies and computation human models and to guide the design of belt restraints.

Recent studies have suggested that a relationship exists between crash injury risk and occupant posture, particularly in postures different from those used with anthropomorphic test devices (ATDs) ...

Objective: This study presents anthropometric data for individuals with high body mass index (BMI). Modified anthropometric dimensions were also developed to address the challenges of obtaining accurate and repeatable data for this population segment. Background: The prevalence of obesity affects approximately 40% of the U.S. adult population. Anthropometric data are needed to guide product design and safety, but few individuals with high BMI have been measured in available datasets. Method: Anthropometric data for a convenience sample of 288 adults with high BMI (≥30 kg/m²) were collected. To increase participation and minimize participant discomfort, measurements were collected at three bariatric weight loss clinics and one academic research institution. Results: The current obese cohort are heavier than the U.S. general population, with a difference in mean body weight of 47 kg for women and 56 kg for men. The obese cohort are also heavier and have a higher BMI compared with the NIOSH (National Institute for Occupational Safety and Health) truck driver population. Waist circumferential measures of the current obese cohort were larger than women or men in either population compared, a result indicative of meaningful body shape differences. Conclusion: To our knowledge, this study is the first to collect anthropometric data for the obese population segment and conduct comparisons to the U.S. general population and available occupational databases. The obese cohort differed substantially with respect to the distributions of anthropometric variables. Application: These data provide insights about the obese population segment that are relevant to product design, and establish a foundation for future data collection efforts.