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In the mid 1970s, UMTRI investigated the biomechanical properties of the head and neck using 180 “normal” adult subjects selected to fill eighteen subject groups based on age (young, mid-aged, older), gender, and stature (short, medium, and tall by gender). Lateral-view radiographs of the subjects’ cervical spines and heads were taken with the subjects seated in a simulated automotive neutral posture, as well as with their necks in full-voluntary flexion and full-voluntary extension. Although the cervical spine and lower head geometry were previously measured manually and documented, new technologies have enabled computer digitization of the scanned x-ray images and a more comprehensive and detailed analysis of the variation in cervical spine and lower head geometry with subject age, stature, and gender. After scanning the radiographic images, 108 skeletal landmarks on the cervical vertebrae and 10 head landmarks were digitized.

Most human figure models used in ergonomic analyses present postural comfort ratings based on joint angles, and present a single comfort score for the whole body or on a joint-by-joint basis. The source data for these ratings is generally derived from laboratory studies that link posture to ratings. Lacking in many of these models is a thorough treatment of the distribution of ratings for the population of users. Information about ratings distributions is necessary to make cost-effective tradeoffs when design changes affect subjective responses. This paper presents experimental and analytic methods used to develop distribution models for incorporating subjective rating data in ergonomic assessments.

Recent research in the ASPECT (Automotive Seat and Package Evaluation and Comparison Tools) program has led to the development of a new method for automobile driver posture prediction, known as the Cascade Model. The Cascade Model uses a sequential series of regression functions and inverse kinematics to predict automobile occupant posture. This paper presents an alternative method for driver posture prediction using data-guided kinematic optimization. The within-subject conditional distributions of joint angles are used to infer the internal cost functions that guide tradeoffs between joints in adapting to different vehicle configurations. The predictions from the two models are compared to in-vehicle driving postures.

A new method of predicting automobile occupant posture is presented. The Cascade Prediction Model approach combines multiple independent predictions of key postural degrees of freedom with inverse kinematics guided by data-based heuristics. The new model, based on posture data collected in laboratory mockups and validated using data from actual vehicles, produces accurate posture predictions for a wide range of passenger car interior geometries. Inputs to the model include vehicle package dimensions, seat characteristics, and occupant anthropometry. The Cascade Prediction Model was developed to provide accurate posture prediction for use with any human CAD model, and is applicable to many vehicle design and safety assessment applications.

The ASPECT program was conducted to develop new Automotive Seat and Package Evaluation and Comparison Tools. This paper presents a summary of the objectives, methods, and results of the program. The primary goal of ASPECT was to create a new generation of the SAE J826 H-point machine. The new ASPECT manikin has an articulated torso linkage, revised seat contact contours, a new weighting scheme, and a simpler, more user-friendly installation procedure. The ASPECT manikin simultaneously measures the H-point location, seat cushion angle, seatback angle, and lumbar support prominence of a seat, and can be used to make measures of seat stiffness. In addition to the physical manikin, the ASPECT program developed new tools for computer-aided design (CAD) of vehicle interiors. The postures and positions of hundreds of vehicle occupants with a wide range of body size were measured in many different vehicle conditions.

Current ATD positioning practices depend on seat track position, seat track travel range, and design seatback angle to determine appropriate occupant position and orientation for impact testing. In a series of studies conducted at the University of Michigan Transportation Research Institute, driver posture and position data were collected in forty-four vehicles. The seat track reference points currently used to position ATDs (front, center, and rear of the track) were found to be poor predictors of the average seat positions selected by small female, midsize male, and large male drivers. Driver-selected seatback angle was not closely related to design seatback angle, the measure currently used to orient the ATD torso. A new ATD Positioning Model was developed that more accurately represents the seated posture and position of drivers who match the ATD statures.

SAE Recommended Practice J941 describes the eyellipse, a statistical representation of driver eye locations, that is used to facilitate design decisions regarding vehicle interiors, including the display locations, mirror placement, and headspace requirements. Eye-position data collected recently at University of Michigan Transportation Research Institute (UMTRI) suggest that the SAE J941 practice could be improved. SAE J941 currently uses the SgRP location, seat-track travel (L23), and design seatback angle (L40) as inputs to the eyellipse model. However, UMTRI data show that the characteristics of empirical eyellipses can be predicted more accurately using seat height, steering-wheel position, and seat-track rise. A series of UMTRI studies collected eye-location data from groups of 50 to 120 drivers with statures spanning over 97 percent of the U.S. population. Data were collected in thirty-three vehicles that represent a wide range of vehicle geometry.