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Recent field data have shown that the occupant protection in vehicle rear seats failed to keep pace with advances in the front seats likely due to the lack of advanced safety technologies. The objective of this study was to optimize advanced restraint systems for protecting rear seat occupants with a range of body sizes under different frontal crash pulses. Three series of sled tests (baseline tests, advanced restraint trial tests, and final tests), MADYMO model validations against a subset of the sled tests, and design optimizations using the validated models were conducted to investigate rear seat occupant protection with 4 Anthropomorphic Test Devices (ATDs) and 2 crash pulses.

This paper describes the development of the fixed seat eyellipse in the October 2008 revision of SAE Recommended Practice J941. The eye locations of 23 men and women with a wide range of stature were recorded as they sat in each of three second-row bench seats in a laboratory mockup. Testing was conducted at 19-, 23-, and 27-degree seat back angles. Regression analysis demonstrated that passenger eye location was significantly affected by stature and by seat back angle. The regression results were used to develop an elliptical approximation of the distribution of adult passenger eye locations, applying a methodology previously used to develop the driver eyellipse in SAE J941-2002.

Seat comfort is driven in part by the fit between the sitter and seat. Traditional anthropometric data provide little information about the size and shape of the torso that can be used for backrest design. This study introduces a methodology for using three-dimensional computer models of the human torso based on a statistical analysis of body shapes for conducting automated fit assessments. Surface scan data from 296 men and 417 women in a seated posture were analyzed to create a body shape model that can be adjusted to a range of statures, body shape, and postures spanning those typical of vehicle occupants. Finite-element models of two auto seat surface were created, along with custom software that generates body models and postures them in the seat. A simple simulation technique was developed to rapidly assess the fit of the torso relative to the seat back.

A study of automotive seating comfort and related design factors was conducted, utilizing subjective techniques of seat comfort assessment and objective measures of the seat/subject interaction. Eight male subjects evaluated four different test seats during a short-term seating session and throughout a three-hour driving simulation. For the latter, subjects operated a static laboratory driving simulator, performing body-area discomfort evaluations at thirty-minute intervals. Cross-modality matching (CMM), a subjective assessment technique in which a stimulus is rated by matching to the level of another stimulus, was used during the long-term driving simulation to evaluate discomfort. Subject posture, muscle activity in the lower back and abdomen, and pressure levels at key support locations on the seat were monitored. In addition, a sonic digitizing system was used to record seat indentation contours and to characterize the subjects' spinal contours.

The fore-aft location of the steering wheel relative to the pedals is a critical determinant of driving posture and comfort. Current SAE practices lack quantitative guidance on steering wheel positioning. This paper presents a model of subjective preference for fore-aft steering wheel position across a range of seat heights. Sixty-eight men and women evaluated the steering wheel positions in a total of 9 package conditions differentiated by seat height and fore-aft steering wheel position. Numerical responses were given on a 7-point scale anchored with the words “Too Close”, “Just Right”, and “Too Far”. A statistical analysis of the results demonstrated that the preferred fore-aft steering wheel position was affected by seat height and driver stature. An ordinal logistic regression model was created that predicts the distribution of subjective responses to steering wheel location. The model can be used to calculate the preferred steering wheel position for individuals or populations.

Seat belt anchorage locations have a strong effect on occupant protection. Federal Motor Vehicle Safety Standard (FMVSS) 210 specifies requirements for the layout of the anchorages relative to the seating reference point and seat back angle established by the SAE J826 H-point manikin. Sled testing and computational simulation has established that belt anchorage locations have a strong effect on occupant kinematics, particularly for child occupants using the belt as their primary restraint. As part of a larger study of vehicle geometry, the locations of the anchorage points in the second-row, outboard seating positions of 83 passenger cars and light trucks with a median model year of 2005 were measured. The lower anchorage locations spanned the entire range of lap belt angles permissible under FMVSS 210 and the upper anchorages (D-ring locations) were distributed widely as well.

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.

Most current applications of digital human figure models for ergonomic assessments of manual tasks focus on the analysis of a static posture. Tools available for static analysis include joint-specific strength, calculation of joint moments, balance maintenance capability, and low-back compression or shear force estimates. Yet, for many tasks, the inertial loads due to acceleration of body segments or external objects may contribute significantly to internal body forces and tissue stresses. Due to the complexity of incorporating the dynamics of motion into analysis, most commercial software packages used for ergonomic assessment do not have the capacity to include dynamic effects. Thus, commercial human modeling packages rarely provide an opportunity for the user to determine if a static analysis is sufficient.

Standing reach envelopes are important tools for the design of industrial and vehicle environments. Previous work in this area has focussed on manikin-based (where a few manikins are used to simulate individuals reaching within the region of interest) and population-based (where data are gathered on many individuals reaching in a constrained environment) approaches. Each of these methods has merits and shortfalls. The current work bridges the manikin- and population-based approaches to assessing reach by creating population models using kinematic simulation techniques driven by anthropometric data. The approach takes into account body dimensions, balance, and postural cost to create continuous models that can be used to assess designs with respect to both maximal and submaximal reaches. Cost is quantified as the degree to which the torso is involved in the reach, since the inclination of the torso is a good measure of lower-back load and may be related to subjective reach difficulty.

Industrial tasks performed by standing workers are among those most commonly simulated using digital human models. Workers often walk, turn, and take acyclic steps as they perform these tasks. Current h uman modeling tools lack the capability to simulate these whole body motions accurately. Most models simulate walking by replaying joint angle trajectories corresponding to a general gait pattern. Turning is simulated poorly if at all, and violations of kinematic constraints between the feet and ground are common. Moreover, current models do not accurately predict foot placement with respect to loads and other hand targets, diminishing the utility of the associated ergonomic analyses. A new approach to simulating stepping and walking in task-oriented activities is proposed. Foot placements and motions are predicted from operator and task characteristics using empirical models derived from laboratory data and validated using field data from an auto assembly plant.

Accurate representation of working postures is critical for ergonomic assessments with digital human models because posture has a dominant effect on analysis outcomes. Most current digital human modeling tools require manual manipulation of the digital human to simulate force-exertion postures or rely on optimization procedures that have not been validated. Automated posture prediction based on human data would improve the accuracy and repeatability of analyses. The effects of hand force location, magnitude, and direction on whole-body posture for standing tasks were quantified in a motion-capture study of 20 men and women with widely varying body size. A statistical analysis demonstrated that postural variables critical for the assessment of body loads can be predicted from the characteristics of the worker and task.

Digital human modeling has traditionally focused on the physical aspects of humans and the environments in which they operate. As the field moves towards modeling dynamic and more complex tasks, cognitive and perceptual aspects of the human's performance need to be considered. Cognitive modeling of complex tasks such as driving has commonly avoided the complexity of physical simulation of the human, distilling motor performance to motion execution times. To create a more powerful and flexible approach to the modeling of human/machine interaction, we have integrated a physical architecture of human motion (the Human Motion Simulation Ergonomics Framework—HUMOSIM) with a computational cognitive architecture (the Queueing network model human processor—QN–MHP). The new system combines the features of the two separate architectures and provides new capabilities that emerge from their integration.

The ease of getting into and out of passenger cars and light trucks is a critical component of customer acceptance and product differentiation. In commercial vehicles, the health and safety of drivers is affected by the design of the steps and handholds they use to get into and out of the cab. Ingress/egress assessment appears to represent a substantial application opportunity for digital human models. The complexity of the design space and the range of possible biomechanical and subjective measures of interest mean that developing useful empirical models is difficult, requiring large-scale subject testing with physical mockups. Yet, ingress and egress motions are complex and strongly affected by the geometric constraints and driver attributes, posing substantial challenges in creating meaningful simulations using figure models.

The Human Motion Simulation Framework (Framework) is a hierarchical set of algorithms for predicting and analyzing task-oriented human motion. The Framework was developed to improve the performance of commercial human modeling software by increasing the accuracy of predicted motions and the speed of generating simulations. This paper presents the addition of stair ascending and descending to the Transition Stepping and Timing (Transit) model, a component of the Framework that predicts gait and acyclic stepping.

The Human Motion Simulation Framework is a hierarchical set of algorithms for physical task simulation and analysis. The Framework is capable of simulating a wide range of tasks, including standing and seated reaches, walking and carrying objects, and vehicle ingress and egress. In this paper, model predictions for the terminal postures of standing object transfer tasks are compared to data from 20 subjects with a wide range of body dimensions. Whole body postures were recorded using optical motion capture for one-handed and two-handed object transfers to target destinations at three angles from straight ahead and three heights. The hand and foot locations from the data were input to the HUMOSIM Framework Reference Implementation (HFRI) in the Jack human modeling software. The whole-body postures predicted by the HFRI were compared to the measured postures using a set of measures selected for their importance to ergonomic analysis.

Efficient methods for simulating operators performing part handling tasks in manufacturing plants are needed. The simulation of part handling motions is an important step towards the implementation of virtual manufacturing for the purpose of improving worker productivity and reducing injuries in the workplace. However, industrial assembly tasks are often complex and involve multiple interactions between workers and their environment. The purpose of this paper is to present a series of industrial simulations using the Human Motion Simulation Framework developed at the University of Michigan. Three automotive assembly operations spanning scenarios, such as small and large parts, tool use, walking, re-grasping, reaching inside a vehicle, etc. were selected.

Assessments of reach capability using human figure models are commonly performed by exercising each joint of a kinematic chain, terminating in the hand, through the associated ranges of motion. The result is a reach envelope determined entirely by the segment lengths, joint degrees of freedom, and joint ranges of motion. In this paper, the validity of this approach is assessed by comparing the reach envelopes obtained by this method to those obtained in a laboratory study of men and women. Figures were created in the Jack human modeling software to represent the kinematic linkages of participants in the laboratory study. Maximum reach was predicted using the software's kinematic reach-envelope generation methods and by interactive manipulation. Predictions were compared to maximum reach envelopes obtained experimentally. The findings indicate that several changes to the normal procedures for obtaining maximum reach envelopes for seated tasks are needed.

Workstation design is one of the most essential components of proactive ergonomics, and digital human models have gained increasing popularity in the analysis and design of current and future workstations (Chaffin 2001). Using digital human technology, it is possible to simulate interactions between humans and current or planned workstations, and conduct quantitative ergonomic analyses based on realistic human postures and motions. Motion capture has served as the primary means by which to acquire and visualize human motions in a digital environment. However, motion capture only provides motions for a specific person performing specific tasks. Albeit useful, at best this allows for the analysis of current or mocked-up workstations only. The ability to subsequently modify these motions is required to efficiently evaluate alternative design possibilities and thus improve design layouts.

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.

Effective application of human figure models to truck interior design requires accurate data on the postures and positions of truck drivers. Errors in positioning of figure models propagate to errors in reach, visibility, and other analyses. This paper describes methods used in a recent study to measure in-vehicle driving postures in Class 6, 7, and 8 trucks. A three-dimensional coordinate measurement machine was used to measure body landmark locations after a driver completed a short road course. The data were used to validate posture-prediction models developed in a previous laboratory study. Vehicle calibration, driver selection, and testing methods are reviewed.