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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.

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.

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.

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.

This paper describes the design and development of a family of surrogate child restraints that are intended for use in developing and testing occupant sensing and classification systems. Detailed measurements were made of the geometry and mass distribution characteristics of 34 commercial child restraints, including infant restraints, convertibles, combination restraints, and boosters. The restraints were installed in three test seats with appropriately sized crash dummies to obtain data on seat-surface pressure patterns and the position and orientation of the restraint with belt loading. The data were used to construct two surrogates with removable components. The convertible surrogate can be used to represent a rear-facing infant restraint with or without a base, a rear-facing convertible, or a forward-facing convertible. The booster surrogate can represent a high-back belt-positioning booster, a backless booster, or a forward-facing-only restraint with a five-point harness.

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.

Reliable, accurate data on vehicle occupant characteristics could be used to personalize the occupant experience, potentially improving both satisfaction and safety. Recent improvements in 3D camera technology and increased use of cameras in vehicles offer the capability to effectively capture data on vehicle occupant characteristics, including size, shape, posture, and position. In previous work, the body dimensions of standing individuals were reliably estimated by fitting a statistical body shape model (SBSM) to data from a consumer-grade depth camera (Microsoft Kinect). In the current study, the methodology was extended to consider seated vehicle occupants. The SBSM used in this work was developed using laser scan data gathered from 147 children with stature ranging from 100 to 160 cm and BMI from 12 to 27 kg/m2 in various sitting postures.

Digital human models (DHM) are now widely used to assess worker tasks as part of manufacturing simulation. With current DHM software, the simulation engineer or ergonomist usually makes a manual estimate of the likely worker standing location with respect to the work task. In a small number of cases, the worker standing location is determined through physical testing with one or a few workers. Motion capture technology is sometimes used to aid in quantitative analysis of the resulting posture. Previous research has demonstrated the sensitivity of work task assessment using DHM to the accuracy of the posture prediction. This paper expands on that work by demonstrating the need for a method and model to accurately predict worker standing location. The effect of standing location on work task posture and the resulting assessment is documented through three case studies using the Siemens Jack DHM software.

The potential of digital human modeling to improve the design of products and workspaces has been limited by the time-consuming manual manipulation of figures that is required to perform simulations. Moreover, the inaccuracies in posture and motion that result from manual procedures compromise the fidelity of the resulting analyses. This paper presents a new approach to the control of human figure models and the analysis of simulated tasks. The new methods are embodied in an algorithmic framework developed in the Human Motion Simulation (HUMOSIM) laboratory at the University of Michigan. The framework consists of an interconnected, hierarchical set of posture and motion modules that control aspects of human behavior, such as gaze or upper-extremity motion. Analysis modules, addressing issues such as shoulder stress and balance, are integrated into the framework.

Cyclical stepping (gait) has been studied extensively. Some of these results are reflected in the straight and curved path step-following algorithms in commercial digital human modeling (DHM) implementations. With the aid of these algorithms, DHM users define start, intermediate, and end path points and the software generates a walking-like motion along the path. Most of these algorithms have substantial limitations, among them that the figures exhibit “foot skate,” meaning that the kinematic constraint of foot contact with the ground is not respected. Turning is accomplished by pivoting the entire figure, rather than through realistic lower-extremity motions. The simulation of the non-cyclical stepping motions accompanying manual material handling pickup and delivery tasks requires manual manikin manipulation. This paper proposes a paradigm for the simulation of stepping behavior in digital human models based on a model of foot placements and motions.

Occupant packaging practice relies on statistical models codified in SAE practices, such as the SAE J941 eyellipse, and virtual human figure models representing individual occupants. The current packaging approach provides good solutions when the problem is relatively unconstrained, but achieving good results when many constraints are active, such as restricted headroom and sightlines, requires a more rigorous approach. Modeling driver needs using continuous models that retain the residual variance associated with performance and preference allows use of optimization methodologies developed for robust design. Together, these models and methods facilitate the consideration of multiple factors simultaneously and tradeoff studies can be performed. A case study involving the layout of the interior of a passenger car is presented, focusing on simultaneous placement of the seat and steering wheel adjustment ranges.

For many industrial tasks (push, pull, lift, carry, etc.), restrictions on grip locations and visibility constrain the hand and head positions and help to define feasible postures. In contrast, foot locations are often minimally constrained and an ergonomics analyst can choose several different stances in selecting a posture to analyze. Also, because stance can be a critical determinant of a biomechanical assessment of the work posture, the lack of a valid method for placing the feet of a manikin with respect to the task compromises the accuracy of the analysis. To address this issue, foot locations and orientations were captured in a laboratory study of sagittal plane and asymmetric manual load transfers. A pilot study with four volunteers of varying anthropometry approached a load located on one of three shelves and transferred the load to one of six shelves.

This panel provided a forum for discussion of future research and development desired by users and potential users of DHM technologies. The discussion was based on the experiences of users from various sectors and industries. Panelists provided written statements and delivered short presentations prior to opening the session to audience discussion. The panel was designed to inform and drive research and development plans to fill these needs.

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.

Two new techniques for investigating the thermal skin-burn potential of airbags are presented. A reduced-volume airbag test procedure has been developed to obtain airbag pressures that are representative of a dynamic ridedown event during a static deployment. Temperature and heat flux measurements made with this procedure can be used to predict airbag thermal burn potential. Measurements from the reduced-volume procedure are complemented by data obtained using two gas-jet simulators, called heatguns. Gas is vented in controlled bursts from a large, heated, pressurized tank of gas onto a target surface. Heat flux measurements on the target surface have been used to develop quantitative models of the relationships between gas jet characteristics and burn potential.

Lateral impact tests were performed using seven male post-mortem human subjects (PMHS) to characterize the force-deflection response of contacted body regions, including the lower abdomen. All tests were performed using a dual-sled, side-impact test facility. A segmented impactor was mounted on a sled that was pneumatically accelerated into a second, initially stationary sled on which a subject was seated facing perpendicular to the direction of impact. Positions of impactor segments were adjusted for each subject so that forces applied to different anatomic regions, including thorax, abdomen, greater trochanter, iliac wing, and thigh, could be independently measured on each PMHS. The impactor contact surfaces were located in the same vertical plane, except that the abdomen plate was offset 5.1 cm towards the subject.

Eight whole fresh-frozen cadavers (6 female, 2 male) that were elderly and/or female were laterally impacted using UMTRI's dual-sled side-impact test facility. Cadavers were not excluded on the basis of old age or bone diseases that affect tolerance. A thinly padded, multi-segment impactor was used that independently measured force histories applied to the shoulder, thorax, abdomen, greater trochanter, iliac wing, and femur of each PMHS. Impactor plates were adjusted vertically and laterally toward the subject so that contact with body regions occurred simultaneously and so that each segment contacted the same region on every subject. This configuration minimized the effects of body shape on load sharing between regions. Prior to all tests, cadavers were CT scanned to check for pre-existing skeletal injuries. Cadavers were excluded if they had pre-existing rib fractures or had undergone CPR.

In recent investigations of airbag deployments, drivers h v c reported abrasions to the face, neck, and forearms due to deploying airbags, A study of the airbag design and deployments parameters affecting the incidence and severity of abrasions caused by driver-side airbags has led to the development of a laboratory test procedure to evaluate the potential of an airbag design m cause skin injury This report describes the procedure, which is based an static deployments of airbags into a cylindrical lest fixture. The target area is covered with a material that responds to abrasion-producing events in a manner related to human skin tolerance. Test results show excellent correlation with abrasion injuries produced by airbag deployments into the skin of human volunteers.