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The factors that influence airbag-induced upper-extremity injuries sustained by drivers were investigated in this study. Seven unembalmed human cadavers were used in nineteen direct-forearm-interaction static deployments. A single horizontal-tear-seam airbag module and two different inflators were used. Spacing between the instrumented forearm and the airbag module was varied from 10 cm to direct contact in some tests. Forearm-bone instrumentation included triaxial accelerometry, crack detection gages, and film targets. Internal airbag pressure was also measured. The observed injuries were largely transverse, oblique, and wedge fractures of the ulna or radius, or both, similar to those reported in field investigations. Tears of the elbow joint capsule were also found, both with and without fracture of the forearm.

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.

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.

Four unembalmed human cadavers were used in eight direct-forearm-airbag-interaction static deployments to assess the relative aggressivity of two different airbag modules. Instrumentation of the forearm bones included triaxial accelerometry, crack detection gages, and film targets. The forearm-fracture predictors, peak and average distal forearm speed (PDFS and ADFS), were evaluated and compared to the incidence of transverse, oblique, and wedge fractures of the radius and ulna. Internal-airbag pressure and axial column loads were also measured. The results of this study support the use of PDFS or ADFS for the prediction of airbag-induced upper-extremity fractures. The results also suggest that there is no direct relationship between internal-airbag pressure and forearm fracture. The less-aggressive system (LAS) examined in this study produced half the number of forearm fracture as the more-aggressive system (MAS), yet exhibited a more aggressive internal-pressure performance.

Digital human modeling (DHM) progress worldwide will be much faster and cohesive if the diverse community now developing simulations has a global blueprint for DHM, and is able to work together efficiently. DHM developers and users can save time by building on each other's work. This paper highlights a panel discussion on DHM goals and strategic plans for the next decade to begin formulating the international blueprint. Four subjects are chosen as the starting points: (1) moving DHM into the public safety and internet arenas, (2) role of DHM in computer assisted surgery and automotive safety, (3) DHM in defense applications, and (4) DHM to improve workplace ergonomics.

Current anthropomorphic test device (ATD) positioning procedures for drivers and front-seat passengers place the crash dummy within the vehicle by reference to the seat track. Midsize-male ATDs are placed at the center of the fore-aft seat track adjustment range, while small-female and large-male ATDs are placed at the front and rear of the seat track, respectively. Research on occupant positioning at UMTRI led to the development of a new ATD positioning procedure that places the ATDs at positions more representative of the driving positions of people who match the ATD's body dimensions. This paper presents a revised version of the UMTRI ATD positioning procedure. The changes to the procedure improve the ease and repeatability of ATD positioning while preserving the accuracy of the resulting ATD positions with respect to the driving positions of people matching the ATD anthropometry.

This study begins the exploration of the relationship between shoulder external moments and perceived exertion levels for submaximal delivery tasks. Twenty subjects were recorded while performing hand load movement tasks to specified targets. After each exertion, subjects were asked to rate the effort required to perform the task. The recorded motion profiles were processed using a biomechanical upper extremity model, from which resultant external shoulder moments were calculated. Average resultant shoulder moments, stratified by exertion level, were also calculated. Several individual subject moment/exertion profiles showed identifiable trends. It was demonstrated that while no strong relationships exist for individual task exertion effort prediction based on resultant shoulder moments, there is a general trend in the overall data sample, as is shown by a high correlation between mean integrated resultant shoulder moment by exertion level and exertion levels.

Simulation of human motions in virtual environments is an essential component of human CAD (Computer-aided Design) systems. In our earlier SAE papers, we introduced a novel motion simulation approach termed Memory-based Motion Simulation (MBMS). MBMS utilizes existing motion databases and predicts novel motions by modifying existing ‘root’ motions through the use of the motion modification algorithm. MBMS overcomes some limitations of existing motion simulation models, as 1) it simulates different types of motions on a single, unified framework, 2) it simulates motions based on alternative movement techniques, and 3) like real humans, it can learn new movement skills continually over time. The current study evaluates the prediction accuracy of MBMS to prove its utility as a predictive tool for computer-aided ergonomics. A total of 627 whole-body one-handed load transfer motions predicted by the algorithm are compared with actual human motions obtained in a motion capture experiment.

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.

Although biomechanics models can predict the stress on the musculoskeletal system, they cannot predict how the muscle load associated with exertion is perceived. The short-term goal of the present study was to model the perception of effort in lifting and reaching tasks. The long-term goal is to determine the correlation between objective and subjective measures of effort and use this information to predict fatigue or the risk of injury. Lifting and reaching tasks were performed in seated and standing situations. A cylindrical object and a box were moved with one hand and two hands, respectively, from a home location to shelves distributed in the space around the subject. The shoulder and torso effort required to perform these tasks were rated on a ten point visual analog scale.

Interference between physical objects in the workspace and the moving human body may cause serious problems, including errors in manual operation, physical damage and trauma from the collision, and increased biomechanical stresses due to movement reorganization for avoiding the obstacles. Therefore, a computer algorithm to detect possible collisions and simulate human motions to avoid obstacles will be an important tool for computer-aided ergonomics and optimization of system design in the early stage of a design process. In the present study, we present a method of modifying motions for obstacle avoidance when the object intrudes near the center of the planned motion. We take the motion modification approach, as we believe that for a certain class of obstacle avoidance problems, a person would modify a pre-planned motion that would result in a collision to a new one that is collision-free, as opposed to organizing a totally unique motion pattern.

Advanced airbag systems use a variety of sensors to classify vehicle occupants so that the airbag deployment can be modulated accordingly. One potential input to such systems is the distribution of pressure applied to the seat surface by the occupant. However, the development of such systems is hindered by the lack of suitable human surrogates. The OCATD program has developed two new surrogates for advanced airbag applications representing a small adult woman and a six-year-old child. This paper describes the development of performance specifications for the OCATDs based on a study of the seat surface pressure distributions produced by vehicle occupants. The pressure distributions of sixty-eight small women and children ranging in body weight between 23 and 48 kg were measured on four seats in up to twelve postures per seat. The data were analyzed to determine the parameters of the pressure distribution that best predict occupant body weight.

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.

A new approach to driver seat-position modeling is presented. The equations of the Seating Accommodation Model (SAM) separately predict parameters of the distributions of male and female fore/aft seat position in a given vehicle. These distributions are used together to predict specific percentiles of the combined male-and-female seat-position distribution. The effects of vehicle parameters-seat height, steering-wheel-to-accelerator pedal distance, seat-cushion angle, and transmission type-are reflected in the prediction of mean seat position. The mean and standard deviation of driver population stature are included in the prediction for the mean and standard deviation of the seat-position distribution, respectively. SAM represents a new, more flexible approach to predicting fore/aft seat-position distributions for any driver population in passenger vehicles. Model performance is good, even at percentiles in the tails of the distribution.

This paper describes and illustrates two different approaches used to predict population strength capabilities. One approach uses statistical regression models to predict maximum acceptable weight or force capabilities as a function of population attributes (e.g., anthropometry, gender and standardized strength scores). A second approach relies on a biomechanical concept stating that the maximum force one can produce in a given posture and for a given type of exertion is a function of the minimum relative strength produced by one's muscles at a particular joint. This latter approach has resulted in a specific static strength prediction model which has been implemented in software produced by the Center for Ergonomics at the University of Michigan. The software allows the simulation of a large variety of manual exertions. This paper describes the status of the two different approaches and implications and requirements for future human simulation and modeling systems.

Static deployments of driver-side airbags into the legs of human subjects were used to investigate the effects of inflator capacity, internal airbag tethering, airbag fabric, and the distance from the module on airbag-induced skin abrasion. Abrasion mechanisms were described by measurements of airbag fabric velocity and target surface pressure. Airbag fabric kinematics resulting in three distinct abrasion patterns were identified. For all cases, abrasions were found to be caused primarily by high-velocity fabric impactrather than scraping associated with lateral fabric motion. Use of higher-capacity inflators increased abrasion severity, and untethered airbags produced more severe abrasions than tethered airbags at distances greater than the length of the tether. Abrasion severity decreased as the distance increased from 225 to 450 mm. Use of a finer-weave airbag fabric in place of a coarser-weave fabric did not decrease the severity of abrasion.

Injuries to the thorax and abdomen comprise a significant percentage of all occupant injuries in motor vehicle accidents. While the percentage of internal chest injuries is reduced for restrained front-seat occupants in frontal crashes, serious skeletal chest injuries and abdominal injuries can still result from interaction with steering wheels and restraint systems. This paper describes the design and performance of prototype components for the chest, abdomen, spine, and shoulders of the Hybrid III dummy that are under development to improve the capability of the Hybrid III frontal crash dummy with regard to restraint-system interaction and injury-sensing capability.

Late model passenger cars and light trucks incorporate occupant protection systems with airbags and knee restraints. Knee restraints have been designed principally to meet the unbelted portions of FMVSS 208 that require femur load limits of 10-kN to be met in barrier crashes up to 30 mph, +/- 30 degrees utilizing the 50% male Anthropomorphic Test Device (ATD). In addition, knee restraints provide additional lower-torso restraint for belt-restrained occupants in higher-severity crashes. An analysis of frontal crashes in the University of Michigan Crash Injury Research and Engineering Network (UM CIREN) database was performed to determine the influence of vehicle, crash and occupant parameters on knee, thigh, and hip injuries. The data sample consists of drivers and right front passengers involved in frontal crashes who sustained significant injuries (Abbreviated Injury Scale [AIS] ≥ 3 or two or more AIS ≥ 2) to any body region.

The reach capability of drivers is currently represented in vehicle design practice in two ways. The SAE Recommended Practice J287 presents maximum reach capability surfaces for selected percentiles of a generic driving population. Driver reach is also simulated using digital human figure models. In typical applications, a family of figure models that span a large range of the target driver population with respect to body dimensions is positioned within a digital mockup of the driver's workstation. The articulated segments of the figure model are exercised to simulate reaching motions and driver capabilities are calculated from the constraints of the kinematic model. Both of these current methods for representing driver reach are substantially limited. The J287 surfaces are not configurable for population characteristics, do not provide the user with the ability to adjust accommodation percentiles, and do not provide any guidance on the difficulty of reaches that are attainable.