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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.

In many task analyses using digital human figure models, only the terminal or apparently most stressful posture is analyzed. For reaches from a seated position, this is generally the posture with the hand or hands at the target. However, depending on the characteristics of the tasks and the people performing them, analyzing only the terminal posture could be misleading. This possibility was examined using data from a study of the reaching behavior of people with spinal cord injury. Participants performed two-handed forward reaching tasks. These reaches were to three targets located in the sagittal plane. The terminal postures did not differ significantly between those with spinal cord injury and those without. However, motion analysis demonstrated that they employed distinct strategies, particularly in the initial phase of motion.

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.

For human motion studies, which are to be used for either dynamic biomechanical analyses or development of human motion simulation models, it is important to establish an empirical motion database derived from efficient measurement and well-standardized data processing methodologies. This paper describes the motion recording and data processing system developed for modeling seated reach motions at the University of Michigan's HUMOSIM Laboratory. Both electromagnetic (Flock of Birds) and optical (Qualysis) motion capture systems are being used simultaneously to record the motion data. Using both types of devices provides a robust means to record human motion, but each has different limitations and advantages. The amount of kinematic information (DOFs), external sources of noise, shadowing, off-line marker identification/tracking time, and setup cost are key differences.

Modeling normal human reach behavior is dependent on many factors. Anthropometry, age, gender, joint mobility and muscle strength are a few such factors related to the individual being modeled. Reach locations, seat configurations, and tool weights are a few other task factors that can affect dynamic reach postures. This paper describes how two different modeling approaches are being used in the University of Michigan Human Motion Simulation Laboratory to predict normal seated reaching motions. One type of model uses an inverse kinematic structure with an optimization procedure that minimizes the weighted sum of the instantaneous velocity of each body segment. The second model employs a new functional regression technique to fit polynomial equations to the angular displacements of each body segment. To develop and validate these models, 38 subjects of widely varying age and anthropometry were asked to perform reaching motions while seated in simulated vehicle or industrial workplace.

A research effort was initiated to establish an empirical data base and to develop predictive models of normal human in-vehicle seated reaching motions while driving. A driving simulator was built, in which a variety of targets were positioned at typical locations a driver would possibly reach. Reaching motions towards these targets were performed by demographically representative subjects and measured by a state-of-the-art motion analysis system. This paper describes the experiment conducted to collect the movement data, and the new techniques that are being developed to process, analyze, and model the data. Some initial findings regarding the role of torso assistive motion, the effect of speed used in completing a motion on multi-segment dynamic postures, and illustrative results from kinematic modeling are presented.

Properties of the human neck which may influence a person's susceptibility to “whiplash” injury during lateral impact have been studied in 96 normal subjects. Subjects were chosen on the basis of age, sex, and stature and data were grouped into six primary categories based on sex (F, M) and age (18-24, 35-44, 62-74). The data include: measures of head, neck and body anthropometry in standing and simulated automotive seating positions, three-dimensional range of motion of the head and neck, head/neck response to low-level acceleration, and both stretch reflex time and voluntary isometric muscle force in the lateral direction. Reflex times are found to vary from about 30 to 70 ms with young and middle aged persons having faster times than older persons, and females having faster times than males. Muscle strength decreases with age and males are, on the average, stronger than females.