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Seated posture inside a vehicle influences driver performance and control of a vehicle. Many vehicles do not properly allow for a natural seated posture for all drivers. Some vehicles are difficult to drive due to the fact that the driver is inadequately accommodated in the driver seat. For people of extreme stature, tall or short, and for people of extreme width, obese or pregnant populations, it can be difficult to safely operate a vehicle if there is not enough room in the cab or if some controls cannot be reached. This paper employs digital human models to study the effect of obesity on seated posture inside a vehicle. Eight digital human models, four non-obese and four obese, are subjected to reach tests inside a virtual vehicle cab. These tests are used to determine how obesity affects the clearance between the steering wheel and driver body and whether additional factors contribute to discomfort associated with obese people seated inside a vehicle.

Digital human modeling and simulation allows a designer to test a product early in the design process. Accounting for variability in the human population which the product is intended for is difficult without developing physical prototypes and conducting population testing. Digital human modeling allows a designer to test a product without a physical prototype in a simulated environment using digital humans. Using digital humans, or manikins, of various sizes, a designer can test for variability in the human population before any physical prototype is needed. This paper proposes an optimization-based approach to determine the seat adjustment range in the interior cab design of a vehicle. Previous methods of cab design include population sampling and stochastic posture prediction. This paper places boundary anthropometric digital human models, a 95% male and a 5% female, in a 3D test environment.

Digital human modeling and posture prediction can only be used as a design tool if the predicted postures are realistic. To date, the most realistic postures have been realized by simultaneously optimizing human performance measures (HPMs). These HPMs currently consist of joint discomfort, delta potential energy, and visual displacement. However these HPMs only consider the kinematics of human posture. Dynamic aspects of human posture such as external loads and mass of limbs have not yet been considered in conjunction with the current HPMs. This paper gives the formulation for a new human performance measure combination including the use of joint torque to account for the dynamics of human posture. Postures are then predicted using multi-objective optimization (MOO) techniques to optimize the combination of the new HPM and the current. The predicted postures are then compared with the benchmark postures which are those obtained from using the current HPMs only.

As the need for more advanced human modeling tools has grown, so has the focus on research and development with posture-prediction capabilities for the design and analysis of products, and for the study of human behavior. Virtual humans have grown from digital mannequins with limited fidelity, to realistic avatars with predictive capabilities. Now, one of the frontiers with posture prediction is the incorporation of external loads and joint torques. Although advancements have been made with dynamic motion prediction, relatively little work has been conducted with external load-based posture prediction. Drawing on past success with optimization-based kinematic posture prediction implemented with the virtual human Santos™, we present a new method for considering external loads. A pilot study is conducted whereby equations for static equilibrium are incorporated in the optimization formulation.