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Human body size, shape, stature, joint range of motion, joint strength, and other factors vary from one person to another. Even for a single person, anthropometric data, such as weights and joint strengths, change with time. Due to this variability, different people adapt different postures to perform the same reach task within a vehicle. Even for the same person and reach task, postures will vary with time. Therefore, it is important to consider the reliability of achieving a reach task within a vehicle to create a better design for vehicle controls, enhance driver safety, and increase the level of accommodation for all types of drivers. In this study, we will present a reliability/probability approach to gain insights into driver reach tasks with uncertainty. Sensitivity levels are found to determine the importance of each joint to the reach tasks. A digital human upper body model with 21 degrees of freedom (DOFs) is introduced to demonstrate the probability approach.

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.