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New Capabilities for Vision-Based Posture Prediction
Book chapter

New Capabilities for Vision-Based Posture Prediction

L Knake, A Mathai, Timothy Marler, K Farrell, R Johnson and K Abdel-Malek
Advances in Applied Digital Human Modeling, pp.640-650
CRC Press
2011
DOI: 10.1201/EBK1439835111-67

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Abstract

Although ficld of view (FOV) is a commonly used evaluation parameter with digital human models, minimal research has involved modeling how eye motion (rclative to the head and body) affects the FOV and posture of a digital human striving to see a particular target. Few models incorporate independent eye movement and the effects of obstacles, with the ability to predict human posture realistically. This work presents two new and critical components for simulating how vision affccts human posture: 1) inclusion of eye movement and 2) visual obstacle avoidance. This work is conducted using Santos™, a real-time predictive physics-based virtual human with a high number of degrees-of-freedom. With optimization-based posture prediction, joint angles serve as design variables used to minimize various human performance measures that provide objective functions, subject to constraints that represent biomechanical limitations and task characteristics. Vision-based objective functions and constraints are developed and easily implemented in order to accurately predict postures. First, two new degrees of freedom wcre added to the Santos™ model, representing vertical and horizontal movement of the eyes. Then, functions for eye movement relative to the head and body were developed based on experimental data. The new vision-based objective function expanded on the current vision model by incorporating these new functions. Additionally, a vision-based obstacle avoidance constraint was added in order to predict postures that incorporate the tendency to look around obstacles that may be in one's line of site. Although vision alone does not govern one's posture, when combined with other performance measures, more realistic predicted postures incorporating vision were obtained. Initial subjective validation suggests the predicted postures are accurate and realistic. The consequent capabilities have proven extremely useful for ergonomic studies and analyses of automotive cab scenarios.

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