Simulating Gait and Postural Effects of Aging for Improved Diversity in Virtual Crowds

Abstract

Autonomous character crowds have been effectively deployed in a variety of application domains including video games, films, psychology and operational research. Diversification of crowd characters has been proved to be a great contributor to realism. Diversification of individuals within real human crowds can be attributed to many factors such as gender, age, skin color, clothing, walking style, level of aggression etc., while most crowd simulation systems prefer to use clothing, skin color and other appearance related variables to make characters seem less similar to each other. This research aims to explore the possibility of increasing crowd diversity by simulating the biomechanical effects of aging in real humans within characters. Support vector regression has been used to train an aging parameter predictor on data from gerontology research recording spinal curvature and walking pattern deterioration caused by progression of age. The simulated characters have been reprogrammed to be able to predict their behavior through this predictor and reflect the results within the simulation. A spatial diversity algorithm has been proposed and implemented to distribute the resulting variants evenly among each other. Finally, three user evaluation experiments have been conducted to gauge the perceptibility and accuracy of simulating these biomechanical changes, to users and to evaluate the impact caused by biomechanical variety on the diversification of the crowd simulation as a whole. The results of these experiments prove that simulating posture and walking pattern deterioration in older characters does have a significant improvement when simulating old characters and that having this biomechanical diversity within a virtual human crowd does provide a considerable improvement in crowd diversity.