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

In real human crowds, diversification is caused by many factors such as gender, age, skin color, clothing, walking style, psychology etc., yet most crowd simulation systems rely only on appearance related variables for diversification. This research explores improving crowd diversity by simulating the biomechanical effects of aging in humans within crowd characters. Simulated characters were reprogrammed to be able to predict their behavior through an aging parameter predictor using support vector regression respective to their age. Data from gerontology studies recording spinal curvature and walking pattern deterioration with age were used for training. A spatial diversity algorithm was designed and implemented to distribute the resulting variants evenly among each other. Evaluations were conducted to gauge the impact on the diversification of the crowd simulation. The results prove that simulating posture and walking pattern deterioration in older characters does significantly improve biomechanical diversity within a virtual human crowd.