APPLYING NEURAL NETWORKS AND OPTIMIZATION TECHNIQUES TO THE SIMULATION OF HUMAN MOTION

摘要：

Human motion synthesis has been an important multidisciplinary research area across computer animation, biomechanics, orthopedics, rehabilitation, bioengineering, ergonomics, etc. The human motion of daily activity is a complex task of movement coordination involving many body parts and joints. Even to generate the simple motion such as raising the arm or getting up from the chair requires complex modeling and computation. Perceptron types of neural networks were used in this study to generalize the movement coordination of a daily human activity, the lifting motion. With only a limited amount of training data, reasonable patterns of the motion could be achieved by the network. The motion patterns were then fitted with Hermite polynomials and initiated in an optimization model to predict the entire motion trajectories. The paper presents the method combining Neural networks and optimization for the generation of human motion.

关键词： Human motion synthesis Neural networks Optimization

作者: CHIUHSIANG JOE LIN WEI-JUNG SHIANG HSIN RAU JAMES C.CHEN

作者单位: Department of Industrial Engineering, Chung Yuan Christian University, 200 Chung Pei Rd., Chung-Li, Department of Industrial Engineering,Chung Yuan Christian University, 200 Chung Pei Rd., Chung-Li, T

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 3194-3198

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）