Synthesizing and Modeling Human Locomotion Using System Identification

Synthesizing human motion signals is difficult because of its multi-dimensional and nonlinear nature. However, the locomotion is a synchronized motion, which means that these signals are related. Using this property, we propose a new method for modeling the human locomotion and identifying this model. The input signals of our model is the trajectory of pelvis and the outputs are corresponding motion signals of whole human body. To identify this system, we considered it as black-box, for which we propose an adapted method of identification using motion capture. We show that the identified model allows to generate various human locomotion in a fast and efficient way