Recursive Models of Human Motion

If we hope to build computers that help people, we must build computers that are able to understand people. One step is the ability to understand human activity. Human motion is a very complex phenomenon, but it is not entirely arbitrary. The physical limitations of the body, the patterns encoded into our physiological structures, and even the habits of motion that we acquire over time, all combine to provide strong constraints on how we move. Modeling these constraints is an important step toward eventual understanding.

Perception for Human Motion Understanding

Christopher R. Wren. Invited chapter in Innovations in Machine Intelligence and Robot Perception, Springer, due out July 2005. (MERL link)

Understanding Expressive Action

Christopher R. Wren. MIT EECS Ph.D. Thesis, March 2000. (MIT Libraries link)

Understanding Purposeful Human Motion

Christopher R. Wren, Brian P. Clarkson, and Alex P. Pentland. Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, March 26-30, 2000. (IEEE link)

Dynaman: Recursive Modeling of Human Motion

Christopher R. Wren and Alex P. Pentland. Image and Vision Computing

Dynamic Modeling of Human Motion

Christopher R. Wren and Alex P. Pentland. Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, Nara, Japan, April 14-16, 1998

This technology is built on the SmartDesk framework