Human motion tracking for rehabilitation using depth images and particle filter optimization

The algorithm presented here allows the motion tracking of a human subject performing rehabilitation exercises during physiotherapy. The motion is tracked by fitting a model into the observed data, which are depth images coming from one or multiple Kinect sensors. The model consists of a surface mesh morphologically close to the patient's body and an articulated skeleton. The mesh is deformed by linear blend skinning according to the pose of the skeleton. The optimization is performed by particle filtering. Thanks to the graphics pipeline and the computing capabilities of the GPU, our algorithm reaches execution speeds close to real time. When working offline with a model very close to the patient's morphology, the joints locations and rotations are estimated with an average accuracy respectively smaller than a few millimeters and a few degrees.