  This paper develops a method to use RGB-D cameras to track the motions of a
human spinal cord injury patient undergoing spinal stimulation and physical
rehabilitation. Because clinicians must remain close to the patient during
training sessions, the patient is usually under permanent and transient
occlusions due to the training equipment and the movements of the attending
clinicians. These occlusions can significantly degrade the accuracy of existing
human tracking methods. To improve the data association problem in these
circumstances, we present a new global feature based on the geodesic distances
of surface mesh points to a set of anchor points. Transient occlusions are
handled via a multi-hypothesis tracking framework. To evaluate the method, we
simulated different occlusion sizes on a data set captured from a human in
varying movement patterns, and compared the proposed feature with other
tracking methods. The results show that the proposed method achieves robustness
to both surface deformations and transient occlusions.
