This paper describes a study protocol to investigate the use of immersive virtual reality as a treatment for amputees' phantom limb pain. This work builds upon prior research using mirror box therapy to induce vivid sensations of movement originating from the muscles and joints of amputees' phantom limbs. The present project transposes movements of amputees' anatomical limbs into movements of a virtual limb presented in the phenomenal space of their phantom limb. It is anticipated that the protocol described here will help reduce phantom limb pain.
In this paper, we introduce a 3-D human-body tracker capable of handling fast and complex motions in real-time. The parameter space, augmented with first order derivatives, is automatically partitioned into Gaussian clusters each representing an elementary motion: hypothesis propagation inside each cluster is therefore accurate and efficient. The transitions between clusters use the predictions of a Variable Length Markov Model which can explain highlevel behaviours over a long history. Using Monte-Carlo methods, evaluation of model candidates is critical for both speed and robustness. We present a new evaluation scheme based on volumetric reconstruction and blobs-fitting, where appearance models and image evidences are represented by Gaussian mixtures. We demonstrate the application of our tracker to long video sequences exhibiting rapid and diverse movements.
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