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 present a tracking framework for capturing articulated human motions in real-time, without the need for attaching markers onto the subject's body. This is achieved by first obtaining a low dimensional representation of the training motion data, using a nonlinear dimensionality reduction technique called back-constrained GPLVM. A prior dynamics model is then learnt from this low dimensional representation by partitioning the motion sequences into elementary movements using an unsupervised EM clustering algorithm. The temporal dependencies between these elementary movements are efficiently captured by a Variable Length Markov Model. The learnt dynamics model is used to bias the propagation of candidate pose feature vectors in the low dimensional space. By combining this with an efficient volumetric reconstruction algorithm, our framework can quickly evaluate each candidate pose against image evidence captured from multiple views. We present results that show our system can accurately track complex structured activities such as ballet dancing in real-time.
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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