Meta-Learning for Avatar Kinematics Reconstruction in Virtual Reality Rehabilitation

Abstract

Virtual Reality (VR) sensorimotor rehabilitation is still in infancy but will soon require avatars, digital alter-egos of patients' physical selves. Such embodied interfaces could stimulate patients' perception in a rich and highly customized environment, where sensorimotor deficits, such as in Chemotherapy-Induced Peripheral Neuropathy, could be corrected. In such scenarios, motion prediction is a key ingredient for realistic immersion. Yet, such a task lives under hard processing latency constraints and the inherent variability of human motion. We propose a neural network meta-learning system exploiting the underlying correlations in body kinematics with potential to provide, within latency guarantees, personalized VR rehabilitation. The unsupervised meta-learner is able to extract underlying statistics of the motion data by exploiting data regularities in order to describe the underlying manifold, or structure, of motion under sensorimotor deficits. We demonstrate, through preliminary experiments the potential of such a learning system for adaptive kinematics estimation in personalized rehabilitation VR avatars.