Bill-EVR: An Embodied Virtual Reality Framework for Reward-and-Error-Based Motor Rehab-Learning

• Published in: IEEE International Conference on Rehabilitation Robotics Vol. 2023; pp. 1 - 6
• Main Authors: Nardi, Federico, Haar, Shlomi, Faisal, A.Aldo
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.01.2023
• Subjects: Assistive robots; Degradation; Neurorehabilitation; Parkinson's disease; Propioception; Virtual environments; Visualization
• DOI: 10.1109/ICORR58425.2023.10304742
• ISSN: 1945-7901

VR rehabilitation is an established field by now, however, it often refers to computer screen-based interactive rehabilitation activities. In recent years, there was an increased use of VR-headsets, which can provide an immersive virtual environment for real-world tasks, but they are lacking any physical interaction with the task objects and any proprioceptive feedback. Here, we focus on Embodied Virtual Reality (EVR), an emerging field where not only the visual input via VR-headset but also the haptic feedback is physically correct. This happens because subjects interact with physical objects that are veridically aligned in Virtual Reality. This technology lets us manipulate motor performance and motor learning through visual feedback perturbations. Bill-EVR is a framework that allows interventions in the performance of real-world tasks, such as playing pool billiard, engaging end-users in motivating life-like situations to trigger motor (re)learning - subjects see in VR and handle the real-world cue stick, the pool table and shoot physical balls. Specifically, we developed our platform to isolate and evaluate different mechanisms of motor learning to investigate its two main components, error-based and reward-based motor adaptation. This understanding can provide insights for improvements in neurorehabilitation: indeed, reward-based mechanisms are putatively impaired by degradation of the dopaminergic system, such as in Parkinson's disease, while error-based mechanisms are essential for recovering from stroke-induced movement errors. Due to its fully customisable features, our EVR framework can be used to facilitate the improvement of several conditions, providing a valid extension of VR-based implementations and constituting a motor learning tool that can be completely tailored to the individual needs of patients.