Adversarial Autoencoder and Multi-Armed Bandit for Dynamic Difficulty Adjustment in Immersive Virtual Reality for Rehabilitation: Application to Hand Movement.

ABSTRACT: Motor rehabilitation is used to improve motor control skills to improve the patient's quality of life. Regular adjustments based on the effect of therapy are necessary, but this can be time-consuming for the clinician. This study proposes to use an efficient tool for high-dimensional data by considering a deep learning approach for dimensionality reduction of hand movement recorded using a wireless remote control embedded with the Oculus Rift S. This latent space is created as a visualization tool also for use in a reinforcement learning (RL) algorithm employed to provide a decision-making framework. The data collected consists of motions drawn with wireless remote control in an immersive VR environment for six different motions called "Cube", "Cylinder", "Heart", "Infinity", "Sphere", and

SUBMITTER: Kamikokuryo K 

PROVIDER: S-EPMC9228873 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature