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

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

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 conside...

Full description

Bibliographic Details
Main Authors: Kenta Kamikokuryo, Takumi Haga, Gentiane Venture, Vincent Hernandez
Format: Article
Language:English
Published: MDPI AG 2022-06-01
Series:Sensors
Subjects:
machine learning
reinforcement learning
multi-armed bandit
immersive virtual reality
dynamic difficulty adjustment
end effector
Online Access:https://www.mdpi.com/1424-8220/22/12/4499

Similar Items

Similar Items