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Updated: Jul 2, 2026

Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation

Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation

Published on: March 8, 2018

Machine learning-based adaptive personalization in virtual reality stroke rehabilitation: a systematic review.

Arar Al Tawil^1,2, Siti Hazyanti Mohd Hashim¹, Aseel Aburub³

¹School of Computer Sciences, Universiti Sains Malaysia, USM Penang, Malaysia.

Frontiers in Rehabilitation Sciences

|July 1, 2026

Summary

Machine learning enhances virtual reality (VR) stroke rehabilitation by personalizing therapy. This systematic review confirms ML-based adaptive VR improves upper limb function, offering a safe and effective approach for stroke recovery.

Keywords:

adaptive personalization machine learning reinforcement learning stroke rehabilitation virtual reality

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Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation

Mobile Game-based Virtual Reality Program for Upper Extremity Stroke Rehabilitation

Published on: March 8, 2018

Cognitive Function and Upper Limb Rehabilitation Training Post-Stroke Using a Digital Occupational Training System

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Published on: December 29, 2023

A Rehabilitation Program of Exoskeleton-assisted Body Weight-Supported Treadmill Training with Non-immersive Virtual Reality for Stroke Patients

A Rehabilitation Program of Exoskeleton-assisted Body Weight-Supported Treadmill Training with Non-immersive Virtual Reality for Stroke Patients

Published on: May 16, 2025

Area of Science:

Neurorehabilitation
Virtual Reality Technology
Machine Learning Applications

Background:

Stroke is a leading cause of global disability, necessitating innovative therapeutic strategies.
Virtual Reality (VR) is emerging as a promising tool in stroke rehabilitation.
Existing VR systems often use rule-based difficulty adjustments, which may not fully address the complex, non-linear recovery process after stroke.

Purpose of the Study:

To systematically review evidence on Machine Learning (ML)-based adaptive personalization in VR stroke rehabilitation.
To analyze ML algorithms, adaptation strategies, clinical outcomes, and implementation considerations.
To synthesize current research on the efficacy and safety of ML-driven VR for stroke recovery.

Main Methods:

A systematic literature search was conducted across six databases (January 2015-December 2025) following PRISMA 2020 guidelines.
Studies utilizing ML algorithms for VR in stroke rehabilitation were included.
Data extraction covered ML algorithms, adaptation mechanisms, therapeutic parameters, clinical outcomes, and implementation factors; risk of bias was assessed, and meta-analysis was performed.

Main Results:

Twenty-five studies were analyzed, with Reinforcement Learning (n=8) and Deep Learning (n=7) being prominent ML algorithms.
Approximately 80% of studies employed real-time closed-loop adaptation.
Meta-analysis revealed a significant improvement in upper limb motor function (FMA-UE) by 7.47 points (p<0.001), exceeding the minimal clinically important difference. Usability scores were high (SUS=72.5), with minimal adverse events.

Conclusions:

ML-based adaptive VR rehabilitation is clinically effective and safe for stroke recovery.
Significant improvements in motor function and positive usability highlight the potential of this approach.
Implementation challenges in resource-limited settings warrant further attention for broader accessibility.