Improving Augmented Reality Serious Games for Cognitive Rehabilitation Using ID3 Decision Tree Algorithm

Background: Cognitive decline is a major challenge for aging adults. Serious games and augmented reality (AR) offer engaging approaches to cognitive rehabilitation, but existing approaches lack personalization.Method: This study applies the ID3 decision-tree algorithm to personalize AR serious games by adjusting difficulty levels based on user age, education and prior performance. A pilot study with 12 participants is conducted in this research, which compared personalized AR games against non-personalized AR training.Results: Both groups showed improvements after training, but the personalized AR group showed greater gains in cognitive speed and attention as measured by the Trail Making Test (TMT).Conclusion: Personalized AR serious games demonstrate potential for enhancing cognitive rehabilitation. Future work should involve larger trials, longer study periods and inclusion of additional user-specific factors to improve accuracy and effectiveness.