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

Bakre, Farouk, Awais, Muhammad ORCID: https://orcid.org/0000-0001-6421-9245, Raza, Mohsin ORCID: https://orcid.org/0000-0002-7351-9749, Khan, Umar and Amin, Hafeezullah (2025) Improving Augmented Reality Serious Games for Cognitive Rehabilitation Using ID3 Decision Tree Algorithm. In: 2025 5th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2025. 2025 5th International Conference on Digital Futures and Transformative Technologies, ICoDT2 2025 . The Institute of Electrical and Electronics Engineers (IEEE). ISBN 979-8-3315-5963-2

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Abstract

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.

Item Type:	Book Section
Uncontrolled Keywords:	ar,cognitive rehabilitation,decision tree,serious games,artificial intelligence,signal processing,health informatics,management of technology and innovation,computer networks and communications,computer science applications ,/dk/atira/pure/subjectarea/asjc/1700/1702
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Health Computing Faculty of Science > Research Groups > Cyber Intelligence and Networks
Related URLs:	https://www.scopus.com/pages/publication...
Depositing User:	LivePure Connector
Date Deposited:	11 Feb 2026 11:30
Last Modified:	18 Jun 2026 22:20
URI:	https://ueaeprints.uea.ac.uk/id/eprint/101911
DOI:	10.1109/ICoDT269104.2025.11360726

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