Extended virtual reality based memory enhancement model for autistic children using linear regression
subject: Assistive technology; Autism spectrum disorder; Gamification; Machine learning; Memory enhancement; Play therapy; Power law of learning; Practice session; Unity
description: Extended Virtual Reality has expanded its wings to almost each and every sector enabling immersive experience in various fields and has found applications in gamification, learning, healthcare, etc. This technology has aided in providing solutions to various problems in different fields, and healthcare is the most prominent one among them. Children suffering from ASD which is a developmental disorder affecting the brain that impacts how a person perceives external responses, are finding it increasingly difficult to get treated as the treatment methods are tedious. There are very few methods which are regarded as standardized means of treating autistic children but there are a few common traits that can be found in children affected by ASD which can be grouped under three common categories. They are lack of communication skills, lack of basic mathematical knowledge and low levels of remembrance. With the help of Gamification, which provides therapy by means of games to those affected, the kids affected by ASD can be treated, powered by the concept of Extended Virtual Reality. In this paper, we have developed a model to provide autistic children a real world experience of playing games which will help them in enhancing their skills without any external interferences. Children who play these Extended Virtual Reality based games show gradual improvement, for which the results can be facilitated with the help of a Linear Regression model, helping us predict future response times. The proposed model results in enhancement of memory levels of the kids as a result of the game and classifies kids based on their enhancement in memory into high, medium and low. The mean absolute error of the linear regression model is found to be 0.0394. 2024, The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.
creator: Srivathsan K.; Bharath S.; Malini A.; Kumaravel R.; Sharma V.
source: International Journal of System Assurance Engineering and Management
publisher: Springer
date: 2024-01-01
rights: Restricted Access
relation: ISSN: 9756809
format: Online
language: English
type: Article
coverage: Srivathsan K., Department of Computer Science and Business Systems, Thiagarajar College of Engineering, Tamil Nadu, Madurai, India; Bharath S., Department of Computer Science and Business Systems, Thiagarajar College of Engineering, Tamil Nadu, Madurai, India; Malini A., Department of Computer Science and Business Systems, Thiagarajar College of Engineering, Tamil Nadu, Madurai, India; Kumaravel R., Department of Computer Science and Business Systems, Thiagarajar College of Engineering, Tamil Nadu, Madurai, India; Sharma V., Department of Computational Sciences, CHRIST (Deemed to be University), Delhi-NCR, India