Self-Guided DMT: Exploring a Novel Paradigm of Dance Movement Therapy in Mixed Reality for Children with ASD
Weiying Liu, Yanyan Zhang, Baiqiao Zhang, Qianqian Xiong, Hong Zhao, Sheng Li, Juan Liu, Yulong Bian
Abstract
Children diagnosed with Autism Spectrum Disorder (ASD) often exhibit motor disorders. Dance Movement Therapy (DMT) has shown great potential for improving the motor control ability of children with ASD. However, traditional DMT methods often lack vividness and are difficult to implement effectively. To address this issue, we propose a Mixed Reality DMT approach, utilizing interactive virtual agents. This approach offers immersive training content and multi-sensory feedback. To improve the training performance of children with ASD, we introduce a novel training paradigm featuring a self-guided mode. This paradigm enables the rapid creation of a virtual twin agent of the child with ASD using a single photo to embody oneself, which can then guide oneself during training. We conducted an experiment with the participation of 24 children diagnosed with ASD (or ASD propensity), recording their training performance under various experimental conditions. Through expert rating, behavior coding of training sessions, and statistical analysis, our findings revealed that the use of the twin agent for self-guidance resulted in noticeable improvements in the training performance of children with ASD. These improvements were particularly evident in terms of enhancing movement quality and refining overall target-related responses. Our study holds clinical potential in the field of medical treatment and rehabilitation for children with ASD.
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