Robotic framework for music-based emotional and social engagement with children with Autism
In the United States, the rapid increase in the population of children with autism spectrum disorder (ASD) has revealed the deficiency in the realm of therapeutic accessibility for children with ASD in the domain of emotion and social interaction. There have been a number of approaches including several robotic therapeutic systems (Feil-Seifer and Mataric 2008; Scassellati, Admoni, and Mataric 2012) displaying many intriguing strategies and meaningful results. However, the spectral diversity of ASD is so vast that we still need to push forward research to provide parameterized therapeutic tools and frameworks. To overcome this challenge, state-of-the-art techniques must still be developed to facilitate autonomous interaction methods for robots to effectively stimulate the emotional and social interactivity of children. We focus on the recent studies that reveal strong relevance in premotor cortex among neural domains for music, emotion, and motor behaviors (Kohler et al. 2002; Molnar-Szakacs and Overy 2006). We propose that musical interaction and activities can provide a new therapeutic domain for effective development in the children’s emotion and social interaction. The objective of the research is to design novel forms of musical interaction combined with physical activities for improving social interactions and emotional responses of children with ASD. We present our initial design schemes of the robotic framework to utilize musical stimulus for initiating engagement and deepen interaction in emotional and social relationships through interactive robotic sessions.
AAAI Workshop: Artificial Intelligence Applied to Assistive Technologies and Smart Environments
Park, C. H.,
Robotic framework for music-based emotional and social engagement with children with Autism.
AAAI Workshop: Artificial Intelligence Applied to Assistive Technologies and Smart Environments, 39-40.
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Copyright c 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Publisher's version of record: http://www.aaai.org/ocs/index.php/WS/AAAIW15/paper/download/10186/10149