Computational Modeling of Psycho-physiological Arousal and Social Initiation of children with Autism in Interventions through Full-Body Interaction

This study is part of a larger project that wants to foster social initiation behaviors in children with Autism Spectrum Disorder (ASD). We approach this through a full-body interactive Mixed Reality (MR) experience that mediates a face-to-face play session between an ASD child and a non-ASD child. The goal of this study is to obtain a data model that allows us to evaluate the goodness of the MR system compared to a typical social intervention strategy based on construction tools (in this case LEGO bricks) which acts as the control condition. In this paper we present our first analysis of the arousal generated by the MR experience compared to that generated in the control condition. We address this by analyzing psychophysiological data recorded during the social interaction behaviors in the ASD child while playing with the non-ASD child. We followed a repeated-measures design with two conditions: our full-body interaction MR environment and the typical social intervention strategy based on LEGO bricks. To measure physiology, Electrocardiogram (ECG), Electrodermal Activity (EDA) and Accelerometer (ACC) data were acquired through a wearable designed by our lab. We used machine learning techniques to analyze the huge amount of multimodal data from the ASD children obtained during 18 trials (3 female and 15 male). As a result, we were capable of classifying social initiation behaviors of ASD children during the MR environment sessions and those occurring during the LEGO construction sessions based on the psycho-physiological data sources. This is a first sign showing that our MR system has specific properties, compared to a traditional construction-based intervention, which potentially provide a new interesting context to intervention in ASD.