Towards Forecasting Engagement in Children with Autism Spectrum Disorder using Social Robots and Deep Learning

Abstract

The personalization of therapy for children with Autism Spectrum Disorder (ASD) has been found to be crucial in comparison to a universal approach. This personalization in therapy demands the ability to adapt to the individual’s needs and engagement levels to avoid disinterest or meltdowns. This paper proposes the first step towards forecasting engagement of children with ASD during therapy sessions using Blood Volume Pulse (BVP). The BVP data is collected from an interactive session between two children with ASD in the presence of a NAO robot, and the forecast is made using a Deep Learning architecture combining Convolutional Neural Networks (CNNs) and Long-short term Memory (LSTM). Out of the three networks tested: LSTM, CNN and CNN+LSTM, the latter was found to outperform the others and gave a coefficient of determination of 0.955. The forecast was done using less than 3 minutes of prior BVP data to forecast 3 minutes into the future time steps.