A Framework for Intelligent Learning Assistant Platform Based on Cognitive Computing for Children with Autism Spectrum Disorder
Akshay Vijayan, S Janmasree, Chenicheri Kizhakkeveettil Keerthana, L Baby Syla · 2018
Children with Autism Spectrum Disorder(ASD) suffer from social and communication issues. In addition to that they also exhibit a complex collection of behaviors which makes it difficult for the trainers to identify the methodology to be adapted for training them. At present a mishmash of techniques are used to evaluate them in general, without identifying their uniqueness or specific characteristics. In this paper, we propose a cognitive computing based intelligent learning assistant that could provide suitable courseware by identifying a child specifically based on the behavioural patterns that aids the autistic student's learning. A hybrid approach which blends cognitive, developmental and behavioural psychology is used to generate an autism assessment model, by using which a specific courseware is provided to the child. An interactive chatbot along with a visual aid is used as an interface to interact with the child so as to capture his real-time responses. This system features Reinforcement learning, Regional Convolution Neural Network (R-CNN), Deep Convolution Neural Network (deep-CNN) to provide a personalized learning assistant platform for children with Autism Spectrum Disorder(ASD).