Detecting A Child’s Stimming Behaviours for Autism Spectrum Disorder Diagnosis using Rgbpose-Slowfast Network

Autism Spectrum Disoder (ASD) is a neurodevelopmental disorder characterized by (a) persistent deficits in social communication and interaction, and (b) presence of restrictive, repetitive patterns of behaviours, interests or activities. The stereotyped repetitive behaviours are also referred to as stimming behaviours. We propose a deep learning based approach to automatically predict a child’s stimming behaviours from videos recorded in unconstrained conditions. The child’s region in the video is tracked and its skeletal joints are derived using the pose estimator. The heatmap representation of skeletal joints and the raw video signals are used as inputs to the two pathways of the RGBPose-SlowFast deep network to model stimming behaviours. The proposed model is evaluated using the publicly available Self-Stimulatory Behaviour Dataset (SSBD) of stimming behaviours. The generalization ability of the model is validated using the Autism dataset containing child’s motor actions. Our experiments demonstrate state-of-the-art results on both datasets.