Object Classification for Child Behavior Observation in the Context of Autism Diagnostics Using a Deep Learning-based Approach

Abstract

In this paper we demonstrate the effectiveness of a deep learning approach for object detection and classification using a mono-vision feedback of a NAO humanoid robot for assessing the child's behavior during a free play with standardized toys. The free play is one of the tasks contained in the standard ADOS-2 autism spectrum disorder diagnostic protocol used by clinicians. In order to make an accurate, robust and fast object detector, a new data set for learning and testing has been created to enable a reliable assessment of the child's behavior while playing with the toys. This has also led to the development of algorithms and mechanism to assess child's attention based on the toys that the child is playing with. This paper concludes with the discussion about the challenges encountered and their solutions, as well as about the prospective development goals focused on achieving more robust and accurate child attention analyzer.