Object Tracking Implementation for a Robot-Assisted Autism Diagnostic Imitation Task

Abstract

Autism spectrum disorders (ASD) is a term used to describe a range of neurodevelopmental disorders affecting about 1% of the population, with increasing prevalence. Due to the absence of any physiological markers, diagnostics is based purely on behavioral tests. The diagnostic procedure can in some cases take years to complete, and the outcome depends greatly on the expertise and experience of the clinician. The predictable and consistent behavior and rapidly increasing sensing capabilities of robotic devices have the potential to contribute to a faster and more objective diagnostic procedure. However, significant scientific and technological breakthroughs are needed, particularly in the field of robotic perception, before robots can become useful tools for diagnosing autism. In this paper, we present computer vision algorithms for performing gesture imitation. This is a standardized diagnostic task, usually performed by clinicians, that was implemented on a small-scale humanoid robot. We describe the algorithms used to perform object recognition, grasping, object tracking and gesture evaluation in a clinical setting. We present an analysis of the algorithms in terms of reliability and performance and describe the first clinical trials.