Autism Signs Detection and Recognition

Abstract

Autism Spectrum Disorder (ASD) is a mental developmental disorder associated with social and communicational defects and Stereotypical Motor Movements (SMM). SMM is also associated with several mental developmental disorders and has several forms like arm flapping, head banging, ear covering and spinning with various degrees of severity. SMM affects the learning development of children and might lead to self-injury in severe cases. ASD reasons are still mysterious, but scientists believe that genetic defect is the main cause. Although autism is a lifelong disorder; early diagnosing and treatment can improve children’s development stages. Diagnosing autism has been an exhaustive process that attracts several researchers’ attention. The traditional protocol of diagnosing autism relies on long clinical observation sessions for an autistic child. As the population of autistics exceeds the medicals who can diagnose this disorder, the diagnosing waiting time may exceed six months. Since the traditional protocol of diagnosing autism was a major time-consuming problem, several techniques were utilized to assist in diagnosing autism including Electroencephalography signals (EEG), Functional Magnetic Resonance Imaging (fMRI), Blood and genetics analysis, wearable sensors, and computer vision techniques. However, computer vision-based techniques have many advantages over the other techniques in terms of simplicity and minimized costs. This thesis aims to design and implement a computer vision-based system that detects and recognizes some signs of autism in children from video sequences, focusing on repetitive motor behaviours in autistics.