Upper limb motor coordination based early diagnosis in high risk subjects for Autism

Autism is a lifelong condition present from early childhood. Medical specialists' diagnosis autism based on observation is of great difficulty in communicating, difficulties for forming relationships with other people, and delayed speech. The scientists tried to discover other early signs to reach the early detection of Autism Spectrum Disorders (ASD). Early diagnosing is very important to initiate and improve treatment results. One of these signs is based on examination of upper limb motor movements. This study aims to determine whether a simple upper limb motor movement could be useful to classify High Risk (HR) infants for autism and comparison infants with Low Risk (LR) for autism. Also, this paper presents a computational intelligence method that uses HR and LR subjects between the ages of 12 and 36 months to make an early autism diagnosing. The paper examined one task which asks to insert an object into a box. It analyzed the data by using Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The results show engorging results in comparison to other state or art methods.