Abstract
Detection of autism in the beginning of infant life is very important to increase child treatment outcomes. There are many signs that help to diagnosis autism based on observation of great difficulty in communicating, delayed speech, and difficulty in forming relationships with other people. Attempts to discover other early symptoms and signs do not stop and tried to reach the early detection of autism. One of these signs is based on examination of child motor movements, such as kinematic for both gait and upper limb. This study aims to know how a basic upper-limb motion would be helpful to accurately classify High-Risk (HR) Infants for autism and infants with Low-Risk (LR) for autism. Also, the paper examined the task that throws a small ball into a transparent plastic tub followed by inserting the ball into a clear tube open at both ends performed by HR and LR subjects between the ages of 12 and 36 months. Then the data was analyzed by using Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The results showed encouraging results in comparison to other state or art.
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