Recognizing Self-Stimulatory Behaviours for Autism Spectrum Disorders

Autism spectrum disorder (ASD) is a neurobiological disorder that some symptoms such as deficit of social interaction and communication, limited and repeated behavior are observed in patients. Repetitive behaviors are signicant clues for diagnosis of ASD. These repetitive behaviors, which is called self-stimulating behaviors, are described as flapping arms like wings, shaking head back and forth, and spinning around itself. Physicians should observe and examine these self- stimulating behaviors by interacting with children for a long time that makes it difficult in early diagnosis of ASD. In this paper, the self-stimulating behaviors of ASD children are examined using deep learning algorithms. For this purpose, a new video dataset recorded by parents in daily environment without being dependent on the hospital environment are created. Video features are extracted using 3DCNN and ConvLSTM deep learning algorithms. Softmax regression is applied as a classifier. As a result of the experiments performed, 75,93 % accuracy is obtained even if the videos are recorded in the daily environment.