Abstract
A system has been developed to automatically record and detect behavioral patterns and vocal stereotypy which is also known as vocal stimming, a non-verbal vocalization often observed in children with Autism Spectrum Disorder (ASD). The system incorporates audio, video and wearable accelerometer based sensors. Microphones and video camera were used to collect data and were used for analysis. K-SVD, which is a generalized version of the k-means clustering algorithms for dictionary learning, was used to detect vocal stereotypy. Observing the subspace that the data lives in allows us to detect vocal stimming and sounds of frustration. The proposed system was able to detect vocalized stimming with detection rate between 73 - 93 percent.