Smartphone Based Autism Social Alert System

Autism currently affects 1 in every 88 American children impairing their social interactions, communications and daily living. Often, parents, educators, and researchers need to purchase expensive equipment to help autistic children cope with challenges in their daily living. In this paper, we present the Smartphone-Based Autism Social Alert (SASA) system which we design to help such children. The SASA system uses the inexpensive sensors embedded within smartphones to facilitate the study of the autistic children´s behaviors by recording and analyzing data collected from such embedded sensors in smartphones carried by autistic children. Our system can automatically detect their stereotypical behaviors such that early interventions can be taken by caregivers or teachers. In addition, the system can correlate environmental sensor data streams, e.g. audio background, with the occurrence of stereotypical behaviors so as to identify potential environmental factors that may trigger such behaviors. We also include some preliminary classification results on the sensor data which we have collected from Android-based phones using the WEKA J.48 classifier. Our preliminary results show that simple features extracted from accelerometer readings are sufficient to give high accuracy rates when training is performed on a per user per device basis. Our audio classifier which uses 12 MFCC coefficients, average zero crossing rate, and energy can give an accuracy of 78.6% when evaluated using audio traces collected for seven audio categories. Additional extensive experiments will be carried out in the near future at a nearby secondary school for autistic children.