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Bio-behavioral monitoring of self-injurious behaviors in Autism Spectrum Disorder


The goal of this work is to develop a real-time system to predict self-injurious behavior (SIB) using wearable sensors and innovative data analytics methods, and to pair this system with simple and intuitive mobile interface designs that provide timely alerts and interventions for mitigating SIB episodes in children with autism.


Our methodology is grounded in movement systems theories, clinical psychology, control theoretical methods, data analytic techniques based on machine learning, statistical modeling approaches and human-factors principles guiding novel interface design for children.