Bridge: An AI-Enabled Platform to Support Coordinated Care for Children with Autism
Lead PI:
Mimi Xie

Children with autism spectrum disorder (CWA) often engage in severe problem behavior, and thus require long-term care with individualized clinical assessment, treatment, and intervention. Applied behavior analysis (ABA) therapy, considered an evidence-based best practice, can be time-consuming, resource intensive, and prone to human bias. This Smart and Connected Communities Planning Grant (SCC-PG) project seeks to co-design an AI-based solution for collecting behavior data for automatic measurement of severe behaviors (e.g., frequency, intensity, latency, etc.), assisting diagnosis and treatment decisions, and communicating to patients and families in real-time for early intervention.

The project team partners with a range of community stakeholders in a pilot study in San Antonio, TX for coordinated care of CWA through an AI-augmented platform. The planning process begins with identifying key platform parameters including data types, privacy concerns, main function modules, and expected performance. Different AI algorithms are examined for fusing multi-modal data to inform ABA. Post-survey and focus groups are conducted to uncover the effects of incorporating the IoT-Edge-Cloud prototype into clinical practices. The project has the potential to improve health outcomes for autistic children and the well-being of their families.

Mimi Xie
I'm an Assistant Professor in the Department of Computer Science at University of Texas at San Antonio (UTSA). Before I joined UTSA in August, 2019, I obtained my Ph.D. from Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
Performance Period: 08/01/2023 - 07/31/2024
Institution: University of Texas at San Antonio
Award Number: 2306596