I-Corps: Artificial Intelligence Driven Prediction, Monitoring, and Management of Unwanted Behavior in Patients with Autism: Realtime, Smart, Automated, and Personalized

Award ID: 2344599

The broader impact/commercial potential of this I-Corps project is the development of an artificial intelligence (AI) powered platform for continuous, real-time, personalized prediction, monitoring, and management of unwanted behavior in patients with autism and developmental disabilities. The proposed technology is designed for families of individuals with autism to receive real-time insights and personalized strategy suggestions, improving their quality of life. Early intervention enabled by this platform also may lead to benefits for insurance companies and hospitals due to savings related to pharmaceuticals and repeated emergency department visits. In addition, practitioners and schools may benefit from automated data analysis, enhancing evidence-based decision-making and student support. Psychiatric hospitals may improve treatment approaches, reducing hospitalizations and emergencies. The proposed AI-driven platform may be used for behavior analysis, positively impacting individuals with autism and their families. The proposed technology may lead to an advancement in personalized behavior management strategies for autism and developmental disabilities. This I-Corps project is based on the development of an artificial intelligence (AI) approach to the prediction and management of unwanted behavior in autism patients. The proposed platform leverages advanced AI and machine learning algorithms to extract patterns from visual, auditory, and kinesthetic data, and uses these data to facilitate early risk assessment and supportive evidence-based management plans. The collected data, including non-wearable sensor information and caregiver input, undergoes advanced analysis for personalized prevention strategies and optimal risk management. By integrating multi-stream sensor data fusion, AI-driven pattern mining, and real-time analysis, the project bridges the gap between existing behavioral intervention methods and cutting-edge technology. The contribution lies in the fusion of diverse data sources to extract meaningful patterns, providing caregivers, practitioners, schools, and hospitals with actionable insights for behavior management. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Ruth Shuman