Database-driven AI for personalized special needs therapy: Scalable behavioral analytics using oracle autonomous infrastructure

Chigurupati, Solomon Raju (2025) Database-driven AI for personalized special needs therapy: Scalable behavioral analytics using oracle autonomous infrastructure. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 1639-1652. ISSN 2582-8266

This paper presents a novel data-centric framework for enhancing special needs therapy through AI-driven behavioral analytics built on autonomous database infrastructure. By leveraging in-database machine learning and advanced database engineering principles, the system processes and analyzes multi-modal data—including video feeds, wearable sensor telemetry, and therapist annotations. The intelligent platform enables real-time detection of behavioral patterns, sensory triggers, and therapy effectiveness for children with autism spectrum disorder, ADHD, and related developmental conditions. Database engineering proves critical in transforming raw observations into timely, actionable insights for caregivers and clinicians, addressing fundamental challenges in current behavioral therapy approaches. The framework bridges the temporal gap between observation and intervention, enabling personalized therapeutic strategies that adapt to individual neurodevelopmental profiles while scaling across diverse clinical environments.