Sensory Walls and AI Integration: A Self-Sustaining Framework for Real-Time Dysregulation Detection

Integrating sensor fusion and AI-driven adaptation, SensoryWall Inc.'s conceptual framework outlines how real-time multisensory data can transform static sensory walls into dynamic learning environments for children with Autism Spectrum Disorder (ASD) and sensory processing challenges.In theoretical pilot simulations spanning approximately 60 minutes of labeled interactions, a Random Forest classifier applied at five-second intervals demonstrated classification accuracy near 95% for distinguishing baseline versus dysregulated behavior.The proposed build methodology employs clear-coated plywood substrates outfitted with edge padding and a modular grid of Velcro-mounted tactile, auditory, and visual components, ranging from sequin panels and fidget spinners to mirror sheets and mechanical latches, to facilitate community-driven assembly and maintenance.A sustainable deployment model leverages open-source construction guides, volunteer-led build days, and grant-funded material kits, while district maintenance teams implement installation using standardized anchors and protocols.Scalability is enhanced through uniform panel dimensions, bulk procurement strategies, and self-sustaining operational practices.This framework suggests that AI-augmented sensory interfaces, grounded in quantitative engagement metrics, can support proactive sensory modulation and personalized intervention.By presenting a theoretically grounded roadmap for sensor integration, data analysis, and sustainable scaling, this work contributes to the growing discourse on leveraging accessible AI and modular design to advance inclusive educational technologies.