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Cognitive-Adaptive AI Framework for Behavioral Crisis Prediction in Children with Autism

Author

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  • M Salman Khan

    (Department of Computer Science & Engineering, Brac University, Dhaka, Bangladesh)

Abstract

Behavioral crises in children with autism often come on sharply and are difficult for caregivers and clinicians to intervene in a timely way. In this study, the proposed cognitive-adaptive artificial intelligence (AI) system integrates reinforcement learning (RL) with multimodal Internet of Things (IoT) sensing and real-time emotional context modeling to forecast the escalation of a crisis. Pilot studies on 12 weeks, comprising a sample of 60 autistic children, showed an improvement of 23 percent in detecting early crisis and a reduction of 17 percent in false positives as compared to baseline models. The framework constantly acquires contextual information patterns - based on physiological, behavioral, and environmental information - to change its decision policies in real-time. The findings demonstrate the revolutionary aspect of humanistic, data-intensive AI systems in proactive management of autism care.

Suggested Citation

  • M Salman Khan, 2024. "Cognitive-Adaptive AI Framework for Behavioral Crisis Prediction in Children with Autism," Frontiers in Computer Science and Artificial Intelligence, Al-Kindi Center for Research and Development, vol. 3(2), pages 07-10, December.
    • Handle: RePEc:alk:fcsai0:v:3:y:2024:i:2:id:11447
    DOI: 10.32996/fcsai.2022.1.2.2
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