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Emotion-Driven IoT Feedback Loop for Caregiver Training

Author

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  • Md Mishal Mahmood

    (Masters in IT, Washington University of Science & Technology, 2900 Eisenhower Ave, Alexandria, VA 22314)

Abstract

Emotional sensitivity is one of the pillars of effective therapy of autism, but caregivers do not have real-time instructions on how to interpret affective expression of children. This paper proposes an Emotion-Driven IoT Feedback Loop (EDIFL) that is focused on feeding back data-driven feedback immediately to caregivers during behavioral interventions. The system includes multimodal IoT sensors, edge level emotion recognition and cloud-based adaptive feedback generation. The framework is made more responsive and accountable in autism care, through integrating machine learning, human-centered AI, and NIST AI RMF governance. Findings indicate an increase of 47% in accuracy of caregiver response and decrease of 38% in delayed reactions when compared to the old method of training sessions. The suggested EDIFL model is a great advancement in terms of intelligent, compassionate and morally responsible caregiving assistance.

Suggested Citation

  • Md Mishal Mahmood, 2024. "Emotion-Driven IoT Feedback Loop for Caregiver Training," Frontiers in Computer Science and Artificial Intelligence, Al-Kindi Center for Research and Development, vol. 3(2), pages 18-23, December.
    • Handle: RePEc:alk:fcsai0:v:3:y:2024:i:2:id:11461
    DOI: 10.32996/fcsai.2022.1.2.4
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