2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 19 , Issue 1 , PP: 144-163, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Nada Asar 1 * , Mohamed Handosa 2 , M. Z. Rashad 3
Doi: https://doi.org/10.54216/FPA.190113
Accurate emotion detection is crucial for individuals facing communication barriers, yet existing approaches struggle with real-time limitations and information Individual privacy. This research presents a new IoT-based framework that integrates EEG and physiological signals from wearable sensors with deep learning models, including CNN, Decision Trees, SVM, KNN, and Naïve Bayes. Unlike traditional methods, our approach effectively mitigates data latency and sensor noise while ensuring compliance with GDPR and HIPAA standards. Experimental results demonstrate a validated accuracy of 99-100%, outperforming state-of-the-art models. These developments establish our framework as a game-changing instrument for affective computing applications, enhancing human-machine interaction and healthcare quality of life.
Smart health , Human-machine interaction , Machine learning , Deep learning , Internet of things , Affect Detection , Emotion Detection
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