• Dr.M.Manoj Prabu
• Mr.J.Nakul Chempian
• Ms.S.Raveena
• Ms.R.C.Subasree
• Ms.M.Shahanas

Cognitive decline and neurological disorders such as dementia and Alzheimer’s disease often remain undetected until advanced stages, limiting timely intervention. This project proposes an AI-powered embedded system for cognitive function monitoring that integrates low-cost wearable sensors with machine learning algorithms to assess real-time brain activity and related physiological parameters. Using EEG, GSR, and heart rate data, the system extracts relevant features and applies deep learning models to evaluate cognitive states such as stress, attention, and memory load. The embedded hardware platform ensures portability, energy efficiency, and continuous monitoring outside clinical environments. The proposed system aims to provide early detection, personalized feedback, and real-time alerts, thereby supporting preventive healthcare, remote patient monitoring, and enhanced clinical decision-making.

Embedded system, EEG, GSR, HRV, Wearable sensors, Machine learning, Real-time monitoring, Dementia detection.

"AI- Powered Embedded System For Cognitive Function Monitoring", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 10, page no.c277-c282, October-2025, Available :http://www.jetir.org/papers/JETIR2510239.pdf

"AI- Powered Embedded System For Cognitive Function Monitoring", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 10, page no. ppc277-c282, October-2025, Available at : http://www.jetir.org/papers/JETIR2510239.pdf