AI-Driven Emotional and Cognitive Well-being System for Elderly Care: A Multi-Modal Biofeedback Platform for Real-Time Monitoring and Support

The rapid advancement of artificial intelligence (AI) and multimodal biofeedback technologies has provided some novel avenues for tackling elderly-care problems. In this paper, we describe an emotional and cognitive well-being system powered by AI that continuously monitors, evaluates, and helps in the mental wellbeing of elderly people. The multimodal system provides a complete emotional and cognitive description based on analyses of voice, facial expression recognition, eye movements, and Watershed enhanced EEG cognitive load measures. Convolutional neural networks and recurrent neural networks are advanced deep learning models being used in emotion classification and prediction of cognitive states. The caregiving-from-the-caregiver interface will be the application, which will provide real-time alerts and personalized recommendations. This app secures all data visualization outputs with discussions through AI enhanced descriptors while ensuring compliance with the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) standards for privacy. When direct-use EEG sensors cannot be attached to the elderly, the system also operates under simulation conditions with EEG dataset inputs, like DEAP or SEED. With this mechanism, the system strives to nurture well-being in aging populations by proactive emotional support, monitoring cognitive health, and early detection for intervention. This is expected to add knowledge in the perspectives of AI in healthcare, cognitive computing, and elderly well-being, affirming the effectiveness of multimodal AI systems in improving the quality of life in the aged populace.