Emotion-Aware AI for Enhancing Elderly Mental Health

Abstract: Emotion recognition plays an important role in understanding the mental and emotional well-being of individuals, especially in elderly care. With the rapid growth of the aging population, there is an increasing need for automated systems that can continuously monitor emotional states and provide timely support. This project presents an emotion-aware artificial intelligence system that detects human emotions from facial expressions using deep learning techniques. The system analyzes facial images uploaded by users and classifies emotions such as happy, sad, angry, fearful, surprised, and neutral. A customized Convolutional Neural Network (CNN) model is used for emotion detection due to its ability to automatically extract discriminative facial features and handle variations in facial expressions. The application is developed using the Django framework for backend operations and a relational database for storing user information, emotion records, appointments, and reminders. The system also integrates healthcare support features such as doctor appointment scheduling, medicine reminders, and WhatsApp-based notifications to caregivers and doctors. Experimental results indicate that the proposed model achieves reliable emotion classification performance and supports timely emotional monitoring, thereby enhancing mental health care and quality of life for elderly individuals.

Keywords: Emotion Recognition, Facial Expression Analysis, Deep Learning, Convolutional Neural Network, Elderly Mental Health, Computer Vision, Healthcare Monitoring.