AI-Powered Wearable System for Depression Detection and Monitoring

This project addresses the critical issue of mental health by providing an AI-powered wearable system that detects and monitors depression through real-time physiological and emotional data analysis. Using smartwatches, the system tracks metrics such as heart rate variability, sleep patterns, and electrodermal activity, which are processed through machine learning models and natural language processing algorithms to assess mental health status. This approach enables early detection of depression symptoms, offering personalized insights and continuous, non-invasive monitoring, especially in regions with limited access to mental health resources. The system empowers users to better understand their emotional well being while equipping healthcare professionals with valuable data for timely intervention. The impact includes improved early detection and personalized mental health support, while community engagement is fostered by offering an accessible solution for mental health monitoring in underserved areas. Challenges faced included ensuring the accuracy of real-time data analysis and adapting the system for diverse populations, which were solved through refined algorithms and user-centric design. The system is scalable for global deployment, particularly in regions with limited mental health services, and sustainable through its low-cost, non-invasive nature. Aligned with the SDGs 3, 10 and 9.