Mental Health Tracker

Mental Health is a critical aspect of overall well-being, yet it often goes undiagnosed due to a lack of accessible tools for early detection. Our project presents a novel approach to mental health tracking using facial expression, voice recognition technologies and stress related words spoken by person. By analyzing key emotional cues through facial gestures and vocal tones, the system is designed to detect early signs of stress, anxiety, and depression. In addition to detection, the system offers personalized remedial measures aimed at relieving stress, such as guided breathing exercises, mindfulness prompts, and suggestions for professional mental health resources. The integration of real-time data analysis and machine learning models ensures that the system provides accurate and responsive feedback, promoting mental well-being in a non-intrusive, user-friendly manner. This paper outlines the architecture, implementation and potential impact of our mental health tracking system.