AI-Powered Detection of Mental Health: A Multimodal System for Analyzing Social Media Activity and Physiological Data

Mental health problems are still widespread all over the world and among the most frequent are depression, anxiety, and stress, etc. The current paper presents a multimodal mental health detection system based on AI that merges behavior and physiological data to deliver psychological well-being with much better context and accuracy. The system leverages the power of Natural Language Processing (NLP) technology which, so to speak, scrutinizes the social media activity to spot the emotional tone, patterns of words, and conversation signals that may indicate mental distress. Moreover, the user's physiological data that user himself/herself provides, such as blood pressure, pulse rate, oxygen levels, sleep duration, sleep quality, etc., is processed by the system to display the user’s mental state with great precision. The whole process of the insights obtained is then used to evaluate the risk levels based on percentage for various conditions like stress, anxiety, depression, suicidal risk, etc. The results of the analysis are then presented in a user-friendly report that not only provides the necessary information but also includes the specific recommendations for the user. A mental health assistant chatbot feature is also there. The find help feature further assists users in finding mental health professionals near them. This system has made it easier by providing the users with a combination of social media analysis and physiological wellness tracking.