MINDWELL AN AI-POWERED MENTAL HEALTH COMPANION

ABSTRACT

In recent years, mental health has emerged as a critical concern, especially among students and working professionals who face high levels of stress, anxiety, and emotional fatigue. While wearable devices like smartwatches can track physiological parameters such as heart rate and sleep, they often fail to interpret these signals in relation to a user’s emotional or psychological wellbeing. This project proposes an AI- powered Mental Wellbeing Monitoring System that intelligently analyzes both physiological and emotional data to detect stress and provide personalized support in real time. The system integrates heart rate data (from a smartwatch or simulated input) with natural language processing (NLP) techniques that evaluate the emotional tone of user text input. Using these combined insights, it computes a wellbeing score and offers instant recommendations such as breathing exercises, relaxation tips, or optional counselor connections. A Streamlet-based interactive dashboard visualizes live stress levels, emotional trends, and chatbot interactions, enabling users to monitor their mood patterns effortlessly. This uses VADER, a lexicon and rule-based algorithm, on your backend to analyse the sentiment and emotional tone of the user's text. Second, you leverage Neural Machine Translation (NMT), a deep learning model, via the Goslate library to translate chatbot responses. Finally, you use the browser's Web Speech API, which contains two "black box" AI models: Automatic Speech Recognition (ASR) to transcribe the user's voice to text, and Text-to-Speech (TTS) to generate spoken audio from the chatbot's replies. This approach bridges the gap between physical and emotional health tracking, promoting early stress detection and self- awareness. By leveraging artificial intelligence, the system aims to create a stigma-free, accessible, and personalized mental wellness companion that empowers individuals to take proactive steps toward maintaining a balanced and healthier mind. Future enhancements include integration with Google Fit or Apple Health APIs, voice-based emotion detection, and personalized meditation content for holistic wellbeing.

Key words: Mental Wellbeing, Multi-modal Data Fusion, Sentiment Analysis, Heart Rate Variability (HRV), Electron, AI Chatbot Natural Language Processing (NLP), Speech Recognition (ASR),Machine Translation.