AI-Powered Emotion-Aware Mental Health Guardian

Abstract

The rising rates of stress, anxiety, and depression in contemporary culture have made mental health a major worldwide concern. In order to prevent serious psychological problems, early detection and prompt care are essential. It is now feasible to create intelligent systems that can use computational methods to analyze human emotional states because to developments in artificial intelligence, machine learning, and deep learning. This research introduces an AI-Powered Emotion Aware Mental Health Guardian that uses multimodal data sources to track and evaluate emotional wellbeing. Three primary modalities are used by the suggested system to detect emotions: text-based sentiment analysis, spoken emotion identification, and facial expression analysis. While Mel-Frequency Cepstral Coefficients (MFCC) in conjunction with deep learning models are utilized to extract emotional aspects from voice signals, Convolutional Neural Networks (CNNs) are utilized to determine emotions from facial photos. Additionally, textual input is analyzed and emotional states are classified using transformer-based models like BERT. An overall mood score and emotional intensity level are calculated by integrating the results from these separate models through a fusion mechanism. The system offers tailored suggestions, such as stressrelieving activities, music selections, relaxation techniques, and encouraging messages, based on the identified emotional intensity. This method guarantees that the system not only detects emotional distress but also provides proactive support to enhance mental health. When compared to singlemodality systems, the incorporation of multimodal emotion detection improves prediction accuracy and dependability. The AI-Powered Emotion Aware Mental Health Guardian shows how deep learning may be used in real-world mental health monitoring and emotional support systems. It is an intelligent supportive tool that can help users manage stress and emotional imbalance, but it does not take the place of a professional psychological diagnosis. The study demonstrates how AI-driven solutions may support mental health and create emotionally intelligent digital systems.