AI-Bot Powered Emotion Detection and Mental Wellness System with Facial and Voice Analysis for Personalized Recommendations

We present a novel Emotion-Based Frequency Recommendation and Healing System using Speech and Facial Analysis in this paper. This system will help to solve the emotional problems of users based on their speech and facial expressions. The system employs machine learning algorithms for the detection of primary emotions such as happiness, sadness, fear, anger, surprise, disgust, and neutrality. The therapy module would prescribe bespoke interventions such as binaural beats, isochronic tones, or guided meditation depending on what has been detected as the emotion with which the person needs help with their emotional self-regulation and control. The system will also prompt for relevant resource access, be it jokes, songs, comedy video, or a list of psychological activity to facilitate therapy. Designed for real-time emotion monitoring, the system adapts in response to the volatility of the emotional states the users experience for timely and effective therapeutic interventions. The paper here discusses design and methodology, architecture, and performance evaluation regarding its novelty in its role as a tool toward enhancing mental and emotional health.