ASP.NET Core MVC mental health platform with AI, Twilio, and Azure integration

Exported from Caffeine project: MindCare AI

mindcare project where User put his/her daily thoughs and AI will give health suggestion on basis of thoughs

StressLytix is the project behind MindCare, an AI-powered stress detection application that analyzes voice, facial expressions, gestures, and text. Using multimodal ML.NET models, MindCare provides real-time stress analysis, alerts, and secure monitoring to support mental well-being.

This project was developed within the PIDEV - 3rd Year Engineering Program at Esprit School of Engineering (2025-2026). PI_3A43 (MindCare) is a web app for student mental health support, featuring appointment management, patient files, AI-based moderation/insights, and online consultation integration.

MindCare is an AI-powered voice analysis system designed to predict mental stress levels by analyzing human speech patterns. The project uses advanced audio signal processing and machine learning techniques to identify stress-related vocal features such as pitch, energy, and zero-crossing rate.

MindCare AI is an open-source project that builds an intelligent question-answering system to support mental well-being. Leveraging Agentic Retrieval-Augmented Generation (RAG) architecture, the system interacts naturally with users to provide trusted, empathetic, and context-aware answers based on verified mental health resources.

MindCare is an AI-based voice analysis system that predicts an individual’s mental stress level using speech signals. The project leverages audio signal processing and machine learning to analyze vocal features such as pitch, energy, and zero-crossing rate, which act as digital biomarkers for mental stress.

MindCare AI is a personal mental health companion designed to provide supportive conversations and helpful guidance. It combines voice and chat interactions to create a safe space for self-reflection. This project also served as a hands-on way to learn how OpenAI and generative AI models work in real applications. For privacy, the app only stores