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MaanSick : A depression detection application
About This Project
MaanSick is a Final Year Capstone project — a depression detection application that combines real-time facial expression analysis and a structured psychological questionnaire to assess signs of depression in users, powered by machine learning.
How It Works
Users go through two parallel inputs: their facial expressions are captured live via webcam using OpenCV, while simultaneously filling out a structured quiz with mental health-related questions. Both inputs are processed and combined by ML classifiers to generate a result with personalized feedback.
Key Features
Facial Expression Analysis
Real-time webcam feed processed using OpenCV to detect and analyze facial expressions, extracting emotional cues as input features for the ML model.
Psychological Quiz
A structured in-app questionnaire capturing self-reported mental health indicators, designed to complement the visual analysis for a more accurate assessment.
ML Classification
Facial expression data and quiz responses are combined and passed through machine learning classifiers to predict depression indicators and severity levels.
Result & Feedback
Users receive a clear result along with personalized feedback and guidance based on their combined score from both inputs.
Full-Stack Interface
Responsive web interface built with ReactJS for a smooth user experience, backed by a Node.js API layer connecting the frontend to the Python ML pipeline.
