AI-Based Stress Detection Module

This repository contains the Stress Detection Module, a key component of the AI-Based Stress Detection System for University Students. This module is responsible for analyzing facial expressions and determining whether a student is stressed or not. It uses deep learning techniques to classify emotions and assess stress levels in real time.

📌 Features

• Facial Expression Analysis – Uses convolutional neural networks (CNNs) to classify emotions.
• Stress Classification – Determines if a student is stressed or not stressed based on facial expressions.
• Real-time Prediction – Receives images via API and returns stress predictions instantly.
• Integration Ready – Designed to work seamlessly with the Face Detection and Frontend modules.

🛠️ Technologies Used

• Python
• TensorFlow / Keras
• OpenCV
• Flask
• NumPy
• Base64 Encoding

🚀 How It Works

1. Receives an image (base64-encoded) from the frontend.
2. Preprocesses the face and feeds it into the trained CNN model.
3. Predicts the emotion (angry, happy, neutral, sad, etc.).
4. Maps emotions to stress states (e.g., "angry," "sad," "fear" → stressed).
5. Returns the predicted label and stress state via API response.

📂 Project Structure

/stress-detection
│── stressdetector.json  # Model architecture
│── stressdetectorV1.h5  # Pre-trained model weights
│── app.py  # Flask API for stress prediction
│── requirements.txt  # Dependencies
│── README.md  # Documentation

🔧 Installation

1️⃣ Clone the Repository

git clone https://github.com/yourusername/stress-detection-module.git
cd stress-detection-module

2️⃣ Create a Virtual Environment

python -m venv venv
source venv/bin/activate  # On Mac/Linux
venv\Scripts\activate  # On Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Run the Stress Detection API

python app.py

📡 API Endpoints

🎭 Predict Stress Level

• URL: /predict
• Method: POST
• Request Body:

  {
    "image": "base64_encoded_image_string"
  }

• Response:

  [
    {
      "label": "sad",
      "state": "stressed",
      "box": [x, y, width, height]
    }
  ]

🛠 Future Improvements

• Improve model accuracy with larger datasets.
• Enhance multi-face detection capabilities.
• Integrate with student support services for automated interventions.

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📌 Related Modules:

• Frontend Module → GitHub Repo
• Face Detection Module → GitHub Repo

🔗 Project Maintainer: Nimtharu Jayasekara