Abstract

The Stress Detection Model Service is an AI-based system that identifies stress levels using data such as facial expressions, voice patterns, text sentiment, or physiological signals. By applying machine learning algorithms, the system analyzes these inputs to detect signs of stress in real time and classify them into different levels.

Unlike traditional self-assessment methods, this service provides continuous and objective monitoring, enabling early detection and timely intervention. It can be integrated into healthcare, workplace wellness, and mobile applications to promote mental well-being, improve productivity, and support proactive stress management.

Existing System

1. Self-reported stress assessments and surveys
2. Manual observation by professionals
3. Lack of real-time monitoring
4. Subjective and inconsistent results

Proposed System

1. Machine learning models for automated stress detection
2. Real-time analysis using facial, voice, or biometric data
3. API integration for wellness apps and healthcare systems
4. Early alerts and stress management recommendations

Software Requirements

1. Python
2. Libraries: OpenCV, TensorFlow/Keras, Scikit-learn, NumPy, Pandas
3. Jupyter Notebook / VS Code
4. Flask or FastAPI for service deployment

Hardware Requirements

1. Minimum 4 GB RAM (8 GB recommended)
2. Webcam or microphone (for facial/voice analysis)
3. Wearable sensors (optional for heart rate data)
4. Stable internet connection for API services