AI-powered Real-Time Stress Detection System
Budget: ₹1,500 – ₹12,500 INR
Stress Detection System (AI + Computer Vision)
Project Overview:
I developed an AI-powered Real-Time Stress Detection System that uses a standard webcam to analyze a person’s emotional state and physiological signals. The system combines Facial Emotion Recognition (FER) and remote Photoplethysmography (rPPG) to generate an accurate stress score in real time.
What the System Does:
Captures live video through a webcam or mobile camera
Detects and tracks the face using AI
Identifies emotions such as anger, fear, sadness, happiness, neutral, disgust
Extracts heart rate (BPM) from facial color variations using rPPG
Calculates a combined stress level (Low / Medium / High)
Displays live output on the screen
Optionally triggers alerts or stores stress logs for analysis
Technologies Used:
Python
OpenCV
MediaPipe / DeepFace (for facial emotion detection)
rPPG algorithms (CHROM / POS) for heart-rate estimation
NumPy / SciPy
Streamlit or Flask (optional front-end UI)
Key Features:
Real-time Facial Emotion Recognition
Contactless heart-rate estimation
Combined stress scoring algorithm
High accuracy due to multi-parameter analysis
Can be deployed as a web app, mobile app, or desktop application
Suitable for corporate wellness, student monitoring, driver stress detection, healthcare applications, and academic research
Why This Project Matters:
This system helps identify stress early, enabling better mental wellness, productivity, safety, and decision-making. It serves as an affordable, contactless stress assessment solution that works using just a standard camera, without the need for specialized hardware.
Research Contribution:
This project has also been submitted as a conference paper, demonstrating its research potential and real-world applicability in AI-driven mental health monitoring.
Project Overview:
I developed an AI-powered Real-Time Stress Detection System that uses a standard webcam to analyze a person’s emotional state and physiological signals. The system combines Facial Emotion Recognition (FER) and remote Photoplethysmography (rPPG) to generate an accurate stress score in real time.
What the System Does:
Captures live video through a webcam or mobile camera
Detects and tracks the face using AI
Identifies emotions such as anger, fear, sadness, happiness, neutral, disgust
Extracts heart rate (BPM) from facial color variations using rPPG
Calculates a combined stress level (Low / Medium / High)
Displays live output on the screen
Optionally triggers alerts or stores stress logs for analysis
Technologies Used:
Python
OpenCV
MediaPipe / DeepFace (for facial emotion detection)
rPPG algorithms (CHROM / POS) for heart-rate estimation
NumPy / SciPy
Streamlit or Flask (optional front-end UI)
Key Features:
Real-time Facial Emotion Recognition
Contactless heart-rate estimation
Combined stress scoring algorithm
High accuracy due to multi-parameter analysis
Can be deployed as a web app, mobile app, or desktop application
Suitable for corporate wellness, student monitoring, driver stress detection, healthcare applications, and academic research
Why This Project Matters:
This system helps identify stress early, enabling better mental wellness, productivity, safety, and decision-making. It serves as an affordable, contactless stress assessment solution that works using just a standard camera, without the need for specialized hardware.
Research Contribution:
This project has also been submitted as a conference paper, demonstrating its research potential and real-world applicability in AI-driven mental health monitoring.