A real-time IoT-based health monitoring system that detects early signs of panic attacks using physiological sensors and machine learning.
It helps users get timely alerts and insights through cloud-connected dashboards and mobile applications.
The Panic Attack Detection System combines hardware and software to continuously monitor vital signals like heart rate, body temperature, and GSR (Galvanic Skin Response). Data is analyzed using a Machine Learning model to predict panic episodes, with alerts sent to a mobile or web dashboard.
- 🫀 Real-time physiological monitoring (Pulse, Temperature, GSR)
- 📡 Wireless data transmission (Wi-Fi / Bluetooth)
- 🤖 ML-based panic attack prediction
- ☁️ Cloud integration for data logging and visualization
- 🔔 Instant mobile or web notifications
- 📊 Dashboard with live charts and history tracking
- Arduino / ESP32 / Raspberry Pi
- Pulse Sensor
- Temperature Sensor (LM35 / DS18B20)
- GSR Sensor
- Wi-Fi / Bluetooth Module
- Python / JavaScript
- Node.js / Express.js (Backend)
- React.js / Flutter (Frontend)
- MongoDB / Firebase (Database)
- Scikit-learn / TensorFlow (ML Model)
- OCI / AWS IoT Core (Cloud Integration)
- Sensors collect physiological signals.
- Microcontroller processes and transmits data via Wi-Fi.
- Backend receives and analyzes the data.
- ML model predicts panic likelihood in real time.
- Alerts are sent to mobile or dashboard.
- Integration with smartwatches for continuous monitoring
- Improved ML accuracy using live patient datasets
- Emergency contact or SMS alert system
# Clone the repository
git clone https://github.com/yourusername/panic-attack-detection-system.git
cd panic-attack-detection-system
# Install dependencies
npm install
# Run the backend server
npm start