This project is a low-cost, real-time system designed to detect signs of depression using physiological sensors, facial emotion recognition, and machine learning. It integrates Arduino and Raspberry Pi to collect and process data and display the result on an LCD screen.
- Heart rate and SpO₂ monitoring using MAX30102
- Stress detection via GSR sensor
- Motion tracking with MPU6050
- Real-time facial emotion detection (Happy, Neutral, Depressed)
- Depression prediction using machine learning
- LCD display for user feedback
- Modular and open-source
- Raspberry Pi 4
- Arduino Uno/Nano
- MAX30102 Sensor
- GSR Sensor
- MPU6050 Sensor
- DS3231 RTC Module (optional)
- Pi Camera Module
- I2C 16x2 LCD Display
- Jumper wires, breadboard, power supply
- Arduino IDE
- Python 3 (with numpy, pandas, scikit-learn, OpenCV, TensorFlow/Keras)
- RPLCD, PySerial libraries
- Arduino collects GSR data and sends it to Raspberry Pi.
- Raspberry Pi collects MAX30102 and MPU6050 data and captures images via the Pi Camera.
- A trained ML model predicts the depression state based on combined inputs.
- Result is displayed on the LCD in real-time.