Smart Radiator System with Hand Gesture Control

This project demonstrates an IoT smart radiator control system that use hand gesture recognition to optimise thermal management in residential household.

Features

• Automated Radiator Valve Control: Use a servo motor mounted on the radiator valve to automate the temperature control process.
• Hand Gesture Recognition: Incorporates a hand-free control method using hand gestures, provides convenience and encourages energy-saving.
• Remote Monitoring and Control: A dashboard interface allows users to monitor real-time temperature data and control the system remotely.
• IoT Integration: The system uses MQTT for communication between devices, showcasing the potential of IoT in smart home applications.

Hardware Components

• Raspberry Pi 5: Serves as the central controller, hosting the Node-RED software, MQTT broker, and hand gesture recognition software.
• ESP32 Microcontroller: Acts as a remote sensor and actuator node, collecting temperature data and controlling the radiator valve.
• Temperature Sensors (DHT11, DS18B20): Monitor room temperature and provide data for the control system.
• Servo Motor (SG90): Controls the radiator valve to regulate the flow of hot water.
• Ultrasonic Sensor (HC-SR04) and Gyroscope Sensor (MPU6050): Detect door events (opening/closing) to trigger hand gesture recognition.
• Camera (Logitech C525): Captures hand gestures for interpretation and control.
• OLED Display and LCD Display: Provide visual feedback on system status and sensor readings.

Software Components

• Node-RED: Implements the logic, handling sensor data, gesture recognition results, and control commands.
• Mosquitto MQTT Broker: Data exchange between the Raspberry Pi and ESP32.
• MediaPipe: Enables hand gesture recognition using machine learning models.
• OpenCV: Processes images from the camera for gesture detection.
• Python: Implements the smart door system and gesture recognition functionality.
• Arduino IDE and C++: Used for ESP32 development.

Setup and Installation

1. Clone this repository to your Raspberry Pi.
2. Install the required libraries and dependencies (refer to the requirements.txt file).
3. Configure the MQTT broker and connect the ESP32 to your Wi-Fi network.
4. Calibrate the sensors for accurate door event detection.
5. Load the hand gesture recognition model.
6. Run the Python script for the smart door system and the Node-RED flow.
7. Access the Node-RED dashboard to monitor and control the system.

Future Enhancements

• Integrate with a mobile robotic platform for enhanced environmental monitoring and user interaction.
• Improve hand gesture recognition accuracy in dark lighting conditions.
• Implement a dedicated door sensor to simplify installation process.

Contributing

Contributions are welcome! Feel free to submit bug reports, feature requests, or pull requests.

License

This project is licensed under the MIT License.

Acknowledgments

• The authors would like to acknowledge the contributions of the open-source communities behind MediaPipe, OpenCV, Mosquitto MQTT, and Node-RED.