Gesture-Controlled LED System Using Hand Detection with OpenCV and Arduino

Here I’ve developed a gesture-controlled LED system that uses computer vision for hand detection with Arduino as a hardware control. By integrating OpenCV for real-time hand gesture recognition with an Arduino Mega 2560 board, the system allows users to control LEDs based on simple hand movements. This project demonstrates the potential of combining software-based computer vision with hardware for interactive control systems.

Components:

Arduino Mega 2560: Microcontroller for controlling the LED outputs.

Laptop with Camera: Captures real-time hand gestures for recognition.

OpenCV: Python-based computer vision library used for detecting hand gestures.

PySerial: Python library to communicate between Python and Arduino.

LEDs, USB Cable, Breadboard and Jumper Wires.

OpenCV is used as a platform for real-time image processing and computer vision tasks, making it ideal for detecting and tracking hand gestures with minimal lag. By utilizing OpenCV, we can eliminate the need for additional hardware like IR sensors or touch interfaces, streamlining the setup and enhancing flexibility. Its customizable nature allows for detecting various gestures, providing greater functionality in gesture-controlled systems.

Installing OpenCV via Bash (WSL):

To install OpenCV and PySerial, just type the following commands in order:

sudo apt update                 # Update the package list
sudo apt install python3        # Install Python 3 (if not already installed)
sudo apt install python3-pip    # Install pip for Python 3
pip3 install opencv-python      # Install OpenCV for Python
pip3 install pyserial           # Install PySerial for Arduino communication

You can download the Arduino C++ code and Python code for OpenCV from my GitHub repository:

This project demonstrates the flexibility and efficiency of computer vision and paves the way for applications, such as home automation and assistive technology, and enhancing user experience in a meaningful way.

I chose this project because it shows the power of integrating software-driven gesture recognition with hardware control systems and by utilising OpenCV for real-time hand detection, I was able to create an intuitive method for controlling LEDs with simple gestures, eliminating the need for additional hardware such as sensors, which promotes an environmentally friendly approach.