Finger Detection using Python & AI (Artificial Intelligence)

This project contributes to the growing field of AI-powered visual applications by combining real-time gesture recognition with hardware control. By utilizing MediaPipe for hand tracking and OpenCV for video processing, the system detects the number of fingers raised by the user and sends this information to a Raspberry Pi RP2040 microcontroller via serial communication. The microcontroller, in turn, controls LEDs based on the detected gesture. Such applications are a simplified yet practical demonstration of how AI can be used for responsive, real-world interactions, echoing the capabilities of more advanced systems like autonomous vehicles, drones, real-time video analytics, etc.

After completing this 10-hour course, students will gain the following skills and knowledge, explained in terms that are easy for parents to understand:

1. Real-Time Video Processing:
   Students will learn how to capture video using a camera and make the computer
   "see" things in the video. This is similar to how a smartphone camera can
   recognize faces or gestures. They will understand the process behind how it
   works, which is important for many future technologies like autonomous vehicles
   and security cameras.
   
   
2. Gesture Recognition Technology:
   By learning how to make a computer recognize hand movements and count fingers, students will understand the technology behind gesture control, which is similar to how body movements can control video games. They will learn how machines can interpret their hand gestures, a fundamental skill used in many modern smart devices.
   
   
3. Combining Hardware and Software:
   Students will learn how to transfer information processed by a computer (such as the number of fingers detected) to a small computer (like the Raspberry Pi RP2040) and provide feedback through LED lights. This process is similar to how smart devices work in everyday life, such as controlling lights or smart locks at home. This knowledge helps them understand how to integrate software with hardware, which is foundational for working with more complex systems in the future.
   
   
4. Building Simple Circuits:
   In the course, students will build a small circuit, connecting LED lights to the Raspberry Pi. The lights will turn on or off based on how many fingers are detected. This will give them an understanding of how many electronic devices at home (like remote controls or fans) work behind the scenes.
   
   
5. Python Programming Basics:
   Students will learn to use the Python programming language to handle video data and control hardware devices. Python is a simple yet powerful programming language widely used in fields like data analysis and artificial intelligence. Mastering this language will lay a strong foundation for learning other programming languages and preparing for future careers in technology.
   
   
6. Problem Solving and Debugging:
   Throughout the course, students may encounter issues, such as lights not turning on or the program not working correctly. They will learn how to identify and fix these problems step by step. This problem-solving ability is not only important in technology but also a valuable skill in future learning and life.
   
   
7. Artificial Intelligence and Machine Learning:
   Machine learning enables computers to improve their accuracy through experience and data. In this course, students will learn how computers gradually get better at recognizing gestures by continuously observing changes. This technology is commonly used in everyday life, such as in video recommendation systems or smart assistants like Alexa.
   
   
8. Computer Vision:
   Computer vision allows computers to "see" the world around them, much like human eyes. Students will learn how to use cameras and AI to help a computer identify objects and gestures in videos. This is similar to how autonomous cars "see" road conditions or how drones "see" obstacles and targets.
   
   
9. Embedded AI:
   Students will also learn about Embedded AI, which involves integrating artificial intelligence into small devices. This technology allows AI to run on limited resources, such as drones, smart home devices, and even wearables. Embedded AI enables these devices to make intelligent decisions without relying on a powerful computer. In this course, students will understand how to embed gesture recognition and AI algorithms into microcontrollers like the Raspberry Pi (learn raspberry pi python programming), enabling real-time decision-making and feedback.

Through finger detection / gesture recognition course, students will not only understand the fundamental concepts of artificial intelligence but also gain hands-on experience in applying this knowledge to real-world scenarios. Parents can be assured that these skills will not only help children grasp cutting-edge technology but also lay a solid foundation for future careers in the tech industry. This is a systematic learning kit to start with artificial intelligence program and create foundation in python machine learning to create more advance AI projects / AI real world applications.

• Raspberry Pi RP2040 with 20POS 0.1 inch Header pins x2 (Board with unique features manufactured by EIM Technology.  

• Breadboard

• 10 LEDs (Red, Green, Blue) 

• Jumper wires
  
  

• Illustrative Tutorial PDF
  

In general, the STEPico Core board is fully compatible with PICO2040 in terms of the pin functions, except that the MicroUSB was replaced to Type C and some buttons and LEDs were added making the board more user friendly for debugging. The pin diagram of STEPico-Core is shown:

Tutorial book will contain all the hardware and software configuration. We would also provide program code of the project 

• Install Required Libraries
• Initialize MediaPipe and Serial Communication
• Raspberry Pi RP2040 Configuration
• Capture and Process Video Input
• Send Data to Raspberry Pi RP2040
• Control LEDs Based on Finger Count
• Display Results
• Python Programming Development assistant

Learn Real-Time Finger Detection with MediaPipe and CNNs

• Hand Landmark Model
  

MediaPipe is an open-source framework developed by Google, designed for real-time perception tasks such as hand tracking, face detection, and object detection. It offers pre-trained models that can analyze input from video streams and recognize patterns, such as detecting hands, recognizing landmarks, and tracking gestures. The framework is optimized for efficiency, enabling real-time performance even on devices with limited processing power, such as mobile phones or laptops. For hand tracking, it uses an underlying CNN-based architecture to detect key points or "landmarks" on the hand and fingers.

MediaPipe Hand Detection Module uses CNNs to identify and track 21 hand landmarks. These landmarks represent key positions on the hand, such as fingertips and joints.

Through this project, students not only gain hands-on experience with gesture-based interfaces but also learn key concepts in Gesture Recognition, Real-Time Computer Vision, and hardware-software integration—all of which are critical in the development of cutting-edge technologies shaping industries today.

   

By the end of the project, the system will be able to:

• Detect and count the number of fingers raised by the user in real-time.

• Send this information to a Raspberry Pi via serial communication.

• Display the video feed with detected hand landmarks and an overlay indicating the number of fingers.

Engineering Applications of Artificial Intelligence

The learnings from this project enable you to apply it and create more advanced real-time AI Projects / Artificial Intelligence Projects.

• Drone
• Object tracking in auto-mobile
  
• Face Detection and many more....

We are excited to embark on a journey to design a comprehensive Artificial Intelligence Learning Kit collection. Our goal is to make AI learning accessible, engaging, and seamless for everyone, enabling users to explore AI concepts independently and apply them in real-world scenarios. These kits will not only complement online learning but also empower learners to create innovative AI-based applications with ease.

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