In this article, we will focus on introducing ten micro:bit-based machine learning projects, covering aspects such as face recognition, speech recognition, pose recognition, object detection, object classification, and other image recognition. These projects fully demonstrate the enormous potential and wide applications of micro:bit in the AI field, hoping to bring inspiration and enlightenment to you!
I、Face recognition
1、The Musical Magic Mirror
Introduction: The project use the face recognition function of HUSKYLENS built-in machine learning technology to recognize faces and play music.
What do you need？
Hardware components：
2、Face-Following Robot
Introduction: Build a face following robot with the microbit and a Useful Sensors Person Sensor, a small, low-cost hardware module that detects nearby faces.
What do you need？
Hardware components：
Software components：
·Python micro:bit editor
Interested? You can find the complete project here: Build a Face-Following Robot
II、Speech recognition
3、MicroPal Guide
Introduction: Craft your own interactive microbit project that responds to your voice. Create a Teachable Machine Model, load it into the MicroPal website, and control the microbit via Web Bluetooth.
What do you need？
Hardware components：
Software components：
• Teachable Machines (for creating audio machine learning model)
• MicroPal App (for programming)
Interested? You can find the complete project here: MicroPal Guide [https://scientiffic.notion.site/scientiffic/MicroPal-Guide-141a70906ea5432599e21cecda2a1fac]
4、micro:bit AI vehicle
Introduction: A voice to controlled microbit vehicle using a Web App that recognizes 5 words and connects to the microbit via Web Bluetooth.
What do you need？
Hardware components：
Software components：
·p5.js
·ml5.js
·Microsoft MakeCode
Interested? You can find the complete project here: micro:bit AI vehicle
III、Pose Recogintion
5、Machine Learning Dance Move Detector
Introduction: Builds a system running on the micro:bit which can identify TikTok dance routines using the onboard accelerometer.
What do you need？
Hardware components：
Software components：
·Microsoft MakeCode
·Edge Impulse
Interested? You can find the complete project here:Machine Learning Dance Move Detector
6、micro:bit Gesture Recognizer
Introduction: An experimental gesture recognition tool using the micro:bit's accelerometer, built using ml5js, which is built on top of TensorFlow.js.
What do you need？
Hardware components：
Software components：
· Gesture Recognizer app
Interested? You can find the complete project here:micro:bit Gesture Recognizer
IV、Object Detection & Object Classification
7、Vending Machine for Stray Cats & Dogs
Introduction: This project uses object recognition function of HUSKYLENS to distinguish cats and dogs through machine learning. Then a microbit will be used to process the result and control the servo to open the valve, and deliver corresponding food to cats and dogs.
What do you need？
Hardware components：
Software components：
Interested? You can find the complete project here:Vending Machine for Stray Cats & Dogs
8、Safety Helmt Reminder
Introduction: This project uses the object classification function of HuskyLens to make a safety helmet reminder! Only those workers wearing safety helmets can enter the construction site, while others without helmets cannot.
What do you need？
Hardware components：
Software components：
Interested? You can find the complete project here:Vending Machine for Stray Cats & Dogs
V 、Other Image Recognition
9、Colorful Piano
Introduction: This project uses the color recognition function of HUSKYLENS to recognize different color keys and play different notes, so that your "playing" will be beautiful and pleasant, and also with absolutely wonderful stage effects.
What do you need？
Hardware components：
10、Energy Harvest
Introduction: This project mainly uses the tag recognition function of HUSKYLENNS. Each time HuskyLens recognizes a learned tag, a random number ranging from 0~9 will be generated, then judge the sum of the random number and perform corresponding operations.
What do you need？
Hardware components：
