Run Machine learning on a micro controller with an accelerometer sensor to classify different moves with the racket - Forehand, Backhand, Serve and Idle.

Data is collected using the tinyml-tennis-collector firmware, that sends data over BLE and there is a Web UI on web-bluetooth-bridge-ui folder that relays the data to Edge Impulse.

Demo video of the collecting data:

• Link on Twitter

The model was trained on Edge Impulse and exported to be used on the tinyml-tennis-classifier firmware, that them shows a different LED color depending on the class - idle (red), forehand(green), backhand(blue)

Demo video of the classification:

• Link on Twitter

️ ⚠️️️️THIS IS A WORK IN PROGRESS ⚠️

• Collect more data and with different people
• Collect Serve data
• Make data available for others to use

• Particle Xenon and/or nRF52840 Dongle
  • I installed the Adafruit nRF52 Bootloader
  • I used the Particle Debugger and openocd to flash the bootloader.
• MPU 6500 Accelerometer Module

I recommend installing the Visual Studio Code (VSCode) IDE and the PlatformIO plugin to get started using it. Just follow the step on the link below:

https://platformio.org/platformio-ide

To deploy to the board, just open the tinyml-tennis-classifer or tinyml-tennis-collector folder and you can use the “Build” and “Upload” buttons on PlatformIO Toolbar. All libraries and dependencies will be downloaded.

You need to generate an API Key/Secret pair to send data to Edge Impulse.

• Run on the command line:

cd web-bluetooth-bridge-ui
npm install
npm start

• https://www.tensorflow.org/lite/microcontrollers
• https://blog.arduino.cc/2019/10/15/get-started-with-machine-learning-on-arduino/
• https://medium.com/@devdevcharlie/play-street-fighter-with-body-movements-using-arduino-and-tensorflow-js-6b0e4734e118