https://github.com/alvarowolfx/tinyml-smart-tennis-sensor
Run Machine learning on a microcontroller with an accelerometer sensor to classify different moves with the racket - Forehand, Backhand, Serve, and Idle.
https://github.com/alvarowolfx/tinyml-smart-tennis-sensor
edge-impulse tinyml
Last synced: about 1 year ago
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Run Machine learning on a microcontroller with an accelerometer sensor to classify different moves with the racket - Forehand, Backhand, Serve, and Idle.
- Host: GitHub
- URL: https://github.com/alvarowolfx/tinyml-smart-tennis-sensor
- Owner: alvarowolfx
- Created: 2020-11-26T20:39:36.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2020-11-26T20:44:45.000Z (over 5 years ago)
- Last Synced: 2025-03-31T14:57:32.004Z (about 1 year ago)
- Topics: edge-impulse, tinyml
- Language: C
- Homepage:
- Size: 11.5 MB
- Stars: 11
- Watchers: 2
- Forks: 1
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
# TinyML Smart Tennis Racket
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](https://edgeimpulse.com).
Demo video of the collecting data:
- [Link on Twitter](https://twitter.com/alvaroviebrantz/status/1318211437681135616)
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](https://twitter.com/alvaroviebrantz/status/1320463069575847936)
️ ⚠️️️️**THIS IS A WORK IN PROGRESS** ⚠️
### TODO
- Collect more data and with different people
- Collect `Serve` data
- Make data available for others to use
## Bom - Bill of Material
- Particle Xenon and/or nRF52840 Dongle
- I installed the [Adafruit nRF52 Bootloader](https://github.com/adafruit/Adafruit_nRF52_Bootloader)
- I used the Particle Debugger and openocd to flash the bootloader.
- MPU 6500 Accelerometer Module
## Upload firmware with PlatformIO
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.
## Web Interface with Bluetooth
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
```
### References
- 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