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tf4micro-motion-kit
Arduino Sketch and a Web Bluetooth API for loading models and running inference on the Nano Sense 33 BLE device.
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tiny-motion-trainer
Train and test machine learning models for your Arduino Nano 33 BLE Sense in the browser.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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To understand it better, I spent time diving into the open-source script tf4micro-motion-kit.js that is used as part of the project Tiny Motion Trainer. In this post, I am going to explain how a machine learning model can be transferred via bluetooth from the browser to an Arduino.
If you want to dive deeper into the entire code written for the Arduino sketch, you can find it in this repository and if you want to look at the code written to create the model, the Tiny Motion Trainer project is also open-source!
The Arduino Nano 33 BLE Sense is designed around a Nordic Semiconductor chip which has a Maximum Transmission Unit (MTU) of 23 bytes, which represents the largest packet size that can be sent at a time. According to this resource, the maximum data throughput for this size is 128 kbps so you need to split the machine learning model into packets of this size to be able to transfer it over to the Arduino.
The first time I experimented with TensorFlow.js for micro-controllers, I got really excited about the fact that a machine learning model was transferred via bluetooth to my Arduino. In just a few seconds gesture control was enabled on a website! My excitement quickly turned into curiosity; how does it actually work?