Arduino Nano 33 BLE Sense
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The Arduino Nano 33 BLE Sense is a tiny development board with a Cortex-M4 microcontroller, motion sensors, a microphone and BLE - and it's fully supported by Edge Impulse. You'll be able to sample raw data, build models, and deploy trained machine learning models directly from the studio. It's available for around 30 USD from Arduino and a wide range of distributors.
You can also use the Arduino Tiny Machine Learning Kit to run image classification models on the edge with the Arduino Nano and attached OV7675 camera module (or connect the hardware together via jumper wire and a breadboard if purchased separately).
The Edge Impulse firmware for this development board is open source and hosted on GitHub: edgeimpulse/firmware-arduino-nano-33-ble-sense.
Arduino Nano 33 BLE Sense rev2?
Arduino recently released a new version of the Arduino Nano 33 BLE Sense, the rev2 version which has different sensors than the original version. We are working on adding a dedicated "official firmware" so you can easily flash this board version. In the meantime, to ingest data, please have a look at Data Ingestion (API), Data Forwarder (CLI) or Data Uploader (CLI and Studio)
To set this device up in Edge Impulse, you will need to install the following software:
Here's an instruction video for Windows.
The Arduino website has instructions for macOS and Linux.
On Linux:
GNU Screen: install for example via sudo apt install screen.
Problems installing the CLI?
See the Installation and troubleshooting guide.
With all the software in place it's time to connect the development board to Edge Impulse.
Use a micro-USB cable to connect the development board to your computer. Then press RESET twice to launch into the bootloader. The on-board LED should start pulsating to indicate this.
The development board does not come with the right firmware yet. To update the firmware:
Download the latest Edge Impulse firmware, and unzip the file.
Open the flash script for your operating system (flash_windows.bat, flash_mac.command or flash_linux.sh) to flash the firmware.
Wait until flashing is complete, and press the RESET button once to launch the new firmware.
From a command prompt or terminal, run:
This will start a wizard which will ask you to log in, and choose an Edge Impulse project. If you want to switch projects run the command with --clean.
Alternatively, recent versions of Google Chrome and Microsoft Edge can collect data directly from your development board, without the need for the Edge Impulse CLI. See this blog post for more information.
That's all! Your device is now connected to Edge Impulse. To verify this, go to your Edge Impulse project, and click Devices. The device will be listed here.
With everything set up you can now build your first machine learning model with these tutorials:
Looking to connect different sensors? The Data forwarder lets you easily send data from any sensor into Edge Impulse.
It probably means you don't have Rosetta 2 installed yet (which allows Intel-based apps to run on M1 chips).
The error looks like the following:
To install Rosetta 2 you can run this command:
You will need the following hardware:
Arduino Nano 33 BLE Sense board with headers.
OV7675 camera module.
Micro-USB cable.
Solderless breadboard and female-to-male jumper wires.
First, slot the Arduino Nano 33 BLE Sense board into a solderless breadboard:
With female-to-male jumper wire, use the following wiring diagram, pinout diagrams, and connection table to link the OV7675 camera module to the microcontroller board via the solderless breadboard:
Download the full pinout diagram of the Arduino Nano 33 BLE Sense here.
Finally, use a micro-USB cable to connect the Arduino Nano 33 BLE Sense development board to your computer.
Now build & train your own image classification model and deploy to the Arduino Nano 33 BLE Sense with Edge Impulse!
