Arduino Knowledge Pack Example for Nano33 BLE Sense

Example application code for running a SensiML Knowledge Pack on Arduino boards, for the Nano33 BLE Sense

Choosing the correct SensiML hardware platform

In order to run a Knowledge Pack, you must first have a model generated via SensiML Analytics Studio.

When downloading a model, you will need to select Arduino Cortex M4 ARM GCC 8.2.1 from the target device.

This will give you the option to build a library configured properly for a Cortex-M4 based Arduino device.

Running your downloaded model

Unzip the download to a location of your choosing. In the folder, you will find the following directories:

knowledgepack/
- knowledgepack_project/
- libsensiml/

Copy the files from libsensiml to lib/sensiml. It is ok to overwrite these, but do not commit them in a pull request. The files provided in this repository are for examples only.

Copy the files from knowledgepack_project to src/ This should be sml_recognition_run.cpp, which will contain the necessary model running code to enable recognition on the device.

Build/upload the firmware.

Adjusting IMU Frequency

If you wish to run the IMU at a different frequency (default for data capture and this example is 119 Hz), modify the ACCEL_GYRO_DEFAULT_ODR in include/sensor_config.h As the Data Capture Lab of the SensiML Toolkit does not actively configure the device, a capture configuration isn't generated when creating the model code.

Viewing Model Output

For Serial output, simply connect to your Nano33 via any serial monitoring application. You should see an output similar to this:

{"ModelNumber":0,"Classification":2,"FeatureLength":33,"FeatureVector":["2","0","253","252","93","217","0","0","0","0","0","0","205","221","255","183","0","0","0","1","1","0","0","1","0","1","0","0","2","0","4","145","19"]}

Using TensorFlow Lite for Microcontrollers in a Knowledge Pack