On your Mbed-enabled development board

Impulses can be deployed as a C++ library. This packages all your signal processing blocks, configuration and learning blocks up into a single package. You can include this package in your own application to run the impulse locally. In this tutorial you'll export an impulse, and build an Mbed OS application to classify sensor data.
Knowledge required
This tutorial assumes that you're familiar with Mbed OS, and have installed Mbed CLI. If you're unfamiliar with these tools you can build binaries directly for your development board from the Deployment page in the studio.
Note: Are you looking for an example that has all sensors included? The Edge Impulse firmware for the ST IoT Discovery Kit has that. See edgeimpulse/firmware-st-b-l475e-iot01a.
Prerequisites
Make sure you followed the Continuous motion recognition tutorial, and have a trained impulse. Also install the following software:
​Mbed CLI - make sure mbed is in your PATH.
​GNU ARM Embedded Toolchain 9 - make sure arm-none-eabi-gcc is in your PATH.
Cloning the base repository
We created an example repository which contains a small Mbed OS application, which takes the raw features as an argument, and prints out the final classification. Import this repository using Mbed CLI:
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$ mbed import https://github.com/edgeimpulse/example-standalone-inferencing-mbed
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Deploying your impulse
Head over to your Edge Impulse project, and go to Deployment. From here you can create the full library which contains the impulse and all external required libraries. Select C++ library and click Build to create the library.
Download the .zip file and place the contents in the 'example-standalone-inferencing-mbed' folder (which you downloaded above). Your final folder structure should look like this:
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example-standalone-inferencing-mbed
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|_ Makefile
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|_ README.md
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|_ build.sh
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|_ edge-impulse-sdk
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|_ model-parameters
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|_ source
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|_ tflite-model
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Running the impulse
With the project ready it's time to verify that the application works. Head back to the studio and click on Live classification. Then load a validation sample, and click on a row under 'Detailed result'.
Selecting the row with timestamp '320' under 'Detailed result'.
To verify that the local application classifies the same, we need the raw features for this timestamp. To do so click on the 'Copy to clipboard' button next to 'Raw features'. This will copy the raw values from this validation file, before any signal processing or inferencing happened.
Copying the raw features.
Open main.cpp and paste the raw features inside the static const float features[] definition, for example:
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static const float features[] = {
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    -19.8800, -0.6900, 8.2300, -17.6600, -1.1300, 5.9700, ...
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};
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Then build and flash the application to your development board with Mbed CLI:
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$ mbed compile -t GCC_ARM -m auto -f
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Seeing the output
To see the output of the impulse, connect to the development board over a serial port on baud rate 115,200 and reset the board (e.g. by pressing the black button on the ST B-L475E-IOT01A. You can do this with your favourite serial monitor or with the Edge Impulse CLI:
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$ edge-impulse-run-impulse --raw
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This will run the signal processing pipeline, and then classify the output:
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Edge Impulse standalone inferencing (Mbed)
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Running neural network...
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Predictions (time: 0 ms.):
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idle:   0.015319
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snake:  0.000444
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updown: 0.006182
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wave:   0.978056
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Anomaly score (time: 0 ms.): 0.133557
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run_classifier_returned: 0
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[0.01532, 0.00044, 0.00618, 0.97806, 0.134]
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Which matches the values we just saw in the studio. You now have your impulse running on your Mbed-enabled development board!
Connecting sensors?
A demonstration on how to plug sensor values into the classifier can be found here: Data forwarder - classifying data (Mbed OS).

On your desktop computer

On your Zephyr-based Nordic Semiconductor development board

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Contents

Prerequisites

Cloning the base repository

Deploying your impulse

Running the impulse

Seeing the output

Connecting sensors?