This example runs an impulse on the STEVAL-BCN002V1 multi-sensor board, classifying data in real-time from the LSM6DSO IMU. An example Edge Impulse project can be found here: https://studio.edgeimpulse.com/public/48988/latest - you can clone this project and use the data forwarder to add new data, or train new gestures.

• Continuous Motion Recognition - Gestures - ST BlueTile Low Memory Edge Impulse project: https://studio.edgeimpulse.com/public/48988/latest
• Fan Monitoring - Advanced Anomaly Detection - ST BlueTile Low Memory Edge Impulse project: https://studio.edgeimpulse.com/public/111797/latest

Note: This is an example, and not a fully supported development board.

Note: This inference test repo only utilizes the accelerometer (LSM6DSO) sensor onboard the STEVAL-BCN002V1, to create an inference application using one of the other sensors, you will need to add the driver code from the STSW-BLUETILE-DK SDK into the mbed-os target folder and update the sensor init code in main.cpp.

To update the existing Edge Impulse continuous motion recognition library in this repository with a new accelerometer Edge Impulse C++ library deployment, follow these instructions:

1. Delete the following directories: edge-impulse-sdk, model-parameters, tflite-model
2. Extract the .zip file of your new Edge Impulse C++ library and copy/paste the following directories into the root of this project: edge-impulse-sdk, model-parameters, tflite-model
3. Change the following code in edge-impulse-sdk/porting/mbed/ei_classifier_porting.cpp from:

   /**
    * Cancelable sleep, can be triggered with signal from other thread
    */
   EI_WEAK_FN EI_IMPULSE_ERROR ei_sleep(int32_t time_ms) {
   #if MBED_VERSION >= MBED_ENCODE_VERSION((5), (11), (0))
       rtos::ThisThread::sleep_for(time_ms);

   to...

   /**
    * Cancelable sleep, can be triggered with signal from other thread
    */
   EI_WEAK_FN EI_IMPULSE_ERROR ei_sleep(int32_t time_ms) {
   #if MBED_VERSION >= MBED_ENCODE_VERSION((5), (11), (0))
       wait_ms(time_ms);

Now you are ready to build and flash your new accelerometer Edge Impulse machine learning application to the STEVAL-BCN002V1!

1. Install the Mbed CLI.

2. Install the GNU Arm Embedded Toolchain 7.

3. Configure the Mbed CLI, and point it to the toolchain:

   $ echo 'ROOT=.' > .mbed
   $ mbed config GCC_ARM_PATH ~/toolchains/gcc-arm-none-eabi-7-2018-q2-update/bin/
   

4. Build the project:

   $ mbed compile -t GCC_ARM -m STEVAL_IDB008V2 --profile=develop.json
   

5. Install the STSW-BNRGFLASHER (Windows only).

6. Open the flash application, and flash ./BUILD/STEVAL_IDB008V2/GCC_ARM-DEVELOP/bluenrg-inference-test.bin to address 0x10040000 and check "Entire Memory".

7. Open a serial terminal to the device on baud rate 9600 to see the inference results.

This work is based on an Mbed OS port for the BlueTile by ntoni92.