The configuration_examples section of the Machine Learning Core repository contains examples showing how to configure the MLC using different tools provided by ST.
The purpose of these examples is not the final application (dedicated application examples are provided for final applications). The focus is on the process to configure the MLC, starting from data logs and following all the MLC configuration steps.
Each folder contains a different configuration procedure depending on the software and hardware used:
| Example | Product | Hardware | GUI/App | Machine Learning Tool | Use case |
|---|---|---|---|---|---|
| example_0 | LSM6DSOX | Professional MEMS Tool (STEVAL-MKI109V3) | Unico GUI | Unico GUI / Weka | Face-up / Face-down / Shaking |
| example_1 | LSM6DSOX | SensorTile.box | STBLESensor | Unico GUI | Yoga pose recognition |
| example_2 | LSM6DSOX | SensorTile.box | AlgoBuilder GUI | Unico GUI | Motion intensity |
| example_3 | LSM6DSRX | STM32 Nucleo | Unicleo GUI | Unico GUI / MATLAB | Face-up / Face-down + Motion intensity |
| example_4 | ISM330DHCX | STWIN | STBLESensor | Unico GUI | Fan rack monitoring |
| example_5 | LSM6DSOX | Professional MEMS Tool (STEVAL-MKI109V3) | Unico GUI | Unico GUI | External sensor data in MLC (LIS2MDL magnetometer) |
| example_6 | LSM6DSOX | SensorTile.box | Unicleo GUI | Unico GUI | MLC + FSM |
| example_7 | ASM330LHHX | Professional MEMS Tool (STEVAL-MKI109V3) | Unico GUI | Unico GUI | Vehicle monitoring |
| example_8 | LSM6DSV16X | Professional MEMS Tool (STEVAL-MKI227KA) | MEMS Studio | Scikit-learn (Python) | Human activity recognition |
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