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STM32Cube function pack for ultra-low power IoT node with artificial intelligence (AI) application based on audio and motion sensing

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Product overview


FP-AI-SENSING1 is an STM32Cube function pack featuring examples that let you connect your IoT node to a smartphone via BLE and use a suitable Android™ or iOS™ application, like the STBLESensor app, to configure the device.

The package enables advanced applications such as human activity recognition or audio scene classification, on the basis of outputs generated by neural networks (NN). The NN are implemented by a multi-network library supporting both floating and fixed point arithmetics, generated by the X-CUBE-AI extension for STM32CubeMX tool.

The NN provided in this package are just examples of what can be achieved by combining the output of X-CUBE-AI with connectivity and sensing components from ST.

The package comes with an AI utility for data logging and annotation on SD card. You can record the data from the sensors and define which classes or events to record. With the recorded annotated data, you can train your own neural network on your PC/GPU/cloud, get the model, use X-CUBE-AI extension for STM32CubeMX tool for conversion, and then run it on the STM32 platform.

This package, together with the suggested combination of STM32 and ST devices, can be used to develop specific wearable AI applications, industrial predictive maintenance applications, smart things and building applications in general, where ultra-low power consumption is a key requirement.

The software runs on the STM32 microcontroller and includes all the necessary drivers for the STM32 Nucleo development board and expansion boards, as well as for the STEVAL-STLKT01V1 and STEVAL-MKSBOX1V1 evaluation boards and the B-L475E-IOT01A STM32L4 Discovery kit IoT node.

  • All features

    • Complete firmware to develop an IoT node with BLE connectivity, digital microphone, environmental and motion sensors, and perform real-time monitoring of sensors and audio data
    • Middleware library generated thanks to STM32CubeMX extension called X-CUBE-AI, featuring example implementation of neural networks for real-time human activity recognition (HAR) and acoustic scene classification (ASC) applications
    • Multi-network support: concurrent execution of several neural networks
    • AI utility for data logging and annotation on SD card or QSPI Flash memory
    • Ultra-low power implementation based on the use of an RTOS
    • Compatible with STBLESensor application for Android/iOS, to perform sensor data reading, audio and motion algorithm feature demo, and firmware update over the air (full and partial FOTA)
    • Sample implementation available for STEVAL-STLKT01V1 and STEVAL-MKSBOX1V1 evaluation boards, for B-L475E-IOT01A and for X-NUCLEO-CCA02M1, X-NUCLEO-IKS01A2 and X-NUCLEO-IDB05A1 connected to a NUCLEO-L476RG board
    • Easy portability across different MCU families, thanks to STM32Cube
    • Free, user-friendly license terms

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Artificial Intelligence overview

Optimize, benchmark, and generate AI Neural Network models for STM32