FP-AI-MONITOR1

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STM32Cube function pack for ultra-low power STM32 with artificial intelligence (AI) monitoring application based on a wide range of sensors

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Product overview

Description

The FP-AI-MONITOR1 function pack helps to jump-start the implementation and development for sensor-monitoring-based applications designed with the X-CUBE-AI Expansion Package for STM32Cube or with the NanoEdge™ AI Studio . It covers the entire design of the Machine Learning cycle from the data set acquisition to the integration on a physical node. X-CUBE-AI is an STM32Cube Expansion Package part of the STM32Cube.AI ecosystem and extending STM32CubeMX capabilities with automatic conversion of pre-trained Neural Network or Machine Learning models and integration of generated optimized library into the user's project. The X-CUBE-AI Expansion Package offers also several means to validate AI models both on desktop PC and STM32, as well as measure performance on STM32 devices without user handmade ad hoc C code. The support vector classifier used for human activity recognition (HAR) example is generated by X-CUBE-AI. Other applications can be created using ML and DNN code generated by X-CUBE-AI.
NanoEdge™ AI Studio (NanoEdgeAIStudio) simplifies the creation of autonomous Machine Learning libraries with the possibility of running training on target and inference on the edge. For instance, condition-based monitoring applications using vibration and motion data can be created easily by re-compiling the function pack with NanoEdge™ AI anomaly detection models.
FP-AI-MONITOR1 runs learning session and the inference in real time on an STM32L4R9ZI ultra-low-power microcontroller (Arm® Cortex®-M4 at 120 MHz with 2 Mbytes of Flash memory and 640 Kbytes of SRAM), taking physical sensor data as input. The SensorTile wireless industrial node (STEVAL-STWINKT1B) embeds industrial-grade sensors, including 6-axis IMU, 3-axis accelerometer and vibrometer to record any inertial and vibrational data with high accuracy at high frequencies.
The NanoEdge™ AI library generation itself is out of the scope of this function pack and must be generated using NanoEdge™ AI Studio (NanoEdgeAIStudio).
FP-AI-MONITOR1 implements a wired interactive CLI to configure the node, and manage the learn and detect phases. For simple operation in the field, a standalone battery-operated mode allows basic controls through the user button, without using the console.
  • All features

    • Complete firmware to program an STM32L4+ sensor node for sensor-monitoring-based applications on the STEVAL-STWINKT1B SensorTile wireless industrial node
    • Runs classical Machine Learning (ML) and Artificial Neural Network (ANN) models generated by the X-CUBE-AI, an STM32Cube Expansion Package
    • Runs NanoEdge™ AI libraries generated by NanoEdge™ AI Studio (NanoEdgeAIStudio) for AI-based sensing applications. Easy integration by replacing the pre-integrated stub
    • Application example of human activity classification based on motion sensors
    • Application binary of high-speed datalogger for STEVAL-STWINKT1B data record from any combination of sensors and microphones configured up to the maximum sampling rate on a microSD™ card
    • Sensor manager firmware module to configure any board sensors easily, and suitable for production applications
    • eLooM (embedded Light object-oriented fraMework) enabling efficient development of soft realtime, multi-tasking, event-driven embedded applications on STM32L4+ Series microcontrollers
    • Digital processing unit (DPU) firmware module providing a set of processing blocks, which can be chained together, to apply mathematical transformations to the sensors data
    • Configurable autonomous mode controlled by user button
    • Interactive command-line interface (CLI):Node and sensor configurationConfigure application running either an X-CUBE-AI ML or ANN model, or a NanoEdge™ AI Studio (NanoEdgeAIStudio) model with learn-and-detect capability
    • Easy portability across STM32 microcontrollers by means of the STM32Cube ecosystem
    • Free and user-friendly license terms

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