MEMS Sensors Ecosystem for Machine Learning

Overview
Sensors
Get Started
Tools and Software
Resources
Webinar
Partnership with Qeexo
 

The ST ecosystem for machine learning in MEMS and Sensors combines several hardware and software tools to help designers implement gesture and activity recognition with Artificial Intelligence at the Edge in sensors through machine learning algorithms based on decision tree classifiers.

IoT solutions developers can therefore deploy any of our sensors with machine learning core (MLC) in a rapid prototyping environment to quickly develop very low power Internet of Things (IoT) applications. Thanks to inherently low-power sensor design, advanced AI event detection, wake-up logic, and real-time Edge computing, MLC in a sensor reduces system data transfer volumes and offloads network processing. 

Added value:

  • Reduced power consumption
  • Increased accuracy (context detectability)
  • Edge to the Edge AI
machine learning artificial intelligence

Sensors with embedded machine-learning core

All ST MEMS and sensors with embedded machine learning core are marked with X at the end of the part number. Each sensor in the ecosystem offers different machine learning capabilities to provide developers with the extra flexibility they need to fulfil their deep edge AI computing design.

Part Number Application Family Machine Learning Core (MLC) Full Scale Temperature Range Power Consumption
LSM6DSOX Consumer iNEMO inertial modules (IMU) 256 nodes ±2000 dps, ±16 g -40°C + 85°C 0.55 mA combo
LSM6DSO32X Consumer iNEMO inertial modules (IMU) 256 nodes ± 2000 dps; ± 32 g -40°C + 85°C 0.55 mA combo
LSM6DSRX Consumer iNEMO inertial modules (IMU) 512 nodes ±4000 dps, ±16 g -40°C + 85°C 1.2 mA combo
ISM330DHCX Industrial iNEMO inertial modules (IMU) 512 nodes ±4000 dps, ±16 g -40°C + 105°C 1.2 mA combo
IIS2ICLX Industrial Accelerometer 512 nodes ±3 g -40°C + 105°C 0.42 mA

Machine learning core in ST sensors

Our latest generation of ST sensors with embedded machine learning core are built in three blocks.

machine learning sensor building blocks

The built-in sensors (accelerometer and gyroscope) filter real-time motion data before sending it to the Computation Block, where statistical parameters defined as “features” are applied to the captured data. The features aggregated in the computation block are then used as inputs for the third block. The Decision Tree evaluates the statistical parameters and compares them against certain thresholds to identify specific situations and generate classified results sent to the MCU.

 

Get Started: Build a decision tree with a Machine Learning supervised approach

 

ST’s MEMS sensors with machine learning cores offer a wide range of design possibilities for developers by allowing them to create their own embedded machine learning algorithms and to build the best decision tree for their application.

Decision Tree in Sensors Capture Data Label Data Build Decision Tree Embed Decision Tree Process New Data

Build a decision tree in five steps with our recommended tools

Collect data

The first step for any machine-learning classification is to collect a representative set of data for the motion-related application being modelled. Sensor data can be collected and labelled using various applications such as Unico-GUI, ST BLE Sensor app, or simply using the AlgoBuilderSuite, along with different hardware devices, depending on the selected sensor, such as the ProfiMEMS board (STEVAL-MKI109V3), SensorTile.Box, Nucleo-boards or STWIN.

Examples of physical parameters include acceleration, temperature, sound, pressure, and magnetic field, depending on your application.

Watch Step-By-Step Tutorial: Data Collection

Label & filters data and configuration features

Once the data is collected, a label is assigned to each statistical data pattern associated with an identified outcome; e.g., “jogging” or “failure mode”. The computation blocks (i.e., the filters and features) can then be configured. The features are statistical parameters computed from the input data (or from the filtered data) in a defined time window set by the user based on the specific application.

Watch Step-By-Step: Labeling and Features Extraction

Build the decision tree

Use a machine learning tool for data mining tasks (such as Unico-GUI, Weka, Rapidminer, Matlab, Python) to generate settings and identify limits in the training data set in order to build a decision tree which recognizes the type of motion data to be detected.

Watch Step-By-Step Tutorial: Device Tree Generation

Embed the decision tree in the MLC

Unico-GUI, Weka or similar tools then generate a configuration file that is uploaded into the sensor and you are ready to go.

Watch Step-By-Step Tutorial: Register and Configuration Testing

Process new data using a trained Decision Tree

Finally, when the device is programmed, the Machine Learning Core results can be processed using the defined trained Decision Tree in your application. 

To learn more about Decision Tree generation

DOWNLOAD DESIGN TIP

Tools and Software

The best way to get started with machine learning is to select the appropriate solution with supporting ST tools and software for your application.

The MEMS and Sensor machine learning ecosystem offer is structured around the following three targets:



Evaluate
Professional MEMS tool lets engineers monitor the behavior of ST MEMS sensors, which can help accelerate time to market and maximize the performance of new product designs.



Develop
The STM32 Open Development Environment offers an open, flexible, and easy way to develop MEMS-based applications by combining STM32 32-bit MCU family with MEMS sensors and other ST components connected via expansion boards.



Prototype
Our small form factor reference design kits simplify prototyping and testing of advanced consumer and industrial IoT applications based on motion and environmental sensor data.



EVALUATE

ST MEMS and Sensors evaluation kit includes 3 main components:

  • a professional motherboard based on a high-performance 32-bit microcontroller
  • a full set of adapter boards to evaluate any of ST’s MEMS sensors
  • an intuitive graphic user interface software package for real-time access to the sensor configuration registers and to perform sensor data analysis.
STEVAL-MKI109V3
STEVAL-MKI109V3
MEMS product evaluation board
Typical MEMS sensor’s adapter

Professional MEMS motherboard

Product evaluation adapter boards

Software


DEVELOP


The combination of STM32 Nucleo boards and expansion boards is a unified scalable approach with unlimited possibilities for any application development.

Graphical user interfaces (GUI) software is available with all the necessary functions to manage the machine learning development and sensor data analysis.

In addition, we offer a set of Function Packs that combine low-level drivers, middleware libraries and sample applications in single software packages. Functions Pack help to jump-start the implementation and the development of pre-integrated sensor application examples

STM32 Nucleo Expansion Board
STM32 Nucleo Expansion Board

STM32 Nucleo Expansion Boards

Software

Function Packs

PROTOTYPE

For quick prototyping, you can choose the ST form factor boards, ready-to-go development kits that simplify prototyping of advanced applications with little or no coding.

The boards are supported by graphical user interface (GUI) for sensor data analysis and bundled with a smartphone application. Function Packs with pre-integrated examples helps you to build custom applications.

Form Factor Boards


SensorTile.box

The latest STWIN

Software

Function Packs

RESOURCES

Following is a list of recommended technical documentation on Machine Learning Core.

00 Files selected for download

APPLICATION NOTES

  Description Action
AN5259
LSM6DSOX: Machine Learning Core
PDF
AN5393
LSM6DSRX: Machine Learning Core
PDF
AN5392
ISM330DHCX: Machine Learning Core
PDF
AN5536
IIS2ICLX: Machine Learning Core
PDF
AN5656
LSM6DSO32X: Machine Learning Core
PDF
AN5259

LSM6DSOX: Machine Learning Core

AN5393

LSM6DSRX: Machine Learning Core

AN5392

ISM330DHCX: Machine Learning Core

AN5536

IIS2ICLX: Machine Learning Core

AN5656

LSM6DSO32X: Machine Learning Core

USER MANUALS

  Description Action
UM1049
Unico GUI
PDF
UM1049

Unico GUI

TECHNICAL NOTES

  Description Action
TN0019
Surface mounting guidelines for MEMS sensors in a QFPN package
PDF
TN0019

Surface mounting guidelines for MEMS sensors in a QFPN package

DESIGN TIPS

  Description Action
Design Tip 139
Decision tree generation
PDF
Design Tip 141
How to import STMems_Standard_C_drivers in an STM32CubeIDE project
PDF
Design Tip 139

Decision tree generation

Design Tip 141

How to import STMems_Standard_C_drivers in an STM32CubeIDE project

FLYERS

  Description Action
ISM330DHCX
iNEMO 6-axis inertial module with Machine Learning Core for IIoT
PDF
LSM6DSOX
iNEMO 6-axis inertial module with Machine Learning core
PDF
LSM6DSRX
iNEMO 6-axis inertial module with Machine Learning core
PDF
IIS2ICLX
2-axis, high-accuracy inclinometer with embedded machine-learning
PDF
ISM330DHCX

iNEMO 6-axis inertial module with Machine Learning Core for IIoT

LSM6DSOX

iNEMO 6-axis inertial module with Machine Learning core

LSM6DSRX

iNEMO 6-axis inertial module with Machine Learning core

IIS2ICLX

2-axis, high-accuracy inclinometer with embedded machine-learning

PRESENTATIONS

  Description Action
iNEMO
inertial modules Tools and GUIs for ML
PDF
LSM6DSOX iNEMO
* inertial module: Evaluation tools and GUI for Machine Learning
PDF
ISM330DHCX tools and GUI
tools and GUI
PDF
LSM6DSRX iNEMO
inertial modules Tools and GUI for Machine Learning Core
PDF
IIS2ICLX
High-accuracy inclinometer - Advantages and benefits
PDF
iNEMO

inertial modules Tools and GUIs for ML

LSM6DSOX iNEMO

* inertial module: Evaluation tools and GUI for Machine Learning

ISM330DHCX

tools and GUI

LSM6DSRX iNEMO™

inertial modules Tools and GUI for Machine Learning Core

IIS2ICLX

High-accuracy inclinometer - Advantages and benefits

Machine Learning Resources​

Our MEMS and Sensors ecosystem for machine learning is constantly growing. There are several examples available in our ST Github repository.

GitHub MLC projects

In our ST MLC GitHub repository you will find a reference configuration example with comprehensive details regarding the Decision Tree building process. You'll find also application examples such as Human Activity Recognition, Gym Activity recognition, Head gestures, Vibration monitoring for predictive maintenance and more. To get started quickly with each example, the README file provides detailed information.

VIDEOS


WEBINARS

Event Location(s)
Program decision trees in sensors with a Machine Learning Core On demand webinar
Moving AI Deeper to the Edge using Sensors with Machine Learning Core On demand webinar


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