MEMS sensors with an ecosystem for machine learning


What is a MEMS?

MEMS means Micro Electro Mechanical System MEMS contain movable 3-D Structures. STMirocelectronics has a large portfolio of MEMS sensors with an ecosystem for machine learning. These sensors are characterized by reduced energy consumption and increased accuracy (context detectability).

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 in sensors through machine learning algorithms based on decision tree classifiers.

IoT solutions developers can therefore deploy any of 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.

Figure 1: Sensors with embedded machine-learning core.

The latest generation of ST sensors with an embedded machine learning core consists of 3 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.

Figure 2: Machine learning core in ST sensors.

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.

Creating a decision tree can be divided into 5 stages:

  1. Collecting data from sensors with the support of Unicleo-GUI, STBLESensor, Unico-GUI applications.
  2. Labeling & filtering of data and configuration functions using Unico-GUI.
  3. Building a decision tree with the use of Unico-GUI.
  4. Implementation of a decision tree using Unico-GUI.
  5. Processing new data using Unicleo GUI, STBLESensor, Unico-GUI, AlgoBuilder.

Figure 3: Decision tree creation proces.

You can find more about the procedure for creating a decision tree in this link.

MEMS brochure: ST21261_flmemsind0421_lr

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