STMicroelectronics has an ecosystem for machine learning (ML) in MEMS and sensors, which combines several hardware and software tools to help designers implement gesture and activity recognition with artificial intelligence (AI) at the edge. This is done on sensors through ML algorithms based on decision tree classifiers.
IoT solutions developers can deploy any of ST’s sensors with machine learning core (MLC) in a rapid prototyping environment to quickly develop very low power 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 processing and data transfer loads on the main system.
ST’s latest generation of sensors with embedded MLC are built in three blocks:
• Sensor data block – The built-in sensors (accelerometer and gyroscope) filter real-time motion data before sending it to the computation block.
• Computation block - 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 decision tree block.
• Decision tree block – This block evaluates the statistical parameters and compares them against certain thresholds to identify specific situations and generate classified results sent to the MCU.
ST’s MEMS sensors with MLCs offer a wide range of design possibilities for developers by allowing them to create embedded machine learning algorithms and to build the best decision tree for a particular application.
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