Motion Sensor With Machine Learning For Activity Tracking

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Comes with embedded intelligence and additional features that improve battery life

STMicroelectronics, a global semiconductor leader serving customers across the spectrum of electronics applications, has integrated machine-learning technology into its advanced inertial sensors to improve activity-tracking performance and battery life in mobiles and wearables.

The LSM6DSOX iNEMO™ sensor contains a 3D MEMS accelerometer and 3D MEMS gyroscope that tracks complex movements using machine-learning core at a low current.

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The machine-learning core works in conjunction with the sensor’s integrated finite-state machine logic to handle motion pattern recognition or vibration detection. Customers creating activity-tracking products with the LSM6DSOX can program the core for using Weka, an open-source PC-based application, to generate settings and limits from sample data such as acceleration, speed, and magnetic angle that characterize the types of movements to be detected.

This activity of the main processor saves energy and accelerates motion-based apps such as fitness logging, wellness monitoring, personal navigation, and fall detection.

Andrea Onetti, Analog and MEMS Group, MEMS sensor, Vice President, STMicroelectronics says that, machine learning’s fast and efficient pattern recognition process greatly enhances LSM6DSOX motion sensor’s activity tracking capabilities in smartphones and wearables.

Key Features

  • “Always-on” user experience without trading battery runtime.
  • Power consumption: 0.55 mA.
  • Analog supply voltage: 1.71 V to 3.6 V.
  • More internal memory than conventional sensors.
  • High-speed I3C digital interface.
  • Shorter connection times for extra energy savings.

The sensor is easy to integrate with popular mobile platforms such as Android and iOS, simplifying use in smart devices for consumer, medical, and industrial markets.

Availability

The LSM6DSOX is in full production and available now, priced from US$2.50 for orders of 1000 pieces.

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