Motion Sensor With Machine Learning For High-Accuracy IoT

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Embedded intelligence and additional enhancements greatly reduce power for longer battery runtime in smartphones, wearables, and game controllers

STMicroelectronics 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 machine-learning core to classify motion data based on known patterns. Relieving this first stage of activity tracking from the main processor saves energy and accelerates motion-based apps such as fitness logging, wellness monitoring, personal navigation, and fall detection.

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Devices equipped with ST’s LSM6DSOX can deliver a convenient and responsive “always-on” user experience without trading battery runtime. The sensor also has more internal memory than conventional sensors, and a state-of-the-art high-speed I3C digital interface, allowing longer periods between interactions with the main controller and 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.

Key Features

  • Power consumption: 0.55 mA in combo high-performance mode
  • Analog supply voltage: 1.71 V to 3.6 V
  • Independent IO supply (1.62 V)
  • Significant Motion Detection, Tilt detection
  • Embedded temperature sensor
  • Machine Learning Core

Availability

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

For more information, click here

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