STMicroelectronics, leader in 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 embedded intelligence and additional enhancements greatly reduce power for longer battery runtime in smartphones, wearables, and game controllers.
With the Machine-learning technology classifies movement data to improve activity tracking. 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.
“Machine learning is already used for fast and efficient pattern recognition in social media, financial modelling, or autonomous driving,” said Andrea Onetti, Analog, MEMS and Sensors Group Vice President, STMicroelectronics. “The LSM6DSOX motion sensor integrates machine-learning capabilities to enhance activity tracking in smartphones and wearables.”
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 integrates with popular mobile platforms such as Android and iOS, simplifying use in smart devices for consumer, medical, and industrial markets.
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