ST embeds machine learning in 6DoF motion sensor

February 13, 2019 //By Peter Clarke
ST embeds machine learning in 6DoF motion sensor
STMicroelectronics NV (Geneva, Switzerland) has integrated machine-learning capability into inertial sensors to improve activity-tracking performance and battery life in mobiles and wearables.

The LSM6DSOX contains a 3D MEMS accelerometer and 3D MEMS gyroscope, and tracks complex movements using the machine-learning core at a typical current consumption of 0.55mA to minimize load on the battery.

The machine learning technology classifies movement data to improve activity tracking. The sensor has more internal memory than conventional sensors, and an I3C digital interface, allowing longer periods between interactions with the main controller.

Moving this first stage of activity track off the main processor and to the peripheral device saves energy and accelerates motion-based apps such as fitness logging, wellness monitoring, personal navigation, and fall detection.

The machine-learning core works 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 train the core for decision-tree based classification 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.

Support for free-fall, wakeup, 6D/4D orientation, click and double-click interrupts allows a wide variety of applications such as user-interface management and laptop protection in addition to activity tracking.

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

Related links and articles:

www.st.com

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