Motion Sensor uses machine learning for accurate tracking
The LSM6DSOX iNEMO sensor integrates a machine-learning core to classify motion data based on known patterns. taking the first stage of activity tracking from the main processor saves energy and accelerates motion-based apps. The sensor has more internal memory than conventional sensors, and a high-speed I3C digital interface for longer periods between interactions with the main controller and shorter connection times, bringing energy savings.
LSM6DSOX contains a 3D MEMS accelerometer and 3D MEMS gyroscope, and tracks complex movements using the machine-learning core at low typical current consumption of just 0.55mA.
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 train the core for decision-tree based classification using Weka, an open-source PC-based application, to generate settings and limits from sample data.
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