STMicroelectronics has integrated machine-learning 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.
“Machine learning is already used for fast and efficient pattern recognition in social media, financial modelling, or autonomous driving. The LSM6DSOX motion sensor integrates machine-learning capabilities to enhance activity tracking in smartphones and wearables,” said Andrea Onetti, Analog, MEMS and Sensors Group Vice President, STMicroelectronics.
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.
Well, this is indeed something that motion sensors needed. With simple installation and myriad of uses, we can safely presume that other organizations specializing in electronics will also be implementing this sooner rather than later. A report by Grand View Research mentioned the growth of the market to almost 35 billion USD in the next 2-3 years, but we can safely assume that the field will grow much much faster.