Accelerometers are enhanced by AI, says STMicroelectronics
Three accelerometers released by STMicroelectronics are claimed to have advanced processing engines which are designed to extend sensor autonomy, allowing systems to respond more quickly to external events while lowering power consumption, explained the company.
The LIS2DUX12 and LIS2DUXS12 leverage ST’s third-generation MEMS technology, adding programmable capabilities including a machine-learning core (MLC), advanced finite state machine (FSM) and an enhanced pedometer.
A third entry-level accelerometer, the LIS2DU12, is available for less demanding applications. All three accelerometers are equipped with the latest industry standard I3C interface. The three devices integrate the common digital features for detecting events, as well as an anti-aliasing filter for high accuracy at lower sampling frequencies with performance benefits for accurate gesture detection at negligible power consumption.
The integrated MLC in the LIS2DUX12 and LIS2DUXS12 enables AI algorithms to perform reliable activity detection and the FSM enhances movement recognition. Together, they provide autonomous processing in the sensor, explained ST which offloads host interaction and processing, significantly lowering power consumption and enables faster system responses.
Deploying an adaptive self-configuration (ASC) capability, the accelerometers adjust their own settings (e.g., measurement range and frequency) independently to further optimise performance.
The LIS2DUXS12 also features ST’s Qvar sensing channel that senses changes in the ambient electrostatic environment to provide presence and proximity detection. This capability lets developers add value to applications such as user-interface control, liquid detection and biometric sensing such as heart rate monitoring. In user-interface applications, Qvar can be combined with an acceleration signal to remove potential false positive detection in two-tap and multi-tap events.
The smart accelerometers provide context sensing wearable devices, true wireless stereo (TWS) speakers and earbuds, smartphones, hearing aids, game controllers, smart watches, asset trackers, robotic appliances and IoT devices.
All three accelerometer uses ST’s low power architecture which combines an anti-alias filter that helps to boost application performance, removing unwanted noise from the signal. Ready-to-use MLC and FSM algorithms are available through ST’s MEMS GitHub model zoo, which facilitates complex gestures, asset tracking, and many other use cases.
The LIS2DUX12 and LIS2DUXS12 are in production now and available in a 2.0 x 2.0 x 0.74mm 12-lead LGA package. The LIS2DU12, in the same package type, is also available.
