eXplore Machine Learning in sensors
Machine Learning Core
Machine Learning is an application of Artificial Intelligence (AI) through which a machine can learn, by itself or in a supervised way, without explicit programming. It provides a system the ability to automatically learn and improve from experience without compromising the accuracy of the data collected. The Machine Learning processing capability allows moving some algorithms from the host processor to the IMU. The IMU would therefore only consume less than one hundredth of the MCU power used for the same typical tasks. The MLC is designed to run in a highly power-efficient manner and provides accurate results in the shortest possible time. A meta-classifier is also available to further enhance data accuracy in specific cases.
Developers of applications using sensors can thus benefit from the advantages of machine learning by creating their decision trees (using large data sets and high processing power) and having them run on an optimized MLC in the same sensor device.
- Decision trees can be created and updated much faster using Machine Learning compared to explicit programming when appropriate data sets are available
- The Machine Learning processing capability allows moving some algorithms from the host processor to the IMU. For typical tasks the IMU would consume 0.001 times the power of an MCU used for the same task. This is a key enabler for ultra low-power edge-computing
- iNEMO™ IMUs with MLC can be configured to run up to 8 decision trees simultaneously and independently giving added flexibility to developers
Discover our webinar on Machine Learning Core
The LSM6DSOX contains a 3-axis accelerometer and 3-axis gyroscope, and tracks complex movements using the machine-learning core at low typical current consumption of just 0.55 mA to minimize load on the battery.
The consumer-grade LSM6DSRX contains a 3-axis accelerometer and a 3-axis digital gyroscope with extended full-scale angular-rate range up to ±4000 dps.
The industrial-grade ISM330DHCX comes with 10-year product-longevity assurance and is specified from -40°C to 105°C, with embedded temperature compensation for superior stability.
Intelligent sensing with iNEMO™ Inertial Measurement Units
iNEMO IMUs are built around three blocks:
The built-in sensors (accelerometer and gyroscope) filter real-time motion data before sending it to the Computation Block where statistical parameters defined as “features” are applied to the captured data. The features aggregated in the computation block will be used as input for the third block of the Machine Learning Core.
The Decision Tree evaluates the statistical parameters and compares them against certain thresholds to identify certain situations and generate results sent to the MCU, such as the end-user cases featured below. The Machine Learning Core results are the decision tree’s output results which include the optional meta-classifier.
Building your Decision Trees
iNEMO™ IMUs offer a wide range of design possibilities for developers by allowing them to create their own embedded machine learning algorithms and to build the best decision tree for their application.