This Machine Learning Core (MLC) example implements detection of the tilt angle, i.e. the current inclination of the device, on an x-y plane parallel to gravity.
Two decision trees are configured:
- Detects angles from 0 to 180 degrees
- Detects angles from 181 to 359 degrees
The tilt angle is the sum of the outputs of the two decision trees.
For information on how to integrate this algorithm in the target platform, please follow the instructions available in the README file of the examples folder.
For information on how to create similar algorithms, please follow the instructions provided in the tutorials folder.
The accelerometer is configured with ±2 g full scale and 26 Hz output data rate.
The device must be placed with the x-y plane parallel to gravity. The detected tilt angle is 0 degrees when the y-axis is pointing up.
Note: the angle is correctly detected only with the x-y plane parallel to gravity since a two-axis accelerometer is used.
Two features have been used (the mean of the x-axis and the mean of the y-axis of the accelerometer data). The MLC runs at 26 Hz, computing features on windows of 1 sample (corresponding to 38.5 milliseconds), in order to ensure maximum responsiveness.
Two decision trees with around 180 nodes have been configured to detect the different classes. Meta-classifiers have not been used.
Decision tree 1:
Output values from 0 to 180 in the corresponding tilt angle range, output value 180 when the angle is more than 180 degrees
Decision tree 2:
Output values from 0 to 179 starting from a tilt angle of 180 degrees and increases together with the angle, output value 0 when the tilt angle is less than 180 degrees;.
The tilt angle is the sum of the outputs of the two decision trees.
The configuration generates an interrupt (pulsed and active high) on the INT1 pin every time the register MLC0_SRC (70h) is updated with a new value. The duration of the interrupt pulse is 38.5 ms in this configuration.
More Information: http://www.st.com
Copyright © 2025 STMicroelectronics