Simple example of 6D position recognition implemented using the Mean features (signed/unsigned) on different axes of the accelerometer data.
The positions recognized with this example are: None, X-axis pointing up, X-axis pointing down, Y-axis pointing up, Y-axis pointing down, Z-axis pointing up, Z-axis pointing down.
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.
Any sensor orientation is allowed for this algorithm.
Two features have been used (mean signed and mean unsigned), and applied to all the accelerometer axes. The MLC runs at 26 Hz, computing features on windows of 16 samples (corresponding to 0.6 seconds). One decision tree with around 8 nodes has been configured to detect the different classes. A meta-classifier has not been used.
- MLC0_SRC (70h) register values
- 0 = None
- 1 = X-axis pointing up
- 2 = X-axis pointing down
- 3 = Y-axis pointing up
- 4 = Y-axis pointing down
- 5 = Z-axis pointing up
- 6 = Z-axis pointing down
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.
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