The activity recognition algorithm described in this example is intended for wrist applications since all the data logs collected for this purpose have been acquired with a smartwatch carried at the user's wrist. The activities recognized in this example are: Stationary/Other, Walking/FastWalking and Jogging/Running. A limited subset of data logs for this example is available here.
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 ±8 g full scale and 26 Hz output data rate.
Any sensor orientation is allowed for this algorithm.
Seven features have been computed (mean, variance, energy, peak-to-peak, zero-crossing, min, max) and applied to the accelerometer input data. The MLC runs at 26 Hz, computing features on windows of 52 samples (corresponding to 2 seconds). One decision tree with around 70 nodes has been configured to detect the different classes. A meta-classifier has been set to reduce false positives.
- MLC0_SRC (70h) register values
- 1 = Stationary/Other
- 4 = Walking/FastWalking
- 8 = Jogging/Running
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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