The activity recognition algorithm described in this example is intended for smartphone applications since all the data logs collected for this purpose have been acquired with a smartphone carried in the user's pocket. The activities recognized in this example are: Stationary, Walking, Jogging, Biking and Driving. 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 ±4 g full scale and 25 Hz output data rate.
Any sensor orientation is allowed for this algorithm.
Four features have been used (mean, variance, peak-to-peak, zero-crossing), and two different filters have been applied to the accelerometer input data. The MLC runs at 25 Hz, computing features on windows of 75 samples (corresponding to 3 seconds). One decision tree with around 120 nodes has been configured to detect the different classes. A meta-classifier has not been used.
- MLC1_SRC (34h) register values
- 0 = Stationary
- 1 = Walking
- 4 = Jogging
- 8 = Biking
- 12 = Driving
The configuration generates an interrupt (pulsed and active high) on the INT1 pin every time the register MLC1_SRC (34h) is updated with a new value. The duration of the interrupt pulse is 40 ms in this configuration.
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