IN4254 Smart Phone Sensing
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IN4254 Smart Phone Sensing
If we have extra time, calibrate the phone to the training data set
Because most of the work needs to be done for #22 anyway, make a simple REST interface to get them from the "cloud" so the profs are satisfied
We need a test fragment for that uses our (nonexistant) classifier.
Implement a K-NN classifier
k=sqrt(size of training data set)
)We should gather new training data and store it in the db (#9)
& GUi elements from lecture
Currently our windows overlap during training, as is intended. However, during testing we do not overlap our windows. Not only might this skew results but it also increases the interval of measurements from 2,5s to 5s.
We need to gather test data for our report
Particle filters if we have enough time
If the smartphone it turned while training, the training stops.
Solution is probably to create a background taks (headless fragment, AsyncTask) to do the measuring in, instead of directly from the training fragment. This might also be beneficial for the testing fragment.
5th icon is only 20 % visible. It can still be selected though. Icons should be scaled or moved to a second row.
Screen information: 4" 800ร480 px WVGA (233 ppi)
I was thinking about sqlite, as this still would enable easy file transfers to analyse data and sqlite also is reasonably easy to use in e..g gnuplot (plot < 'sqlite x.db SELECT * FROM training'
) while still being a full-flegded SQL database
I was thinking about the following tables:
measurements (ID, timestamp, activity)
measurements_raw_samples (timestamp, measurement_id, X, Y, Z)
features (ID, feature_name)
measurements_features (measurement_ID, feature_ID, value)
I realised there isn't really difference between training data en testing data, as we currently also store an expected activiy with the testing data. Thus we can use our testing data as training data and vice versa :)
What do you think about the features? Should we save them like this or in a flat table?
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