This week, I am working to get the dynamic time warping functionality into my program. The process of doing so includes re-processing the features to include the time series, putting each series back together when we construct sequences, and then performing the DTW to generate a number that will be used to compute the kNN of each sequence which can then be used for predictions with the models. The processing time of these activities has gone up significantly since we have been using five different metrics with each of the F phase datasets. I am returning to school next week, and once I've completed the DTW processing all that will remain before we put together our second paper (The date for the reach journal we would like to submit it to is October 1), I am hoping I will have time to look again into the Agglomerative Hierarchical Clustering concept, which I did not successfully complete when we explored it earlier in the summer and then changed focus to the paper. We heard back
In addition to the Fitbit data, we've received 12 new subjects' data from the P Phase. I am going to spend this week cleaning the data and running it through our processing pipeline to get it up to speed. A challenge with introducing Fitbit data is going to be finding an appropriate measurement of activity since it's output does not provide a single summarizing "Activity" metric. Instead, it reports minute-by-minute steps, heart rate, calories burned, distance moved (vertically) and caloric mets. We want to use the Fitbit data with our other data, meaning we need to find out which of the measurements can be used for the "Active" and "Inactive" minute calculations - finding the median and classifying all of the minutes above/below as Active or Inactive for Day and Night, respectively.
My plan with respect to this is to go forward with all of the measurements and see which performs best. We hope that we will be able to successfully integrate the three data sets and get positive results - i.e. a model that performs as well as or better than any model we can produce with one of the sets individually - but we think that it could also be a fairly interesting topic for the paper if we can't. We could potentially discuss the different models and attempt to figure out why the models that work best for each group were the best fit.
My plan with respect to this is to go forward with all of the measurements and see which performs best. We hope that we will be able to successfully integrate the three data sets and get positive results - i.e. a model that performs as well as or better than any model we can produce with one of the sets individually - but we think that it could also be a fairly interesting topic for the paper if we can't. We could potentially discuss the different models and attempt to figure out why the models that work best for each group were the best fit.
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