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
At the request of Dr. Skornyakov, Alexa, Dr. Sprint and I intend to calculate individualized wake time factors for each day. This will be determined by the patient's sleep/wake status between six and seven in the morning. The idea behind this is that previously if a patient had woken before our "Day" period began (7:00AM) their activity would be recorded in the previous day's sleep period. Dr. Skornyakov has provided clear instructions on how to calculate this time. I plan to start by adding an extra level of processing to the output files such that we will have an additional column all for this new factor. Then, I'll modify my feature calculator so that it accesses the features from the newly determined time period.
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