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
This week, I wrapped up my daytime activity ratio calculator and spent time reviewing literature that may shed light on how we can determine something like good sleep from our data. We have decided that if we cannot find a data-driven way to label sleep good or bad, we will have to refer to the self-reported sleep questionnaires that are given to subjects every third day of their time in the study. Our primary reference would be the Karolinska Sleepiness Scale, which asks the subject to select a face in a lineup of five faces that they believe best represents how sleepy they feel at the moment. These questionnaires are provided at consistent times for each patient at a consistent rate.
In the upcoming week, I am going to continue our literature review and the search for meaningful features.
In the upcoming week, I am going to continue our literature review and the search for meaningful features.
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