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
I am wrapping up my individualized sleep calculator and will begin my individual sleep stats calculator. This proved to be difficult with the way I had initially set up my features calculator since I was using the Day/Night Lights On/Lights Off times determined by Dr. Skornyakov as constant values. In order to do this properly, I am going to have to essentially redo my calculator to better support a variable wake time. This is probably something I should have done from the start, and going forward I hope to not subject myself to the same tedium by minimizing the number of constants I use.
Dr. Weeks has suggested that the most effective way for us to determine "good sleep" from "bad sleep", we should develop some baseline value that we can use to determine whether or not the patient is doing better or worse relative to only their own behaviour. To recognize how a value can be compared to the "usual" behaviour, we will say that our baseline period will be the first three days. We will then calculate the coefficient of variation within these days to see what sort of variation can be expected from that subject.
Once I have worked out the coefficient of variation calculations, I am hoping to create a graphical representation of CV across patients for each feature.
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