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
The Guide to Actigraphy data collection proved to be very helpful! We'd come across many articles comparing Actigraphy to Polysomnography, but few that discuss research using solely watch-based data collection. The study by Dr. Weeks and Dr. Skornyakov has not relied on Actigraphy logs, as is the general recommendation, because their subjects are not going to be consistently capable of filling them out with accurate information. The issue then will be determining whether our Day/Night times are appropriate for the individual patient. I think that Dr. Skornyakov's individualized sleep idea will see to this issue fairly well.
An additional factor I have seen over and over again during our literature review is sleep onset latency. This is what is used in sleep labs to diagnose things like narcolepsy and insomnia. It is a measurement of how long it takes a subject to fall asleep once they lie down in complete darkness. Because we are dealing with patients who spend more time immobile than most, it may be difficult to locate this point in time based on only activity data. We also have a designated Lights Off time, but we have no way of knowing whether subjects are watching TV/on their phones once the hospital's lights have been turned off.
I am going to explore the idea of doing something similar to Elena's individualized wake time in order to find a potentially more accurate sleep time near the Lights Off time. This could give us a clearer picture of the patient's day.
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