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 worked on the high activity tracking idea I had from the past few weeks to try and see if there is a relationship between sleep and the number of high activity minutes during the day. My initial approach to this concept was to look at the number of sleep minutes and their correlation with activity minutes the prior day or the following day. As Alexa and I noticed the first time we began comparing these, the data is all over the place. For each grouping (night-day and day-night), some subjects see a positive correlation and some see a negative one. The strength of these correlations was varied as well.
Because of the potential difference in the data for the first and second groups during data collection, we'd been looking for some sort of data-driven way to look at sleep that didn't involve the hard cutoffs we'd used to classify sleep initially. So, in the same way that I'd been looking at "sedentary-like" activity, I decided to try measuring "sleep-like" minutes at night to see if that could possibly be a metric that looked interesting. I did this by taking the median activity count over the first three nights and counting the number of minutes on the following nights that were below this count. This looks like metric with potential based on the simple test I used (see how much of an agreement there is between a higher number of "sleep-like" minutes and a higher number of high-activity minutes during the day). Going foreward, I am going to test to see how this metric compares to our original sleep/wake system and work to provide more formal definitions on why "sedentary-like" and "sleep-like" measures might be helpful.
Because of the potential difference in the data for the first and second groups during data collection, we'd been looking for some sort of data-driven way to look at sleep that didn't involve the hard cutoffs we'd used to classify sleep initially. So, in the same way that I'd been looking at "sedentary-like" activity, I decided to try measuring "sleep-like" minutes at night to see if that could possibly be a metric that looked interesting. I did this by taking the median activity count over the first three nights and counting the number of minutes on the following nights that were below this count. This looks like metric with potential based on the simple test I used (see how much of an agreement there is between a higher number of "sleep-like" minutes and a higher number of high-activity minutes during the day). Going foreward, I am going to test to see how this metric compares to our original sleep/wake system and work to provide more formal definitions on why "sedentary-like" and "sleep-like" measures might be helpful.
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