Open Asanto32 opened 2 months ago
No rush on this but just some thoughts I had as we go through the Kaggle comp implementation. Since our current sleep detection is actually pretty good, the more time-sensitive, simple question is separating non-wear time from it. @gkiar @vitoetc @frey-perez @ReinderVosDeWael @nx10 @clane9
If the goal is not to mimick GGIR, I think conceptually non-wear should be regarded as missing data and (if necessary) either imputed or processed by a model that can handle missing data.
I also realize this falls more in the "analysis" branch and not really "wristpy for processing actigraphy data"
If the goal is not to mimick GGIR, I think conceptually non-wear should be regarded as missing data and (if necessary) either imputed or processed by a model that can handle missing data.
I should start looking into using the capsense data and see how we can take advantage of that.
I agree that the capsense/temperature data would be very useful for distinguishing non-wear from sleep. Otherwise, I agree the two seem hard to distinguish.
I think the ideal approach would be a model/algorithm that takes as input all available raw data, and produces separate predictions for sleep and non-wear. Any pre-processing/feature-engineering/heuristics would be internal to the model. Ofc, it is also fine to have simple heuristics for detecting non-wear/sleep that work fine in most cases. But I would discourage having any of these be upstream of a sleep detection model. Anything you do upstream of the model removes information and limits the model's performance.
In particular, I would discourage trying to detect and code non-wear as missing/NaN before feeding to a sleep detection model. I agree with @nx10 that non-wear is fundamentally missing data. I just think it should be the model's job to detect it.
Just to clarify: non-wear and sleep are currently completely separate algorithms (see detect_non_wear
and run_sleep_detection
. I just have an additional function that filters out sleep windows (already detected) that have any overlap with detected non-wear periods (basically, I agree that if we detect that the watch isn't being worn, we shouldn't say that someone is sleeping).
Basically the point of my thought process is essentially a second stage that would try to filter out false-positives. But I agree an all-encompassing model would be better. This is almost more of a stop gap idea that I thought is both easier and quicker to implement while we wait for something more robust/nicer.
Does that help clarify a bit? I guess this was better for a meeting and not a forum discussion ha
Reclassified to spike, time-boxed to 3 days.
Description
Currently, the detection of sleep will also serve as non-wear detection (most non-wear periods will also be detected as sleep, as from an accelerometer point of view they are the same, the inverse is of course-not true, not all sleep periods are detected as non-wear). The current implementation of the Van Hees algorithm for sleep only relies on angle-z (looking the changes in the rolling std of angle-z metric, essentially). As a solution to separate sleep windows from non-wear times, a simple filtering method has been implemented in
analytics.remove_nonwear_from_sleep
.However, what if we were to apply some simple classification looking at a few features of both enmo and angle-z during a defined sleep window. Notably:
Some examples highlighting the differences between sleep and non-wear are shown below (blue indicates sleep, red enmo, green anglez, black non-wear)
This is an especially difficult case, non-wear immediately before bed with only a very short window of activity separating the two. Merged into one sleep window (but it has two distinct sleep periods, SIBs) since the activity time is less than the threshold.
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