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walkboutr
package will process GPS and accelerometry
data and create two different outputs:
First we will generate some sample data:
gps_data <- generate_walking_in_seattle_gps_data()
accelerometry_counts <- make_full_day_bout_without_metadata()
Now that we have sample data, we can look at how
walkboutr
generates bouts:
walk_bouts <- identify_walk_bouts_in_gps_and_accelerometry_data(gps_data, accelerometry_counts)
summary_walk_bouts <- summarize_walk_bouts(walk_bouts)
The bouts identified look like this:
bout | bout_category | activity_counts | time | non_wearing | complete_day | latitude | longitude | speed |
---|---|---|---|---|---|---|---|---|
1 | walk_bout | 500 | 2012-04-07 00:03:00 | FALSE | TRUE | 47.64979 | 122.3757 | 1.1009735 |
1 | walk_bout | 500 | 2012-04-07 00:05:00 | FALSE | TRUE | 47.69260 | 122.4185 | 2.7901428 |
1 | walk_bout | 500 | 2012-04-07 00:07:00 | FALSE | TRUE | 47.75359 | 122.4795 | 0.9801357 |
1 | walk_bout | 500 | 2012-04-07 00:05:30 | FALSE | TRUE | 47.70575 | 122.4317 | 2.7249735 |
1 | walk_bout | 500 | 2012-04-07 00:06:00 | FALSE | TRUE | 47.71839 | 122.4443 | 4.0867381 |
1 | walk_bout | 500 | 2012-04-07 00:06:30 | FALSE | TRUE | 47.73835 | 122.4643 | 3.0513150 |
We can now use the second function to generate our summarized dataset, which is de-identified and shareable:
bout | median_speed | complete_day | bout_start | duration | bout_category |
---|---|---|---|---|---|
1 | 2.736466 | TRUE | 2012-04-07 00:02:30 | 5.0 | walk_bout |
2 | 2.555720 | TRUE | 2012-04-07 00:09:30 | 4304.5 | walk_bout |
walk_bout
is defined based
on the scientific literature as: Assuming a greedy algorithm and
consideration of inactive time as consecutive, a walk bout is any
contiguous period of time where the active epochs have accelerometry
counts above the minimum threshold of 500 CPE (to allow for capture of
light physical activity such as slow walking) and the time period:
Accordingly, the following non-walk-bouts are defined as:
non_walk_slow
bout is a bout where the median speed is too
slow to be considered walking.non_walk_fast
bout is a bout where the median speed is too
fast to be considered walking.non_walk_too_vigorous
bout is a bout where the average CPE
is too high to be considered walking (ex. running or biking).dwell_bout
is a bout
where the radius of GPS points is below our threshold for considering
someone to have stayed in one place.non_walk_incomplete_gps
bout is a bout where the GPS
coverage is too low to be considered complete.In order to better visualize our bouts, we can also plot the accelerometry counts and GPS radius.
accelerometry_counts <- make_smallest_bout_without_metadata()
gps_data <- generate_walking_in_seattle_gps_data()
generate_bout_plot(accelerometry_counts, gps_data, 1)
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.