walkboutrpackage will process GPS and accelerometry data and create two different outputs:
First we will generate some sample data:
Now that we have sample data, we can look at how
walkboutr generates bouts:
The bouts identified look like this:
We can now use the second function to generate our summarized dataset, which is de-identified and shareable:
walk_boutis 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_slowbout is a bout where the median speed is too slow to be considered walking.
non_walk_fastbout is a bout where the median speed is too fast to be considered walking.
non_walk_too_vigorousbout is a bout where the average CPE is too high to be considered walking (ex. running or biking).
dwell_boutis a bout where the radius of GPS points is below our threshold for considering someone to have stayed in one place.
non_walk_incomplete_gpsbout 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.