Detects the accelerometer non-wear time based on an algorithm developed by van Hees (see Details) and remove these periods from the raw data. This function can also draw a plot to better visualize the detected non-wear periods and generate a wear time daily summary.

## Usage

remove_nonwear(
data,
window1 = 60,
window2 = 15,
threshold = 2,
min_hour_crit = 0,
min_day_crit = 0,
plot = FALSE,
save_plot = FALSE,
save_summary = FALSE
)

## Arguments

data

An impactr_data object, as obtained with read_acc().

window1, window2

Windows size, in minutes, for the non-wear detection algorithm. Defaults to 60 and 15 minutes, respectively. Also, window2 must be smaller than window1, and window1 must be a multiple of window2.

threshold

Number of axes that need to meet the non-wear criteria. Defaults to 2.

min_hour_crit

The minimum number of hours marked as wear time in a day for it to be considered valid (see Data validation). Defaults to 0, meaning that every day is considered valid.

min_day_crit

The minimum number of valid days for the data of a given subject to be considered valid (see Data validation). Defaults to 0, meaning that all data is valid.

plot

A logical value indicating whether or not to display the plot to visualize the detected non-wear periods. Defaults to FALSE. Notice that the plot will only be displayed in your R session if you do not provide a path to save the plot (see the argument save_plot).

save_plot, save_summary

Indicates whether of not to save the plot to visualize the detected non-wear periods to a pdf file and the wear time daily summary to a csv file, respectively. Defaults to FALSE. Provide a valid path to a file, ending with the ".pdf" extension for the plot or with the ".csv" extension to the summary, as a character string if you want the outputs to be saved.

## Value

An object of class impactr_data and a plot if plot = TRUE and save_plot = FALSE.

## The non-wear detection algorithm

The current version of this algorithm is described in a paper by van Hees et al (see References) and also in this vignette from package GGIR. Briefly, in a first stage it identifies non-wear time based on threshold values of standard deviation (0.013g) and range (0.050g) of raw acceleration from each axis. The classification is done per blocks of window2 size (default 15 minutes) based on the characteristics of a larger window1 (default 60 minutes) centred at the window2. In the second stage of the algorithm, the plausibility of wear periods in between non-wear periods is tested based on the duration and proportion of the duration relative to the surrounding non-wear periods.

## Data validation

After the detection of non-wear periods through the algorithm, a data validation step is applied. For each measurement day to be considered valid, it has to present a minimum number of wear time hours determined by the min_hour_crit argument. If the number of wear time hours of a given day falls below the threshold, the whole day is considered invalid and is then removed from the subsequent analyses. The whole measurement is also classified as valid or invalid based on the number of valid days and a threshold given by min_day_crit. If the number of valid days is less than the value determined by the min_day_crit argument, the whole data is deleted and the remove_nonwear() function signals an error, stopping its execution. Nevertheless, this error does not prevent the plot to be displayed or saved, or the wear time daily summary to be saved, if the arguments are set to do so.

## References

• van Hees VT, Gorzelniak L, Dean León EC, Eder M, Pias M, Taherian S, Ekelund U, Renström F, Franks PW, Horsch A, Brage S. Separating movement and gravity components in an acceleration signal and implications for the assessment of human daily physical activity. PLoS One. 2013. Apr 23. doi:10.1371/journal.pone.0061691 .

## Examples

# \donttest{
# Ensure that {accdata} package is available before running the example.
# If it is not, run install_accdata() to install the required package.
if (requireNamespace("accdata", quietly = TRUE)) {
data <- import_dataset("daily_acc_3d")
remove_nonwear(data)
}
#> # Start time:              2016-01-20 00:01:00
#> # Sampling frequency:      100Hz
#> # Accelerometer placement: Back
#> # Subject body mass:       119kg
#> # Filter:                  No filter applied
#> # Data dimensions:         18,180,000 × 4
#>    timestamp           acc_X acc_Y acc_Z
#>    <dttm>              <dbl> <dbl> <dbl>
#>  1 2016-01-20 07:01:00 0.176 0.021  1.01
#>  2 2016-01-20 07:01:00 0.176 0.021  1.01
#>  3 2016-01-20 07:01:00 0.176 0.021  1.01
#>  4 2016-01-20 07:01:00 0.176 0.021  1.01
#>  5 2016-01-20 07:01:00 0.176 0.021  1.01
#>  6 2016-01-20 07:01:00 0.176 0.021  1.01
#>  7 2016-01-20 07:01:00 0.176 0.021  1.01
#>  8 2016-01-20 07:01:00 0.176 0.021  1.01
#>  9 2016-01-20 07:01:00 0.176 0.021  1.01
#> 10 2016-01-20 07:01:00 0.176 0.021  1.01
#> # … with 18,179,990 more rows
#> # ℹ Use print(n = ...) to see more rows
# }