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Estimate Non-wear from Raw Accelerometry

Source: R/acti_calculate_wear_raw.R
acti_calculate_wear_raw.Rd

Detects non-wear from raw tri-axial acceleration. Unlike acti_calculate_wear(), which works on 60-second activity counts, this function takes the raw X, Y, Z signal (in g) as input. It calls acti_calculate_wear_raw_ggir() or acti_calculate_wear_raw_actipy(); see those functions for the algorithms and their arguments. Neither GGIR nor actipy is required.

Usage

acti_calculate_wear_raw(data, method = c("ggir", "actipy"), ...)

Arguments

data

A data.frame of raw accelerometer data with a time column and X, Y, Z columns, or an AccData object. Data should be calibrated (e.g. with acti_calibrate()).

method

Method for detecting non-wear, either "ggir" or "actipy", corresponding to acti_calculate_wear_raw_ggir() or acti_calculate_wear_raw_actipy().

...

additional arguments to pass to the method function, such as sample_rate, approach, or sd_threshold for "ggir", or window, patience, or sd_threshold for "actipy".

Value

A tibble with one row per epoch, containing at least time (start of the epoch), nonwear, and wear. The epoch length and the other columns depend on method.

See also

acti_calculate_wear_raw_ggir(), acti_calculate_wear_raw_actipy()

Examples

n = 3 * 3600 * 10
time = as.POSIXct("2020-01-01 00:00:00", tz = "UTC") + (seq_len(n) - 1) / 10
moving = rep(c(TRUE, FALSE, TRUE), each = n / 3)
set.seed(1)
data = data.frame(
  time = time,
  X = ifelse(moving, stats::rnorm(n, sd = 0.3), 0),
  Y = ifelse(moving, stats::rnorm(n, sd = 0.3), 0),
  Z = ifelse(moving, stats::rnorm(n, sd = 0.3), 1)
)
ggir = acti_calculate_wear_raw(data, sample_rate = 10)
actipy = acti_calculate_wear_raw(data, method = "actipy", patience = 3000)