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actisomni

actisomni estimates sleep from wrist-worn accelerometry. It provides sleep-period-time (SPT) guiders, sustained-inactivity-bout (SIB) labels, consensus sleep labels, diary helpers, and wrappers for the asleep and sleeper machine-learning models.

The guider functions identify a likely main sleep window. Combine one or more guiders with a SIB label when an epoch-level sleep/wake label is needed.

Why not actisleep?

The original package name was actisleep, but that conflicted with a previous R package: https://CRAN.R-project.org/package=ActiSleep.

Installation

Install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("jhuwit/actisomni")

Sleep guiders and labels

For epoch-level activity data, use an activity-based guider such as L5 and then apply a SIB label. The result has one logical sleep label per epoch.

library(actisomni)

time <- as.POSIXct("2020-01-01 18:00:00", tz = "UTC") + 0:1439 * 60
epochs <- data.frame(
  time = time,
  activity = c(rep(20, 240), rep(0, 420), rep(20, 780)),
  sib = c(rep(FALSE, 240), rep(TRUE, 420), rep(FALSE, 780))
)

guider <- acti_sleep_l5(epochs)
epochs$sleep <- acti_sleep_label(epochs$sib, guider)
head(epochs)
#>                  time activity   sib sleep
#> 1 2020-01-01 18:00:00       20 FALSE FALSE
#> 2 2020-01-01 18:01:00       20 FALSE FALSE
#> 3 2020-01-01 18:02:00       20 FALSE FALSE
#> 4 2020-01-01 18:03:00       20 FALSE FALSE
#> 5 2020-01-01 18:04:00       20 FALSE FALSE
#> 6 2020-01-01 18:05:00       20 FALSE FALSE

For raw X/Y/Z acceleration, acti_sleep_sib() calculates the van Hees SIB indicator and acti_sleep_ensemble() combines compatible guiders into a consensus label. The example recording supplied by actiread can be used directly:

library(actiread)

raw <- actiread::acti_read_gt3x(
  actiread::acti_example_gt3x(),
  verbose = FALSE
)
result <- acti_sleep_ensemble(raw, epoch = "5 seconds")

result$label_data

result$labels retains one label per guider, while result$fused contains the consensus probability and logical consensus label.

Diary windows

When diary times are available, create a guider directly from onset and wake times. Times are inclusive at both endpoints.

diary_window <- acti_sleep_diary(
  epochs,
  onset = as.POSIXct("2020-01-01 22:00:00", tz = "UTC"),
  wakeup = as.POSIXct("2020-01-02 06:00:00", tz = "UTC")
)
sum(diary_window$window)
#> [1] 481

Tudor–Locke sleep periods and metrics

For labelled, one-minute epochs, acti_sleep_tudor_locke() delegates Tudor–Locke period detection and sleep metrics to actigraph.sleepr. It accepts the usual actisomni column names: time, an activity column, and a logical or sleep/wake sleep column.

sleep_diary <- data.frame(
  night = 1:2,
  onset = as.POSIXct(c("2020-01-03 22:30:00", "2020-01-04 22:45:00"), tz = "UTC"),
  wakeup = as.POSIXct(c("2020-01-04 06:15:00", "2020-01-05 06:30:00"), tz = "UTC")
)
sleep_diary
#>   night               onset              wakeup
#> 1     1 2020-01-03 22:30:00 2020-01-04 06:15:00
#> 2     2 2020-01-04 22:45:00 2020-01-05 06:30:00

tudor_epochs <- acti_sleep_tudor_locke_diary(
  data.frame(
    time = as.POSIXct("2020-01-03 22:00:00", tz = "UTC") + 0:539 * 60,
    activity = 0
  ),
  sleep_diary = sleep_diary
)
tudor_locke <- acti_sleep_tudor_locke(tudor_epochs)
tudor_locke[c("onset", "out_bed_time", "total_sleep_time", "efficiency")]
#> # A tibble: 1 × 4
#>   onset               out_bed_time        total_sleep_time efficiency
#>   <dttm>              <dttm>                         <int>      <dbl>
#> 1 2020-01-03 22:30:00 2020-01-04 06:15:00              465        100

Run the diary helper separately for each person. A diary is one row per sleep interval, with POSIXct onset and wakeup columns; intervals can cross midnight and span multiple days.

Machine-learning model wrappers

acti_asleep() and acti_sleeper() both require complete, strictly time-ordered raw triaxial data. They return the same columns: time, sleep, sleep_probability, sleep_stage, nonwear, and method.

The models and their Python requirements are managed by the upstream packages. For sleeper, download the model files first and provide their directory as model_dir.

asleep_estimate <- acti_asleep(raw, verbose = FALSE)
sleeper_estimate <- acti_sleeper(raw, model_dir = "path/to/sleeper-models")

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.
Health stats visible at Monitor.