Create Sleep Measures of Actigraphy and Activity Analysis

Estimates sleep from wrist accelerometry and epoch-level activity data. Implements sleep-period-time guiders based on angle changes, posture, least-active periods, sustained inactivity, low activity, and diary or fixed time windows; combines guider windows with sustained-inactivity bouts to produce sleep/wake labels and consensus estimates. Provides diary helpers and Tudor-Locke sleep-period summaries, and interfaces to the 'asleep' and 'sleeper' machine-learning estimators. The angle-based and sustained-inactivity methods are described by van Hees et al. (2015) and van Hees et al. (2018) ; the sleep-period summary method by Tudor-Locke et al. (2014) ; and the model estimators by Yuan et al. (2024) and Sundararajan et al. (2021) .

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")