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Apply the LIDS (Locomotor Inactivity During Sleep) transform

Source: R/lids.R
lids_transform.Rd

Converts an activity signal into "inactivity" via the LIDS non-linear transform, then smooths it – the first step of the LIDS ultradian-rhythm pipeline (Winnebeck et al. 2018; Hammad et al. 2026). LIDS is only ever computed inside an already-identified sleep bout; extract bouts first via compute_lids() or detect_lids_bouts().

Usage

lids_transform(
  activity,
  epoch_min = 1,
  method = c("gaussian", "mva"),
  win_min = 30,
  sigma_min = 5
)

Arguments

activity

Numeric vector of activity counts for a single sleep bout, in chronological order at a constant epoch length.

epoch_min

numeric(1). Epoch duration in minutes. Default 1.

method

character(1). "gaussian" (default) or "mva".

win_min

numeric(1). Smoothing window width in minutes (full width for "mva"; +/- 3 sigma width for "gaussian"). Default 30.

sigma_min

numeric(1). Gaussian kernel standard deviation in minutes, used only when method = "gaussian". Default 5 (Hammad et al. 2026); ignored for "mva".

Value

Numeric vector of smoothed LIDS values, same length as activity.

Details

$$\text{LIDS}_i = \frac{100}{1+x_i}$$

where \(x_i\) is the raw activity count at epoch \(i\). A LIDS value of 100 means zero movement; it falls toward 0 as movement increases.

Two smoothing methods are available, both over a nominal 30-min window:

  • "gaussian" (default) – Gaussian kernel, standard deviation sigma_min (default 5 min), truncated at +/- 3 sigma (Hammad et al. 2026).

  • "mva" – centered moving average (Winnebeck et al. 2018; pyActigraphy's default). Uses zeitR's border-replicated internal rolling_mean_cpp(), which replicates the edge value rather than shrinking the window near the bout boundary (pandas' min_periods=1 behaviour) – a minor difference confined to the first/ last ~15 min of each bout.

Any NA in activity (e.g. a brief off-wrist gap inside an otherwise valid bout) is linearly interpolated first – fit_lids() cannot handle missing values.

References

Winnebeck, E. C., Fischer, D., Leise, T., & Roenneberg, T. (2018). Dynamics and Ultradian Structure of Human Sleep in Real Life. Current Biology, 28(1), 49-59.e5. doi:10.1016/j.cub.2017.11.063

Hammad, G., Schoch, S. F., Engelmann, M., Spock, Z., Kurth, S., & Winnebeck, E. C. (2026). Charting infant sleep cycle development using actigraphy: Longitudinal evidence for ultradian cycle lengthening within the first year of life. SLEEP.

See also

fit_lids(), detect_lids_bouts(), compute_lids()

Examples

set.seed(1)
activity <- pmax(0, 20 + 15 * sin(seq(0, 6 * pi, length.out = 360)) +
                    rnorm(360, sd = 5))
lids <- lids_transform(activity, method = "gaussian")