Detects the main rest and activity periods across a recording with the Crespo
algorithm (Crespo et al. 2012): a rank-order (median) filter at the alpha
(~rest-length) scale, an alpha/24h percentile threshold, and a separate
minutes-scale morphology pass that turn activity counts into a binary
rest/activity series read bout by bout from its transitions. The wide smoothing
window suppresses short within-period transitions, so it reports the main daily
rest rather than every nap.
Usage
rest.crespo(
counts,
timestamps,
epoch_length = 60,
zeta = 15,
zeta_r = 30,
zeta_a = 2,
t = 0.33,
alpha = 8 * 3600,
beta = 1 * 3600
)Arguments
- counts
Numeric activity vector (minute epochs recommended).
- timestamps
POSIXct timestamps, one per value.
- epoch_length
Epoch length in seconds (default 60).
- zeta, zeta_r, zeta_a
Maximum valid consecutive-zero run, in epochs, for the global pre-conditioning, within rest segments, and within active segments (defaults 15, 30, 2). Longer zero runs are treated as non-wear.
- t
Quantile of the counts used to replace non-wear epochs (default 0.33).
- alpha
Expected daily rest length, in seconds (default 28800, 8 hours); sets the rank-order smoothing window (Eq 4), the threshold percentile (alpha / 24 h), and the minimum data required.
- beta
Morphology scale, in seconds (default 3600, 1 hour); sets the structuring-element size that consolidates the thresholded series.
Value
An object of class actiRhythm_crespo: a rest_periods
data frame (one row per bout, with onset, offset, and duration), the
per-epoch rest_state ("R"/"A"), and per-bout counts. The function
never errors; with no resolvable bout it returns an empty
rest_periods.
References
Crespo C, Aboy M, Fernandez JR, Mojon A (2012). “Automatic identification of activity-rest periods based on actigraphy.” Medical & Biological Engineering & Computing, 50(4), 329–340. doi:10.1007/s11517-012-0875-y .
Hammad G, Reyt M, Beliy N, Baillet M, Deantoni M, Lesoinne A, Muto V, Schmidt C (2021). “pyActigraphy: open-source python package for actigraphy data visualization and analysis.” PLOS Computational Biology, 17(10), e1009514. doi:10.1371/journal.pcbi.1009514 .
Examples
# Two nights of rest with a daytime nap between them
ts <- seq(as.POSIXct("2024-01-01", tz = "UTC"), by = 60, length.out = 3 * 1440)
h <- as.numeric(format(ts, "%H")) + as.numeric(format(ts, "%M")) / 60
day <- as.integer(format(ts, "%j"))
counts <- ifelse(h >= 23 | h < 7, 5, 300)
counts[h >= 14 & h < 15 & day == min(day) + 1L] <- 5
rest.crespo(counts, ts)
#> Crespo Rest/Activity Periods
#>
#> Rest bouts: 3 (1.5 per day over 2 days)
#> Total rest: 23.0 h
#>
#> First bouts:
#> 01-01 00:01 -> 01-01 06:59 (419 min)
#> 01-01 23:00 -> 01-02 06:59 (480 min)
#> 01-02 23:00 -> 01-03 06:59 (480 min)
#>
#> Reference: Crespo et al. (2012)
#>