apply_choi | R Documentation |
The Choi algorithm detects periods of non-wear in activity data from an ActiGraph device. Such intervals are likely to represent invalid data and therefore should be excluded from downstream analysis.
apply_choi( agdb, min_period_len = 90, min_window_len = 30, spike_tolerance = 2, use_magnitude = FALSE )
agdb |
A |
min_period_len |
Minimum number of consecutive "zero" epochs to start a non-wear period. The default is 90. |
min_window_len |
The minimum number of consecutive "zero" epochs immediately preceding and following a spike of artifactual movement. The default is 30. |
spike_tolerance |
Also known as artifactual movement interval.
At most |
use_magnitude |
Logical. If true, the magnitude of the vector (axis1, axis2, axis3) is used to measure activity; otherwise the axis1 value is used. The default is FALSE. |
The Choi algorithm extends the Troiano algorithm by requiring that
short spikes of artifactual movement during a non-wear period are
preceded and followed by min_window_len
consecutive "zero" epochs.
This implementation of the algorithm expects that the epochs are 60 second long.
A summary tibble
of the detected non-wear periods.
If the activity data is grouped, then non-wear periods are
detected separately for each group.
L Choi, Z Liu, CE Matthews and MS Buchowski. Validation of accelerometer wear and nonwear time classification algorithm. Medicine & Science in Sports & Exercise, 43(2):357–364, 2011.
ActiLife 6 User's Manual by the ActiGraph Software Department. 04/03/2012.
apply_troiano()
, collapse_epochs()
library("dplyr") data("gtxplus1day") gtxplus1day %>% collapse_epochs(60) %>% apply_choi()
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