dataset: The source dataset, in dataframe format, which needs to be marked.
frame: The size of time interval to be considered; Window 1 described in Choi et al. (2011). The default is 90.
perMinuteCts: The number of data rows per minute. The default is 1-sec epoch (perMinuteCts = 60). For examples: for data with 10-sec epoch, set perMinuteCts = 6; for data with 1-min epoch, set perMinuteCts = 1.
TS: The column name for timestamp. The default is TimeStamp .
cts: The column name for counts. The default is axis1 .
streamFrame: The size of time interval that the program will look back or forward if activity is detected; Window 2 described in Choi et al.
(2011). The default is the half of the frame.
allowanceFrame: The size of time interval that zero counts are allowed; the artifactual movement interval described in Choi et al. (2011). The default is 2.
newcolname: The column name for classified wear and nonwear status. The default is wearing . After the data is processed, a new field will be added to the original dataframe. This new field is an indicator for the wearing (w ) or nowwearing (nw ).
getMinuteMarking: Return minute data with wear and nonwear classification. If the source is not a minute dataset, the function will collapse it into minute data. The default is FALSE.
dayStart: Define the starting time of day. The default is the midnight, "00:00:00". It must be in the format of "hh:mm:ss".
tz: Local time zone, defaults to UTC.
...: Parameter settings that will be used in dataCollapser function.
Returns
A data frame with the column for wear and nonwear classification indicator by epoch-by-epoch basis.
Details
A detailed description of the algorithm implemented in this function is described in Choi et al. (2011).
Note
Warning: It will be very slow if accelerometer data with 1-sec epoch for many days are directly classified. We recommend to collapse a dataset with 1-sec epoch to 1-min epoch data using dataCollapser and then classify wear and nonwear status using a dataset with a larger epoch.
Examples
data(dataSec)## mark data with 1-min epochmydata1m = dataCollapser(dataSec, TS ="TimeStamp", col ="counts", by =60)data1m = wearingMarking(dataset = mydata1m, frame =90, perMinuteCts =1, TS ="TimeStamp", cts ="counts", streamFrame =NULL, allowanceFrame=2, newcolname ="wearing")sumVct(data1m, id="dataid")## mark data with 1-sec epoch## Not run:data1s = wearingMarking(dataset = dataSec, frame =90, perMinuteCts =60, TS ="TimeStamp", cts ="counts", streamFrame =NULL, allowanceFrame=2, newcolname ="wearing", getMinuteMarking =FALSE)sumVct(data1s, id="dataid")sumVct(data1s, id="dataid", markingString ="nw")## End(Not run)
References
Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc. 2011 Feb;43(2):357-64.