GGIR (version 2.6-0)

g.getmeta: Function to extract meta-data (features) from data in accelerometer file

Description

Reads a accelerometer file in blocks, extracts various features and stores average feature value per short or long epoch. Acceleration and angle metrics are stored at short epoch length. The non-wear indication score, the clipping score, temperature (if available), light (if available), and Euclidean norm are stored at long epoch length. The function has been designed and thoroughly tested with accelerometer files from GENEA and GENEActiv. Further, the function should be able to cope with csv-format data procuded by GENEActiv and Actigraph

Usage

g.getmeta(datafile, params_metrics = c(), params_rawdata = c(),
                     params_general = c(), daylimit = FALSE, 
                     offset = c(0, 0, 0), scale = c(1, 1, 1), tempoffset = c(0, 0, 0),
                     meantempcal = c(), selectdaysfile = c(), myfun = c(), ...)

Arguments

datafile

name of accelerometer file

params_metrics

See g.part1

params_rawdata

See g.part1

params_general

See g.part1

daylimit

number of days to limit (roughly), if set to FALSE no daylimit will be applied

offset

offset correction value per axis, usage: value = scale(value,center = -offset, scale = 1/scale)

scale

scaling correction value per axis, usage: value = scale(value,center = -offset, scale = 1/scale)

tempoffset

temperature offset correction value per axis, usage: value = scale(value,center = -offset, scale = 1/scale) + scale(temperature, center = rep(averagetemperate,3), scale = 1/tempoffset)

meantempcal

mean temperature corresponding to the data as used for autocalibration. If autocalibration is not done or if temperature was not available then leave blank (default)

selectdaysfile

see g.part1

myfun

External function object to be applied to raw data. See details applyExtFunction.

...

Any argument used in the previous version of g.getmeta, which will now be used to overrule the arguments specified with the parameter objects.

Value

metalong

dataframe with long epoch meta-data: EN, non-wear score, clipping score, temperature

metashort

dataframe with short epoch meta-data: timestamp and metric

tooshort

indicator of whether file was too short for processing (TRUE or FALSE)

corrupt

indicator of whether file was considered corrupt (TRUE or FALSE)

References

• van Hees VT, Gorzelniak L, Dean Leon EC, Eder M, Pias M, et al. (2013) Separating Movement and Gravity Components in an Acceleration Signal and Implications for the Assessment of Human Daily Physical Activity. PLoS ONE 8(4): e61691. doi:10.1371/journal.pone.0061691

• Aittasalo M, Vaha-Ypya H, Vasankari T, Husu P, Jussila AM, and Sievanen H. Mean amplitude deviation calculated from raw acceleration data: a novel method for classifying the intensity of adolescents physical activity irrespective of accelerometer brand. BMC Sports Science, Medicine and Rehabilitation (2015).

Examples

# NOT RUN {
  
# }
# NOT RUN {
    datafile = "C:/myfolder/testfile.bin"
    
    #Extract meta-data:
    M = g.getmeta(datafile)
    
    #Inspect first couple of rows of long epoch length meta data:
    print(M$metalong[1:5,])
    
    #Inspect first couple of rows of short epoch length meta data:
    print(M$metalong[1:5,])
  
# }