Calls function g.getmeta and g.calibrate, and converts the output to .RData-format which will be the input for g.part2. Here, the function generates a folder structure to keep track of various output files. The reason why these g.part1 and g.part2 are not merged as one generic shell function is because g.part1 takes much longer to and involves only minor decisions of interest to the movement scientist. Function g.part2 on the other hand is relatively fast and comes with all the decisions that directly impact on the variables that are of interest to the movement scientist. Therefore, the user may want to run g.part1 overnight or on a computing cluster, while g.part2 can then be the main playing ground for the movement scientist. Function g.shell.GGIR provides the main shell that allows for operating g.part1 and g.part2.
g.part1(datadir=c(),outputdir=c(),f0=1,f1=c(),
windowsizes = c(5,900,3600),
desiredtz = "",chunksize=c(),studyname=c(),
do.enmo = TRUE,do.lfenmo = FALSE,do.en = FALSE,
do.bfen = FALSE, do.hfen=FALSE, do.hfenplus = FALSE,
do.mad = FALSE, do.anglex=FALSE, do.angley=FALSE,
do.anglez=FALSE, do.enmoa=FALSE,
do.roll_med_acc_x=FALSE, do.roll_med_acc_y=FALSE,
do.roll_med_acc_z=FALSE, do.dev_roll_med_acc_x=FALSE,
do.dev_roll_med_acc_y=FALSE, do.dev_roll_med_acc_z=FALSE,
do.lfen = FALSE, do.lfx=FALSE, do.lfy=FALSE, do.lfz=FALSE,
do.hfx=FALSE, do.hfy=FALSE, do.hfz=FALSE,
do.bfx=FALSE, do.bfy=FALSE, do.bfz=FALSE,
do.cal = TRUE,lb = 0.2, hb = 15, n = 4,
spherecrit=0.3,minloadcrit=72,
printsummary=TRUE,print.filename=FALSE,overwrite=FALSE,
backup.cal.coef="retrieve",selectdaysfile=c(),dayborder=0,
dynrange=c(), configtz=c(), do.parallel=TRUE, minimumFileSizeMB = 2,
myfun=c(),
do.sgAccEN=TRUE, do.sgAnglex=FALSE, do.sgAngley=FALSE, do.sgAnglez=FALSE,
...)Directory where the accelerometer files are stored or list of accelerometer filenames and directories
Directory where the output needs to be stored. Note that this function will attempt to create folders in this directory and uses those folder to organise output
File index to start with (default = 1). Index refers to the filenames sorted in increasing order
File index to finish with (defaults to number of files available)
see g.getmeta
see g.getmeta
see g.getmeta
If the datadir is a folder then the study will be given the name of the data directory. If datadir is a list of filenames then the studyname will be used as name for the analysis
if TRUE, calculate metric BFEN with band-pass filter configuration set by
lb and hb, see g.getmeta
if TRUE (default), calculate metric ENMO, see g.getmeta
if TRUE, calculate metric LFENMO with low-pass filter configuration set by
hb,see g.getmeta
if TRUE, calculate metric EN, see g.getmeta
if TRUE, calculate metric HFEN with low-pass filter configuration set by
hb, see g.getmeta
if TRUE, calculate metric HFENplus with band-pass filter configuration set
by lb and hb, see g.getmeta
if TRUE, calculate metric MAD (Mean Amplitude Deviation), see g.getmeta
if TRUE, calculate the angle of the x-axis relative to the horizontal plane (degrees) utilizing all three axes
if TRUE, calculate the angle of the y-axis relative to the horizontal plane (degrees) utilizing all three axes
if TRUE, calculate the angle of the z-axis relative to the horizontal plane (degrees) utilizing all three axes
if TRUE (default), calculate metric ENMOa which is equal to metric ENMO but with the absolute taken from the Euclidean norm minus one.
see g.getmeta
see g.getmeta
see g.getmeta
see g.getmeta
see g.getmeta
see g.getmeta
see g.getmeta
see g.getmeta
see g.getmeta
see g.getmeta
see g.getmeta
see g.getmeta
see g.getmeta
see g.getmeta
see g.getmeta
see g.getmeta
Whether to apply auto-calibration or not, see g.calibrate. Default and recommended setting is TRUE
lower boundary of the frequency filter (in Hertz)
upper boundary of the frequency filter (in Hertz), see g.getmeta
order of the frequency filter, see g.getmeta
see g.calibrate the minimum required acceleration value (in g) on both sides of 0 g for each axis. Used to judge whether the sphere is sufficiently populated
see g.calibrate the minimum number of hours the code needs to read for the autocalibration procedure to be effective (only sensitive to multitudes of 12 hrs, other values will be ceiled). After loading these hours only extra data is loaded if calibration error has not be reduced to under 0.01 g.
see g.calibrate if TRUE will print a summary when done
Whether to print the filename before before analysing it (default is FALSE). Printing the filename can be useful to investigate problems (e.g. to verify that which file is being read).
Overwrite previously generated milestone data by this function for this particular dataset. If FALSE then it will skip the previously processed files (default = FALSE).
Default "retrieve". Option to use backed-up calibration coefficient instead of deriving the calibration coefficients when analysing the same file twice, see details.
Optional functionality. Character pointing at a csv file holding the relationship between device serial numbers (first column) and measurement dates of interest (second and third column). The date format should be dd/mm/yyyy. And the first row if the csv file is assumed to have a character variable names, e.g. "serialnumber" "Day1" and "Day2" respectively. Raw data will be extracted and stored in the output directory in a new subfolder named 'raw'.
Hour at which days start and end (default = 0), value = 4 would mean 4 am
Optional, provide dynamic range for accelerometer data to overwrite hardcoded 6 g for GENEA and 8 g for other brands
Only functional for AX3 cwa data at the moment. Timezone in which the accelerometer was configured. Only use this argument if the timezone of configuration and timezone in which recording took place are different.
Boolean whether to use multi-core processing (only works if at least 4 CPU cores are available.
Minimum File size in MB required to enter processing, default 2MB. This argument can help to avoid having short uninformative files to enter the analyses. Given that a typical accelerometer collects several MBs per hour, the default setting should only skip the very short files.
External function object to be applied to raw data. See details applyExtFunction.
Boolean, see g.getmeta)
Boolean, see g.getmeta)
Boolean, see g.getmeta)
Boolean, see g.getmeta)
Any input arguments needed for function read.myacc.csv if you are working with a non-standard csv formatted files.
The function provides no values, it only ensures that the output from other functions is stored in .RData(one file per accelerometer file) in folder structure
Argument backup.cal.coef has two usecase. Use case 1: If the auto-calibration fails then the user has the option to provide back-up calibration coefficients via this argument. The value of the argument needs to be the name and directory of a csv-spreadsheet with the following column names and subsequent values: 'filename' with the names of accelerometer files on which the calibration coefficients need to be applied in case auto-calibration fails; 'scale.x', 'scale.y', and 'scale.z' with the scaling coefficients; 'offset.x', 'offset.y', and 'offset.z' with the offset coefficients, and; 'temperature.offset.x', 'temperature.offset.y', and 'temperature.offset.z' with the temperature offset coefficients. This can be useful for analysing short lasting laboratory experiments with insufficient sphere data to perform the auto-calibration, but for which calibration coefficients can be derived in an alternative way. It is the users responsibility to compile the csv-spreadsheet. Instead of building this file the user can also
Use case 2: The user wants to avoid performing the auto-calibration repeatedly on the same file. If backup.cal.coef value is set to "retrieve" (default) then GGIR will look out for the data_quality_report.csv file in the outputfolder QC, which holds the previously generated calibration coefficients. If you do not want this happen, then deleted the data_quality_report.csv from the QC folder or set it to value "redo".
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
van Hees VT, Fang Z, Langford J, Assah F, Mohammad A, da Silva IC, Trenell MI, White T, Wareham NJ, Brage S. Auto-calibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents. J Appl Physiol (1985). 2014 Aug 7
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).
# NOT RUN {
datafile = "C:/myfolder/mydata"
outputdir = "C:/myresults"
g.part1(datadir,outputdir)
# }
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