import_activpal3_csv imports the raw multi-channel accelerometer data stored in ActivPal3 csv format by converting the accelerometer values (in digital voltage values) to \(g\) unit.

import_activpal3_csv(filepath, header = FALSE)

Arguments

filepath

string. The filepath of the input data.

header

boolean. If TRUE, the input csv file will have column names in the first row.

Value

dataframe. The imported multi-channel accelerometer signal, with the first column being the timestamps in POSXlct format, and the rest columns being accelerometer values in \(g\) unit.

Details

ActivPal 3 sensors have known dynamic range to be \((-2g, +2g)\). And the sensor stores values using 8-bit memory storage. So, the digital voltage values may be converted to \(g\) unit using following equation.

$$x_g = \frac{x_{voltage} - 127}{2^8} * 4$$

How is it used in MIMS-unit algorithm?

This function is a File IO function that is used to import data from ActivPal3 devices during algorithm validation.

See also

Examples

default_ops = options() options(digits.secs=3) # Use the sample activpal3 csv file provided by the package filepath = system.file('extdata', 'activpal3.csv', package='MIMSunit') # Check the csv format readLines(filepath)[1:5]
#> [1] "43265.5705908565,112,140,58" "43265.5705914352,124,132,59" #> [3] "43265.5705920139,116,126,61" "43265.5705925926,116,121,64" #> [5] "43265.5705931713,102,113,68"
# Load the file, in our case without header df = import_activpal3_csv(filepath, header=FALSE) # Check loaded file head(df)
#> HEADER_TIME_STAMP X Y Z #> 1 2018-06-14 13:41:39.050 -0.234375 0.203125 -1.078125 #> 2 2018-06-14 13:41:39.100 -0.046875 0.078125 -1.062500 #> 3 2018-06-14 13:41:39.150 -0.171875 -0.015625 -1.031250 #> 4 2018-06-14 13:41:39.200 -0.171875 -0.093750 -0.984375 #> 5 2018-06-14 13:41:39.250 -0.390625 -0.218750 -0.921875 #> 6 2018-06-14 13:41:39.300 -0.421875 -0.546875 -0.843750
# Check more summary(df)
#> HEADER_TIME_STAMP X Y #> Min. :2018-06-14 13:41:39.04 Min. :-1.56250 Min. :-1.953125 #> 1st Qu.:2018-06-14 13:42:41.53 1st Qu.: 0.04688 1st Qu.: 0.046875 #> Median :2018-06-14 13:43:44.01 Median : 0.06250 Median : 0.046875 #> Mean :2018-06-14 13:43:44.01 Mean : 0.02084 Mean :-0.006097 #> 3rd Qu.:2018-06-14 13:44:46.50 3rd Qu.: 0.06250 3rd Qu.: 0.046875 #> Max. :2018-06-14 13:45:49.00 Max. : 0.90625 Max. : 1.218750 #> Z #> Min. :-1.9688 #> 1st Qu.:-1.0156 #> Median : 1.1250 #> Mean : 0.3263 #> 3rd Qu.: 1.1250 #> Max. : 1.9688
# Restore default options options(default_ops)