R/aggregate_over_epoch.R
aggregate_for_mims.Rdaggregate_for_mims returns a dataframe with integrated values by
trapzoidal method over each epoch for each column. The epoch start time will
be used as timestamp in the first column.
aggregate_for_mims(
df,
epoch,
method = c("trapz", "power", "mean_by_time", "mean_by_size", "sum"),
rectify = TRUE,
st = NULL
)dataframe of accelerometer data in mhealth format. First column should be timestamps in POSIXt format.
string. Any format that is acceptable by argument breaks
in method cut.POSIXt.For example, "1 sec", "1 min", "5
secs", "10 mins".
string. Integration methods. Supported strings include: "trapz", "power", "sum", "meanBySecond", "meanBySize". Default is "trapz".
logical. If TRUE, input data will be rectified before integration. Default is TRUE.
character or POSIXct timestamp. An optional start time you can set to force the epochs generated by referencing this start time. If it is NULL, the function will use the first timestamp in the timestamp column as start time to generate epochs. This is useful when you are processing a stream of data and want to use a common start time for segmenting data. Default is NULL.
dataframe. The returned dataframe will have the same format as input dataframe.
This function accepts a dataframe (in mhealth accelerometer data format) and
computes its aggregated values over each fixed epoch using different
integration methods (default is trapzoidal method, other methods are not used
by mims unit algorithm) for each value columns. The returned dataframe will
have the same number of columns as input dataframe, and have the same
datetime format as input dataframe in the timestamp column. The trapzoidal
method used in the function is based on trapz.
If epoch argument is not provided or is NULL, the
function will treat the input dataframe as a single epoch.
If the number of samples in one segment is less than 90 samples, the aggregation result will be -1 (marker of invalid value).
This function is used in
mims-unit algorithm after filtering (iir). The filtered
signal will be rectified and integrated to get mims unit values for each
axis using this function.
aggregate_for_orientation for aggregating to get
accelerometer orientation estimation for each epoch.
Other aggregate functions:
aggregate_for_orientation()
# sample data
df = sample_raw_accel_data
head(df)
#> HEADER_TIME_STAMP X Y Z
#> 1 2016-01-15 11:00:00 0.148 -0.438 0.016
#> 2 2016-01-15 11:00:00 0.215 -0.418 -0.023
#> 3 2016-01-15 11:00:00 0.266 -0.402 -0.012
#> 4 2016-01-15 11:00:00 0.336 -0.430 0.012
#> 5 2016-01-15 11:00:00 0.430 -0.320 0.000
#> 6 2016-01-15 11:00:00 0.535 -0.258 0.004
# epoch set to 5 seconds, and method set to "trapz"
aggregate_for_mims(df, epoch = '5 sec', method='trapz')
#> HEADER_TIME_STAMP AGGREGATED_X AGGREGATED_Y AGGREGATED_Z
#> 1 2016-01-15 11:00:00 6.359663 2.420398 1.62992
#> 2 2016-01-15 11:00:05 -1.000000 -1.000000 -1.00000
# epoch set to 1 second, method set to "sum"
aggregate_for_mims(df, epoch = '1 sec', method='sum')
#> HEADER_TIME_STAMP AGGREGATED_auc_values AGGREGATED_X AGGREGATED_Y Z
#> 1 2016-01-15 11:00:00 99.542 NA NA NA
#> 2 2016-01-15 11:00:00 34.291 NA NA NA
#> 3 2016-01-15 11:00:00 22.296 NA NA NA
#> 4 2016-01-15 11:00:01 97.332 NA NA NA
#> 5 2016-01-15 11:00:01 36.783 NA NA NA
#> 6 2016-01-15 11:00:01 25.970 NA NA NA
#> 7 2016-01-15 11:00:02 104.054 NA NA NA
#> 8 2016-01-15 11:00:02 42.027 NA NA NA
#> 9 2016-01-15 11:00:02 27.141 NA NA NA
#> 10 2016-01-15 11:00:03 104.749 NA NA NA
#> 11 2016-01-15 11:00:03 39.400 NA NA NA
#> 12 2016-01-15 11:00:03 26.702 NA NA NA
#> 13 2016-01-15 11:00:04 103.642 NA NA NA
#> 14 2016-01-15 11:00:04 41.445 NA NA NA
#> 15 2016-01-15 11:00:04 28.518 NA NA NA
#> 16 2016-01-15 11:00:05 100.172 NA NA NA
#> 17 2016-01-15 11:00:05 28.005 NA NA NA
#> 18 2016-01-15 11:00:05 21.359 NA NA NA
#> 19 2016-01-15 11:00:06 NA -1 -1 -1
# epoch set to 1 second, and st set to be 1 second before the start time of the data
# so the first segment will only include data for 1 second, therefore the resulted
# aggregated value for the first segment will be -1 (invalid) because the
# samples are not enough. And the second segment starts from 11:00:01, instead
# of 11:00:02 as shown in prior example,
aggregate_for_mims(df, epoch = '1 sec', method='sum', st=df[1,1] - 1)
#> HEADER_TIME_STAMP AGGREGATED_auc_values AGGREGATED_X AGGREGATED_Y Z
#> 1 2016-01-15 11:00:00 99.542 NA NA NA
#> 2 2016-01-15 11:00:00 34.291 NA NA NA
#> 3 2016-01-15 11:00:00 22.296 NA NA NA
#> 4 2016-01-15 11:00:01 97.332 NA NA NA
#> 5 2016-01-15 11:00:01 36.783 NA NA NA
#> 6 2016-01-15 11:00:01 25.970 NA NA NA
#> 7 2016-01-15 11:00:02 104.054 NA NA NA
#> 8 2016-01-15 11:00:02 42.027 NA NA NA
#> 9 2016-01-15 11:00:02 27.141 NA NA NA
#> 10 2016-01-15 11:00:03 104.749 NA NA NA
#> 11 2016-01-15 11:00:03 39.400 NA NA NA
#> 12 2016-01-15 11:00:03 26.702 NA NA NA
#> 13 2016-01-15 11:00:04 103.642 NA NA NA
#> 14 2016-01-15 11:00:04 41.445 NA NA NA
#> 15 2016-01-15 11:00:04 28.518 NA NA NA
#> 16 2016-01-15 11:00:05 100.172 NA NA NA
#> 17 2016-01-15 11:00:05 28.005 NA NA NA
#> 18 2016-01-15 11:00:05 21.359 NA NA NA
#> 19 2016-01-15 11:00:06 NA -1 -1 -1