This function accepts a dataframe of multi-channel signal, segments it into epoch windows with length specified in breaks.

segment_data(df, breaks, st = NULL)

Arguments

df

dataframe. Input dataframe of the multi-channel signal. The first column is the timestamps in POSXlct format and the following columns are accelerometer values.

breaks

character. An epoch length character that can be accepted by cut.breaks function.

st

character or POSIXct timestamp. An optional start time you can set to force the breaks 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 breaks. 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.

Value

dataframe. The same format as the input dataframe, but with an extra column "SEGMENT" in the end specifies the epoch window a sample belongs to.

How is it used in MIMS-unit algorithm?

This function is a utility function that was used in various part in the algorithm whenever we need to segment a dataframe, e.g., before aggregating values over epoch windows.

See also

Examples

# Use sample data df = sample_raw_accel_data # segment data into 1 minute segments output = segment_data(df, "1 min") # check the 3rd segment, each segment would have 1 minute data summary(output[output['SEGMENT'] == 3,])
#> HEADER_TIME_STAMP X Y Z SEGMENT #> Min. :NA Min. : NA Min. : NA Min. : NA Min. : NA #> 1st Qu.:NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA #> Median :NA Median : NA Median : NA Median : NA Median : NA #> Mean :NA Mean :NaN Mean :NaN Mean :NaN Mean :NaN #> 3rd Qu.:NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA #> Max. :NA Max. : NA Max. : NA Max. : NA Max. : NA
# segment data into 15 second segments output = segment_data(df, "15 sec") # check the 1st segment, each segment would have 15 second data summary(output[output['SEGMENT'] == 1,])
#> HEADER_TIME_STAMP X Y #> Min. :2016-01-15 11:00:00.00 Min. :0.129 Min. :-2.715000 #> 1st Qu.:2016-01-15 11:00:01.50 1st Qu.:1.004 1st Qu.:-0.309000 #> Median :2016-01-15 11:00:03.00 Median :1.205 Median :-0.125000 #> Mean :2016-01-15 11:00:03.00 Mean :1.273 Mean :-0.008275 #> 3rd Qu.:2016-01-15 11:00:04.50 3rd Qu.:1.583 3rd Qu.: 0.011000 #> Max. :2016-01-15 11:00:06.00 Max. :2.652 Max. : 2.871000 #> Z SEGMENT #> Min. :-1.7300 Min. :1 #> 1st Qu.:-0.3490 1st Qu.:1 #> Median :-0.1840 Median :1 #> Mean :-0.2462 Mean :1 #> 3rd Qu.:-0.0740 3rd Qu.:1 #> Max. : 1.1090 Max. :1
# segment data into 1 hour segments output = segment_data(df, "1 hour") # because the input data has only 15 minute data # there will be only 1 segment in the output unique(output['SEGMENT'])
#> SEGMENT #> 1 1
summary(output)
#> HEADER_TIME_STAMP X Y #> Min. :2016-01-15 11:00:00.00 Min. :0.129 Min. :-2.715000 #> 1st Qu.:2016-01-15 11:00:01.50 1st Qu.:1.004 1st Qu.:-0.309000 #> Median :2016-01-15 11:00:03.00 Median :1.205 Median :-0.125000 #> Mean :2016-01-15 11:00:03.00 Mean :1.273 Mean :-0.008275 #> 3rd Qu.:2016-01-15 11:00:04.50 3rd Qu.:1.583 3rd Qu.: 0.011000 #> Max. :2016-01-15 11:00:06.00 Max. :2.652 Max. : 2.871000 #> Z SEGMENT #> Min. :-1.7300 Min. :1 #> 1st Qu.:-0.3490 1st Qu.:1 #> Median :-0.1840 Median :1 #> Mean :-0.2462 Mean :1 #> 3rd Qu.:-0.0740 3rd Qu.:1 #> Max. : 1.1090 Max. :1
# use manually set start time output = segment_data(df, "15 sec", st='2016-01-15 10:59:50.000') # check the 1st segment, because the start time is 10 seconds before the # start time of the actual data, the first segment will only include 5 second # data. summary(output[output['SEGMENT'] == 1,])
#> HEADER_TIME_STAMP X Y #> Min. :2016-01-15 11:00:00.00 Min. :0.129 Min. :-2.715000 #> 1st Qu.:2016-01-15 11:00:01.25 1st Qu.:1.004 1st Qu.:-0.312500 #> Median :2016-01-15 11:00:02.50 Median :1.223 Median :-0.125000 #> Mean :2016-01-15 11:00:02.50 Mean :1.276 Mean :-0.006125 #> 3rd Qu.:2016-01-15 11:00:03.74 3rd Qu.:1.582 3rd Qu.: 0.020000 #> Max. :2016-01-15 11:00:04.99 Max. :2.652 Max. : 2.871000 #> Z SEGMENT #> Min. :-1.730 Min. :1 #> 1st Qu.:-0.350 1st Qu.:1 #> Median :-0.184 Median :1 #> Mean :-0.251 Mean :1 #> 3rd Qu.:-0.074 3rd Qu.:1 #> Max. : 1.109 Max. :1