actimetrics
Create Metrics Actigraphy and Activity Analysis
Provides functions for calibrating, counting, and summarizing
actigraphy and activity data into specific metrics and sleep measures.
The metrics include activity counts, step counts, activity index,
Monitor Independent Movement Summary Unit (MIMS), mean amplitude
deviation (MAD), and provides wrappers for sleep estimation from
activity counts using Tudor-Locke (2014)
actimetrics
actimetrics provides helpers for actigraphy preprocessing, summary
statistics, count-based overlays, and MIMS-oriented processing.
Core entry points:
calculate_measures()for summary metrics such as AI, MAD, MIMS, and ACacti_calculate_counts()andacti_process()for count and wear overlaysmims_default_processing()for the default MIMS preprocessing chainacti_calibrate()for calibration throughagcountsusing the van Hees method commonly implemented inGGIR
Installation
You can install actimetrics from GitHub with:
# install.packages("remotes")
remotes::install_github("jhuwit/actimetrics")
Quick Start
library(actibase)
library(actiread)
library(actimetrics)
path <- actiread::acti_example_gt3x()
data <- actiread::acti_read_gt3x(path, verbose = FALSE)
We can calculate minute-level Activity Counts using the agcounts
package.
counts <- acti_calculate_counts(data)
#> [1] "Creating Downsampled Data"
#> [1] "Filtering Data"
#> [1] "Trimming Data"
#> [1] "Getting data back to 10Hz for accumulation"
#> [1] "Summing epochs"
counts
#> # A tibble: 280 × 5
#> time axis1 axis2 axis3 counts
#> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 2019-09-17 18:40:00 0 0 0 0
#> 2 2019-09-17 18:41:00 0 0 0 0
#> 3 2019-09-17 18:42:00 0 0 0 0
#> 4 2019-09-17 18:43:00 0 0 0 0
#> 5 2019-09-17 18:44:00 0 0 0 0
#> 6 2019-09-17 18:45:00 0 0 0 0
#> 7 2019-09-17 18:46:00 0 0 0 0
#> 8 2019-09-17 18:47:00 0 0 0 0
#> 9 2019-09-17 18:48:00 0 0 0 0
#> 10 2019-09-17 18:49:00 0 0 0 0
#> # ℹ 270 more rows
get_transformations(counts)
#> [1] "acti_calculate_counts:sample_rate_attribute_changed_to_1"
#> [2] "acti_calculate_counts:counts_created_at_60s_epoch"
#> [3] "acti_read_gt3x:timezone_GMT_forced"
#> [4] "acti_read_gt3x:timezone_Etc/GMT-4_applied"
#> [5] "acti_read_gt3x:attributes_set"
#> [6] "acti_fill_zeros:filled_zeros"
#> [7] "acti_read_gt3x:data_read"
From this, we can calculate wear flags for each minute according to the Choi or Troiano methods.
wear = acti_calculate_nonwear(counts)
#> Joining with `by = join_by(timestamp)`
get_transformations(wear)
#> [1] "acti_calculate_wear:choi_wear_algorithm_run_using_magnitude"
#> [2] "acti_calculate_counts:sample_rate_attribute_changed_to_1"
#> [3] "acti_calculate_counts:counts_created_at_60s_epoch"
#> [4] "acti_read_gt3x:timezone_GMT_forced"
#> [5] "acti_read_gt3x:timezone_Etc/GMT-4_applied"
#> [6] "acti_read_gt3x:attributes_set"
#> [7] "acti_fill_zeros:filled_zeros"
#> [8] "acti_read_gt3x:data_read"
#> [9] "acti_calculate_counts:sample_rate_attribute_changed_to_1"
#> [10] "acti_calculate_counts:counts_created_at_60s_epoch"
#> [11] "acti_read_gt3x:timezone_GMT_forced"
#> [12] "acti_read_gt3x:timezone_Etc/GMT-4_applied"
#> [13] "acti_read_gt3x:attributes_set"
#> [14] "acti_fill_zeros:filled_zeros"
#> [15] "acti_read_gt3x:data_read"
We can then merge them so the counts have the wear flags:
result = dplyr::full_join(counts, wear, by = "time") %>%
dplyr::mutate(wear = ifelse(is.na(wear), FALSE, wear))
get_transformations(result)
#> [1] "acti_calculate_counts:sample_rate_attribute_changed_to_1"
#> [2] "acti_calculate_counts:counts_created_at_60s_epoch"
#> [3] "acti_read_gt3x:timezone_GMT_forced"
#> [4] "acti_read_gt3x:timezone_Etc/GMT-4_applied"
#> [5] "acti_read_gt3x:attributes_set"
#> [6] "acti_fill_zeros:filled_zeros"
#> [7] "acti_read_gt3x:data_read"
These functions are combined in acti_process, but the transformations
are retained correctly:
processed = acti_process(data)
#> [1] "Creating Downsampled Data"
#> [1] "Filtering Data"
#> [1] "Trimming Data"
#> [1] "Getting data back to 10Hz for accumulation"
#> [1] "Summing epochs"
processed
#> # A tibble: 40 × 6
#> time axis1 axis2 axis3 counts wear
#> <dttm> <dbl> <dbl> <dbl> <dbl> <lgl>
#> 1 2019-09-17 22:40:00 5223 9743 8142 13729 TRUE
#> 2 2019-09-17 22:41:00 9298 9061 4099 13615 TRUE
#> 3 2019-09-17 22:42:00 4382 4371 3495 7108 TRUE
#> 4 2019-09-17 22:43:00 3265 3153 2540 5201 TRUE
#> 5 2019-09-17 22:44:00 1415 874 900 1891 TRUE
#> 6 2019-09-17 22:45:00 0 0 0 0 TRUE
#> 7 2019-09-17 22:46:00 115 218 140 283 TRUE
#> 8 2019-09-17 22:47:00 0 0 0 0 TRUE
#> 9 2019-09-17 22:48:00 0 0 0 0 TRUE
#> 10 2019-09-17 22:49:00 0 0 0 0 TRUE
#> # ℹ 30 more rows
get_transformations(processed)
#> [1] "acti_process:counts_wear_merge"
#> [2] "acti_calculate_counts:sample_rate_attribute_changed_to_1"
#> [3] "acti_calculate_counts:counts_created_at_60s_epoch"
#> [4] "acti_resample:sample_rate_attribute_changed_to_30"
#> [5] "acti_resample:linear_resampled_to_30Hz"
#> [6] "acti_read_gt3x:timezone_GMT_forced"
#> [7] "acti_read_gt3x:timezone_Etc/GMT-4_applied"
#> [8] "acti_read_gt3x:attributes_set"
#> [9] "acti_fill_zeros:filled_zeros"
#> [10] "acti_read_gt3x:data_read"
summary <- acti_calculate_measures(
data,
calculate_mims = FALSE,
calculate_ac = TRUE,
flag_data = FALSE
)
#> Fixing Zeros with fix_zeros
#> Calculating ai0
#> Calculating MAD
#> Joining AI and MAD
#> Calculating AC
#> [1] "Creating Downsampled Data"
#> [1] "Filtering Data"
#> [1] "Trimming Data"
#> [1] "Getting data back to 10Hz for accumulation"
#> [1] "Summing epochs"
#> Joining AC
processed <- mims_default_processing(data[1:6000, ])
#> Warning in get_dynamic_range(data, dynamic_range): No dynamic range found in
#> header, using data estimate
#> Running extrapolation
#> Running filtering
#> Registered S3 methods overwritten by 'signal':
#> method from
#> print.freqs gsignal
#> print.freqz gsignal
#> print.grpdelay gsignal
#> plot.grpdelay gsignal
#> print.impz gsignal
#> print.specgram gsignal
#> plot.specgram gsignal
Calibration uses the van Hees method as implemented by agcounts, which
is the same approach typically exposed through GGIR.
calibrated <- acti_calibrate(data)
#> Filling Zeros
#> Running agcounts::agcalibrate
#> Loading chunk: 1
#>
#> There is not enough data to perform the GGIR calibration method. Returning data as read by read.gt3x.
get_transformations(calibrated)
#> [1] "acti_calibrate:agcounts_calibrated"
#> [2] "acti_fill_zeros:filled_zeros"
#> [3] "acti_read_gt3x:timezone_GMT_forced"
#> [4] "acti_read_gt3x:timezone_Etc/GMT-4_applied"
#> [5] "acti_read_gt3x:attributes_set"
#> [6] "acti_fill_zeros:filled_zeros"
#> [7] "acti_read_gt3x:data_read"
#> [8] "acti_fill_zeros:filled_zeros"
#> [9] "acti_read_gt3x:timezone_GMT_forced"
#> [10] "acti_read_gt3x:timezone_Etc/GMT-4_applied"
#> [11] "acti_read_gt3x:attributes_set"
#> [12] "acti_fill_zeros:filled_zeros"
#> [13] "acti_read_gt3x:data_read"