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actimetrics workflow

actimetrics bundles a small set of actigraphy helpers for raw preprocessing, summary metrics, count overlays, and MIMS-oriented processing.

Read data

path <- actiread::acti_example_gt3x()
data <- actiread::acti_read_gt3x(path, verbose = FALSE)
data <- data[1:12000, ]

Summary metrics

summary <- acti_calculate_measures(
  data,
  calculate_mims = FALSE,
  calculate_ac = FALSE,
  flag_data = FALSE
)
#> Fixing Zeros with fix_zeros
#> Calculating ai0
#> Calculating MAD
#> Joining AI and MAD
summary
#> # A tibble: 2 × 9
#>   time                   AI    SD  SD_t AI_DEFINED   MAD MEDAD mean_r ENMO_t
#>   <dttm>              <dbl> <dbl> <dbl>      <dbl> <dbl> <dbl>  <dbl>  <dbl>
#> 1 2019-09-17 18:40:00  23.2  1.87  1.85       1.33 1.09  0.630   1.65  0.688
#> 2 2019-09-17 18:41:00  22.9  1.54  1.53       1.08 0.853 0.552   1.69  0.708

Counts and wear

counts <- acti_calculate_counts(data)
wear <- acti_calculate_nonwear(counts)
head(counts)
head(wear)

MIMS preprocessing

processed <- mims_default_processing(data[1:6000, ], round_after_processing = TRUE)
#> Running extrapolation
#> Running filtering
head(processed)
#>     HEADER_TIME_STAMP     X Y     Z
#> 1 2019-09-17 18:40:00 0.000 0 0.000
#> 2 2019-09-17 18:40:00 0.000 0 0.003
#> 3 2019-09-17 18:40:00 0.000 0 0.012
#> 4 2019-09-17 18:40:00 0.000 0 0.033
#> 5 2019-09-17 18:40:00 0.001 0 0.070
#> 6 2019-09-17 18:40:00 0.001 0 0.127

Calibration

Calibration uses the van Hees method as implemented by agcounts, which is the same approach typically exposed through GGIR.

calibrated <- acti_calibrate(data)
get_transformations(calibrated)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.