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activerse

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activerse is a lightweight helper package that attaches actibase, actiread, actimetrics, actisensorlog, actiwalkability, and actiquantiles together.

It is designed to behave a bit like tidyverse: load one package, then use the functions from the actigraphy packages directly.

Installation

# install.packages("remotes")
remotes::install_github("jhuwit/activerse")

Packages

Package Repository R CMD check
actibase jhuwit/actibase R CMD check
actiread jhuwit/actiread R CMD check
actimetrics jhuwit/actimetrics R CMD check
actisensorlog jhuwit/actisensorlog R CMD check
actiwalkability jhuwit/actiwalkability R CMD check
actiquantiles jhuwit/actiquantiles R CMD check

Load the packages

library(activerse)

# activerse-load:start
search()[grepl("^package:(actibase|actiread|actimetrics|actisensorlog|actiwalkability|actiquantiles)$", search())]
#> [1] "package:actiquantiles"   "package:actiwalkability"
#> [3] "package:actisensorlog"   "package:actimetrics"    
#> [5] "package:actiread"        "package:actibase"
# activerse-load:end

Example data

The packages ship with small example datasets that are useful for quick tests.

data(acti_raw_data, package = "actibase")
data(acti_count_data, package = "actimetrics")

head(acti_raw_data)
#>                  time     X      Y     Z
#> 1 2019-09-17 18:40:00 0.000  0.008 0.996
#> 2 2019-09-17 18:40:00 0.016  0.000 1.008
#> 3 2019-09-17 18:40:00 0.020 -0.008 1.004
#> 4 2019-09-17 18:40:00 0.016 -0.012 1.012
#> 5 2019-09-17 18:40:00 0.016 -0.008 1.008
#> 6 2019-09-17 18:40:00 0.008 -0.008 1.008
head(acti_count_data)
#>                  time axis1 axis2 axis3 counts
#> 1 2019-09-17 18:40:00  5435  9659  8253  13818
#> 2 2019-09-17 18:41:00  9125  9197  4131  13598
#> 3 2019-09-17 18:42:00  4404  4367  3494   7119
#> 4 2019-09-17 18:43:00  3267  3170  2543   5214
#> 5 2019-09-17 18:44:00  1405   896   894   1891
#> 6 2019-09-17 18:45:00     0     0     0      0

Use actimetrics

Once activerse is attached, functions from the activerse packages are available directly. You can also call them with the relevant pkg:: prefix if you prefer being explicit.

Calculate ENMO

enmo <- acti_calculate_enmo(data = acti_raw_data)
head(enmo)
#> # A tibble: 6 × 2
#>   HEADER_TIME_STAMP   ENMO_t
#>   <dttm>               <dbl>
#> 1 2019-09-17 18:40:00 0.688 
#> 2 2019-09-17 18:41:00 0.708 
#> 3 2019-09-17 18:42:00 0.183 
#> 4 2019-09-17 18:43:00 0.150 
#> 5 2019-09-17 18:44:00 0.0278
#> 6 2019-09-17 18:45:00 0.0139

Calculate activity index

activity_index <- acti_calculate_activity_index(
  acti_raw_data,
  unit = "1 min",
  ensure_all_time = TRUE,
  verbose = FALSE
)

head(activity_index)
#> # A tibble: 6 × 2
#>   HEADER_TIME_STAMP      AI
#>   <dttm>              <dbl>
#> 1 2019-09-17 18:40:00 23.2 
#> 2 2019-09-17 18:41:00 22.9 
#> 3 2019-09-17 18:42:00 11.9 
#> 4 2019-09-17 18:43:00 10.5 
#> 5 2019-09-17 18:44:00  2.35
#> 6 2019-09-17 18:45:00  0

Calculate MAD

mad <- acti_calculate_mad(data = acti_raw_data)
head(mad)
#> # A tibble: 6 × 8
#>   HEADER_TIME_STAMP       SD   SD_t AI_DEFINED    MAD   MEDAD mean_r ENMO_t
#>   <dttm>               <dbl>  <dbl>      <dbl>  <dbl>   <dbl>  <dbl>  <dbl>
#> 1 2019-09-17 18:40:00 1.87   1.85        1.33  1.09   0.630     1.65 0.688 
#> 2 2019-09-17 18:41:00 1.54   1.53        1.08  0.853  0.552     1.69 0.708 
#> 3 2019-09-17 18:42:00 0.253  0.204       0.278 0.207  0.188     1.14 0.183 
#> 4 2019-09-17 18:43:00 0.228  0.175       0.228 0.191  0.178     1.10 0.150 
#> 5 2019-09-17 18:44:00 0.0815 0.0637      0.309 0.0300 0.00330   1.02 0.0278
#> 6 2019-09-17 18:45:00 0      0           0     0      0         1.01 0.0139

More examples

actimetrics also includes functions for wear/non-wear detection, step count estimation, and other processing helpers:

acti_process(acti_raw_data)
acti_calculate_wear(acti_raw_data)
acti_calculate_nonwear(acti_raw_data)
acti_calculate_stepcount(acti_raw_data)

Running Walking with Different Python Environments

If you want to use both walking estimation from forest and stepcount, you can run them in separate R processes using callr to avoid Python package conflicts. Here’s how you can do it:

library(callr)
library(activerse)
gt3x_path = acti_example_gt3x()
data <- acti_read_gt3x(gt3x_path, cleanup = TRUE)

stepcount_callr = function(data,
                           ...) {
  
  reticulate::py_require("stepcount==3.11.0", python_version = "3.10")
  sc <- reticulate::import("stepcount")
  stepcount::stepcount_check()
  
  res = actimetrics::acti_calculate_stepcount(data, ...)
  return(res)
}

# 2. Run the isolated background R process
result <- callr::r(
  func = stepcount_callr,
  show = TRUE,
  args = list(data = data) # Safely injects data into the process
)
#> Loading model...
#> Downloading https://wearables-files.ndph.ox.ac.uk/files/models/stepcount/ssl-20230208.joblib.lzma...
#> Checking Data
#> Writing file to CSV...
#> Reading in Data for Stepcount
#> Predicting from Model
#> Running step counter...
#> Gravity calibration...Gravity calibration... Done! (0.04s)
#> Nonwear detection...Nonwear detection... Done! (0.05s)
#> Resampling...Resampling... Done! (0.05s)
#> Defining windows...
#>   0%|          | 0/241 [00:00<?, ?it/s]100%|██████████| 241/241 [00:00<00:00, 2300.90it/s]
#> Using local /Users/johnmuschelli/Library/Caches/org.R-project.R/R/reticulate/uv/cache/archive-v0/12AzDesQwp07_KC6/lib/python3.10/site-packages/stepcount/torch_hub_cache/OxWearables_ssl-wearables_v1.0.0
#> Classifying windows...
#>   0%|          | 0/1 [00:00<?, ?it/s]100%|██████████| 1/1 [00:02<00:00,  2.04s/it]
#> Processing Result
#> /usr/local/Cellar/python@3.10/3.10.20_1/Frameworks/Python.framework/Versions/3.10/lib/python3.10/multiprocessing/resource_tracker.py:224: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
#>   warnings.warn('resource_tracker: There appear to be %d '
head(result)
#> # A tibble: 6 × 3
#>   time                steps walking
#>   <dttm>              <dbl> <lgl>  
#> 1 2019-09-17 18:40:00    34 TRUE   
#> 2 2019-09-17 18:41:00    82 TRUE   
#> 3 2019-09-17 18:42:00   104 TRUE   
#> 4 2019-09-17 18:43:00   105 TRUE   
#> 5 2019-09-17 18:44:00    15 TRUE   
#> 6 2019-09-17 18:45:00     0 FALSE

forest_callr = function(data,
                        ...) {
  reticulate::py_require(
    "git+https://github.com/onnela-lab/forest@45fb41038bd46c25d9e6a4442aa74fa03b501317", 
    python_version = "3.11")
  fr = reticulate::import("forest")
  oak = fr$oak$base
  oak
  res = actimetrics::acti_calculate_forest(data, ...)
  return(res)
}

# 2. Run the isolated background R process
fresult <- callr::r(
  func = forest_callr,
  show = TRUE,
  args = list(data = data) # Safely injects data into the process
)
#> Preprocessing Bout
#> Bout is Preprocessed
#> OAK: Find walking is done
head(fresult)
#> # A tibble: 6 × 2
#>   time                 steps
#>   <dttm>               <dbl>
#> 1 2019-09-17 18:40:00  12.3 
#> 2 2019-09-17 18:41:00  53.9 
#> 3 2019-09-17 18:42:00 105.  
#> 4 2019-09-17 18:43:00 105.  
#> 5 2019-09-17 18:44:00   6.75
#> 6 2019-09-17 18:45:00   0

In the previous example we use reticulate::py_require to install python dependencies on demand, but recommend an environment (e.g. conda) with the required packages pre-installed for better performance and use on multiple subjects.

Updating activerse

When a new package is added to activerse, update the package list, the README, and the attach helper by running:

./scripts/update-activerse.R actisensorlog

If the new package lives under a different GitHub owner, pass --owner=:

./scripts/update-activerse.R --owner=some-owner actisensorlog

The script updates DESCRIPTION, R/activerse.R, and both README files in one pass.

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Attach Activerse Packages

jhuwit.github.io/activerse/

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