Package {actinet}

Title: Estimate Human Activity from 'Accelerometry' Data
Version: 0.2.0
Description: Interfaces the 'actinet' Python module https://github.com/OxWearables/actinet for an activity classification model based on self-supervised learning for wrist-worn accelerometer data.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: assertthat, curl, magrittr, readr, reticulate (≥ 1.42.0)
Suggests: tidyr, dplyr, ggplot2, testthat (≥ 3.0.0)
Config/testthat/edition: 3
Config/roxygen2/version: 8.0.0
Language: en-US
URL: https://github.com/jhuwit/actinet
BugReports: https://github.com/jhuwit/actinet/issues
NeedsCompilation: no
Packaged: 2026-07-28 01:03:00 UTC; johnmuschelli
Author: John Muschelli ORCID iD [aut, cre]
Maintainer: John Muschelli <muschellij2@gmail.com>
Repository: CRAN
Date/Publication: 2026-08-05 17:50:02 UTC

Load Actinet Model

Description

Load Actinet Model

Usage

ac_load_model(
  classifier = c("walmsley", "willetts"),
  model_path = NULL,
  check_md5 = TRUE,
  force_download = FALSE,
  as_python = TRUE
)

ac_model_filename(classifier = c("walmsley", "willetts"))

ac_download_model(
  model_path,
  classifier = c("walmsley", "willetts"),
  check_md5 = TRUE,
  ...
)

Arguments

classifier

type of the model: either walmsley or willetts

model_path

the file path to the model. If on disk, this can be re-used and not re-downloaded. If NULL, will download to the temporary directory

check_md5

Do a MD5 checksum on the file

force_download

force a download of the model, even if the file exists

as_python

Keep model object as a python object

...

for ac_download_model, additional arguments to pass to curl::curl_download()

Value

A model from Python. ac_download_model returns a model file path.

Run Actinet Model on Data

Description

Run Actinet Model on Data

Usage

actinet(
  file,
  outdir = tempfile(),
  classifier = NULL,
  sample_rate = NULL,
  model_path = NULL,
  pytorch_device = NULL,
  no_hmm = FALSE,
  require_sleep_above = NULL,
  single_sleep_block = FALSE,
  force_download = FALSE,
  exclude_first_last = NULL,
  exclude_wear_below = NULL,
  csv_start_row = NULL,
  csv_txyz = NULL,
  csv_txyz_idxs = NULL,
  csv_date_format = NULL,
  calibration_stdtol_min = NULL,
  plot_activity = FALSE,
  cache_classifier = FALSE,
  verbose = TRUE
)

Arguments

file

accelerometry file to process, including CSV, CWA, GT3X, and GENEActiv bin files

outdir

folder location to save output files

classifier

Enter custom activity classifier file to use. Default: walmsley (Walmsley2020 annotations of activity intensity). Can also enter path to local classifier (.joblib.lzma) file.

sample_rate

Sample rate for measurement, otherwise inferred.

model_path

the file path to the model. If on disk, this can be re-used and not re-downloaded. If NULL, will download to the temporary directory

pytorch_device

torch device to use, e.g.: 'cpu' or 'cuda:0'. Default: 'mps' if available, otherwise 'cpu'

no_hmm

Disable HMM post-processing

require_sleep_above

Require sleep blocks to exceed a minimum duration, otherwise be classified as sedentary. Pass values as strings, e.g.: '2H', '30min'. Default: None (no requirement)

single_sleep_block

Recognize only one sleep block per day, all other sleep blocks will be converted to sedentary

force_download

Force download of classifier file

exclude_first_last

first,last,both Exclude first, last or both days of data. Default: None (no exclusion)

exclude_wear_below

Exclude days with wear time below threshold. Pass values as strings, e.g.: '12H', '30min'. Default: None (no exclusion)

csv_start_row

Row number to start reading a CSV file. Default: 1 (First row)

csv_txyz

CSV_TXYZ Column names for time, x, y, z in CSV files. Comma_ separated string. Default: 'time,x,y,z'

csv_txyz_idxs

Column indices for time,x,y,z (0_indexed, e.g., '0,1,2,3'). Overrides csv_txyz.

csv_date_format

Date time format for csv file when reading a csv file. See https://docs.python.org/3/library/datetime.html#strftime_and_strptime_format_codes for more possible codes. Default: '%Y-%m-%d %H:%M:%S.%f' (e.g. '2023-10-01 12:34:56.789')

calibration_stdtol_min

Minimum standard deviation tolerance (g) for detecting stationary periods for calibration. Default: None

plot_activity

Plot the predicted activity labels

cache_classifier

Download and cache classifier file and model modules for offline usage

verbose

print diagnostic messages

Value

A list of the results (data.frame), summary of the results, adjusted summary of the results, and information about the data.

Examples


  library(magrittr)
  file = system.file("extdata/P30_wrist100.csv.gz", package = "actinet")
  if (actinet_check()) {
    out = actinet(file = file)
    data = readr::read_csv(out$outfiles[1])
    daily_data = readr::read_csv(out$outfiles[3])
  }

Check the actinet Python Module

Description

Check the actinet Python Module

Usage

have_actinet()

actinet_check()

actinet_version()

Value

A logical value indicating whether the actinet Python module is available.

Examples


  if (have_actinet()) {
     actinet_version()
  }