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Title:

Segments Accelerometry Data into Walking Bouts using Open Source Methods

Version:

0.7.0

Description:

Segments walking from accelerometry data using 'forest' python module https://github.com/onnela-lab/forest from Yi (2025) <doi:10.2196/71375>, 'Verisense' original from Rowlands (2022) <doi:10.1080/02640414.2022.2147134> and 'Verisense' revised from Maylor (2022)<doi:10.3390/s22249984>, and Step Detection Threshold (SDT) from Ducharme (2021) <doi:10.1123/jmpb.2021-0011> methods.

License:

GPL (≥ 3)

Encoding:

UTF-8

Imports:

actibase, assertthat, dplyr, lubridate, magrittr, matrixStats, reticulate (≥ 1.42.0), signal, tidyr

Suggests:

readr, testthat (≥ 3.0.0)

Config/testthat/edition:

Config/roxygen2/version:

8.0.0

NeedsCompilation:

Packaged:

2026-07-23 13:59:14 UTC; johnmuschelli

Author:

John Muschelli

[aut, cre]

Maintainer:

John Muschelli <muschellij2@gmail.com>

Repository:

CRAN

Date/Publication:

2026-08-03 17:40:02 UTC

Estimate Steps with Unified Syntax

Description

Estimate Steps with Unified Syntax

Usage

estimate_steps_forest(data, ...)

have_forest()

estimate_steps_verisense(
  data,
  resample_to_15hz = FALSE,
  ...,
  method = c("original", "revised")
)

estimate_steps_sdt(data, ...)

Arguments

data

A data.frame with a column for time in POSIXct (usually HEADER_TIMESTAMP), and X, Y, Z

...

additional arguments to pass to specific method

resample_to_15hz

resample the data to 15Hz as verisense indicated that was the sample rate this was designed under.

method

Either using the original parameters verisense_count_steps() or the revised parameters verisense_count_steps_revised()

Value

A data.frame of seconds and steps

Examples

run_example = FALSE
csv_file = system.file("test_data_bout.csv", package = "walking")
if (requireNamespace("readr", quietly = TRUE)) {
  run_example = TRUE
  x = readr::read_csv(csv_file)
  colnames(x)[colnames(x) == "UTC time"] = "time"
  out = estimate_steps_verisense(x, sample_rate = 10L,
                                 method = "original")
  out = estimate_steps_verisense(x, sample_rate = 10L,
                                 method = "revised")
  out = estimate_steps_sdt(x, sample_rate = 10L)
}

if (requireNamespace("readr", quietly = TRUE) &&
    reticulate::py_module_available("forest")) {
    out = estimate_steps_forest(x, sample_rate_analysis = 10L)
  }

Finds walking and calculate steps from raw acceleration data.

Description

Method finds periods of repetitive and continuous oscillations with predominant frequency occurring within know step frequency range. Frequency components are extracted with Continuous Wavelet Transform.

Usage

find_walking(
  data,
  sample_rate_analysis = 10L,
  min_amplitude = 0.3,
  step_frequency = c(1.4, 2.3),
  alpha = 0.6,
  beta = 2.5,
  min_duration_peak = 3L,
  delta = 20L,
  verbose = TRUE
)

Arguments

data

A data.frame with a column for time in POSIXct (usually HEADER_TIMESTAMP), and X, Y, Z

sample_rate_analysis

sampling frequency (in Hz) for analyzing walking. Note, this is NOT the sampling frequency of the data.

min_amplitude

minimum amplitude (in g)

step_frequency

step frequency range

alpha

maximum ratio between dominant peak below and within step frequency range

beta

maximum ratio between dominant peak above and within step frequency range

min_duration_peak

minimum duration of peaks (in seconds)

delta

maximum difference between consecutive peaks (in multiplication of 0.05Hz)

verbose

print diagnostic messages

Value

A vector of number of steps per second

Examples


  csv_file = system.file("test_data_bout.csv", package = "walking")
  if (requireNamespace("readr", quietly = TRUE) && reticulate::py_module_available("forest")) {
    x = readr::read_csv(csv_file)
    colnames(x)[colnames(x) == "UTC time"] = "time"
    res = find_walking(data = x)
  }

Preprocesses accelerometer bout to a common format.

Description

Resample 3-axial input signal to a predefined sampling rate and compute vector magnitude.

Usage

preprocess_bout(data, sample_rate = 10L)

preprocess_bout_r(data, sample_rate = 10L)

Arguments

data

A data.frame with a column for time in POSIXct (usually HEADER_TIMESTAMP), and X, Y, Z

sample_rate

sampling frequency, coercible to an integer. This is the sampling rate you're sampling the data into.

Value

A list of vm_bout, which is an unnamed list of length 2, containing times and VM (vector magnitude) interpolated. This will be passed into other forest functions. The list also contains vm_data, which transforms vm_bout into a data.frame after creating the required times.

Examples


  csv_file = system.file("test_data_bout.csv", package = "walking")
  if (requireNamespace("readr", quietly = TRUE)) {
    x = readr::read_csv(csv_file)
    colnames(x)[colnames(x) == "UTC time"] = "time"
    if (reticulate::py_module_available("forest")) {
      res = preprocess_bout(data = x)
      res2 = preprocess_bout_r(data = x)
      testthat::expect_equal(res$vm_bout, res2$vm_bout, tolerance = 1e-4)
    }
  }

Require Forest module

Description

Require Forest module

Usage

py_require_forest(python_version = "3.11", ...)

Arguments

python_version

Python version to use, passed to reticulate::py_require()

...

additional arguments passed to reticulate::py_require()

Value

invisible NULL

Examples


files <- list.files()
py_require_forest()
walking:::cleanup_uv_lock_files()
stopifnot(length(setdiff(list.files(), files)) == 0L)

Estimate Steps via SDT

Description

Estimate Steps via SDT

Usage

sdt_count_steps(
  data,
  sample_rate,
  order = 4L,
  high = 0.25,
  low = 2.5,
  location = c("wrist", "waist"),
  verbose = TRUE
)

Arguments

data

A data.frame with a column for time in POSIXct (usually HEADER_TIMESTAMP), and X, Y, Z

sample_rate

sampling frequency (in Hz)

order

order of bandpass Butterworth filter order

high

highpass threshold for filter

low

lowpass threshold for filter

location

location of the device, which indicates a different threshold for peaks

verbose

print diagnostic messages

Value

A data.frame of timeand steps

Examples

csv_file = system.file("test_data_bout.csv", package = "walking")
if (requireNamespace("readr", quietly = TRUE)) {
  x = readr::read_csv(csv_file)
  colnames(x)[colnames(x) == "UTC time"] = "time"
  res = sdt_count_steps(data = x, sample_rate = 100L)
}

Count Steps According to Gu et al, 2017 Method

Description

This method is based off finding peaks in the summed and squared acceleration signal and then using multiple thresholds to determine if each peak is a step or an artifact. An additional magnitude threshold was added to the algorithm to prevent false positives in free living data.

Usage

verisense_count_steps(
  data,
  sample_rate,
  k = 3,
  periodicity_range = c(5, 15),
  similarity_threshold = -0.5,
  continuity_window_size = 4,
  continuity_threshold = 4,
  variance_threshold = 0.001,
  vm_threshold = 1.2,
  peak_finder = c("fast", "original"),
  verbose = TRUE,
  global_vm_threshold = 0.025
)

verisense_count_steps_revised(
  ...,
  k = 4,
  periodicity_range = c(4, 20),
  similarity_threshold = -1,
  continuity_window_size = 4,
  continuity_threshold = 4,
  variance_threshold = 0.01,
  vm_threshold = 1.25
)

Arguments

data

A data.frame with a column for time in POSIXct (usually HEADER_TIMESTAMP, not required), and X, Y, Z

sample_rate

sampling frequency of the input data

k

window size for controlling peak finding.

periodicity_range

a length-2 vector of the range of periodicity. These are integers that represent samples not seconds.

similarity_threshold

threshold (in g) for similarity between magnitude of peaks

continuity_window_size

Window size for continuity

continuity_threshold

Threshold for continuity

variance_threshold

Variance threshold for the signal

vm_threshold

vector magnitude threshold for a peak to be called a peak

peak_finder

function to find peaks, either the "original" from the code, or the optimized "fast" version.

verbose

print diagnostic messages

global_vm_threshold

Global acceleration VM threshold (in standard deviation) for the total vector. If sd(acc) < thresh no steps are estimated. Set to 0 to run estimation regardless.

...

not used, used to passes arguments from verisense_count_steps_revised to verisense_count_steps

Value

A vector of length round(nrow(input_data) / sample_rate) of the estimated steps, where the data is rounded to seconds

Note

the ⁠_revised⁠ version is the same algorithm with different defaults for the parameters as based on https://doi.org/10.3390/s22249984.

Author(s)

Examples

csv_file = system.file("test_data_bout.csv", package = "walking")
if (requireNamespace("readr", quietly = TRUE)) {
  x = readr::read_csv(csv_file)
  colnames(x)[colnames(x) == "UTC time"] = "time"
  out = verisense_count_steps(x, sample_rate = 10L)
}
input_data <- matrix(runif(500 * 3, min = -1.5, max = 1.5), ncol = 3)
verisense_count_steps(input_data, sample_rate = 15L)
verisense_count_steps(input_data, sample_rate = 15L, peak_finder = "fast")
acc = sqrt(rowSums(input_data^2))
verisense_count_steps(acc, sample_rate = 15L, peak_finder = "fast")

Package {walking}

Estimate Steps with Unified Syntax

Description

Usage

Arguments

Value

Examples

Finds walking and calculate steps from raw acceleration data.

Description

Usage

Arguments

Value

Examples

Preprocesses accelerometer bout to a common format.

Description

Usage

Arguments

Value

Examples

Require Forest module

Description

Usage

Arguments

Value

Examples

Estimate Steps via SDT

Description

Usage

Arguments

Value

Examples

Count Steps According to Gu et al, 2017 Method

Description

Usage

Arguments

Value

Note

Author(s)

Examples