find_walking: Finds walking and calculate steps from raw acceleration data.

In walking: Segments Accelerometry Data into Walking Bouts using Open Source Methods

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)
  }

walking documentation built on Aug. 4, 2026, 1:07 a.m.