Mechanical Loading Prediction Through Accelerometer Data

Functions to read, process and analyse accelerometer data related to mechanical loading variables. This package is developed and tested for use with raw accelerometer data from triaxial 'ActiGraph' < https://theactigraph.com> accelerometers.

impactr

impactr is a package with functions to read, process and analyse raw accelerometer data related to mechanical loading variables. You can learn more about this package features and how to use it in vignette("impactr").

Installation

To install the latest stable version of impactr from CRAN, run:

install.packages("impactr")

You can also install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("verasls/impactr")

Usage

library(impactr)

read_acc(impactr_example("hip-raw.csv")) |>
 define_region(
    start_time = "2021-04-06 15:45:00",
    end_time = "2021-04-06 15:45:30"
  ) |>
  specify_parameters(
    acc_placement = "hip",
    subj_body_mass = 78
  ) |>
  filter_acc() |>
  use_resultant() |>
  find_peaks(vector = "resultant") |>
  predict_loading(
    outcome = "grf",
    vector = "resultant",
    model = "walking/running"
  )
#> # Start time:              2021-04-06 15:43:00
#> # Sampling frequency:      100Hz
#> # Accelerometer placement: Hip
#> # Subject body mass:       78kg
#> # Filter:                  Butterworth (4th-ord, low-pass, 20Hz)
#> # Data dimensions:         26 × 3
#>    timestamp           resultant_peak_acc resultant_peak_grf
#>    <dttm>                           <dbl>              <dbl>
#>  1 2021-04-06 15:45:00               1.32              1466.
#>  2 2021-04-06 15:45:01               1.36              1469.
#>  3 2021-04-06 15:45:04               1.30              1464.
#>  4 2021-04-06 15:45:04               2.32              1543.
#>  5 2021-04-06 15:45:05               1.50              1480.
#>  6 2021-04-06 15:45:06               1.68              1494.
#>  7 2021-04-06 15:45:06               1.51              1480.
#>  8 2021-04-06 15:45:07               1.96              1515.
#>  9 2021-04-06 15:45:08               1.37              1470.
#> 10 2021-04-06 15:45:08               1.86              1508.
#> # ℹ 16 more rows