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").
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")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.
#> # … with 16 more rows
#> # ℹ Use `print(n = ...)` to see more rows