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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:
You can also install the development version from GitHub with:
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 rowsThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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