Predict either ground reaction force or loading rate, or both, based on accelerometer data.
Arguments
- data
An
impactr_dataobject, as obtained with read_acc().- outcome
A character string. Can be either "grf" (for ground reaction force), or "lr" (for loading rate) or "all" (for both mechanical loading variables).
- vector
A character string indicating in which acceleration vector to find the peaks. Can be "resultant", "vertical" or "all".
- model
A character string indicating which model to use to make the predictions. The values currently supported are "walking", "walking/running" and "jumping".
Value
An object of class impactr_peaks with the ground reaction
force and/or loading rate peaks magnitude stored in the columns.
Examples
data <- read_acc(impactr_example("hip-raw.csv"))
data <- specify_parameters(data, acc_placement = "hip", subj_body_mass = 78)
data <- find_peaks(data, vector = "vertical")
predict_loading(
data,
outcome = "grf",
vector = "vertical",
model = "walking/running"
)
#> # Start time: 2021-04-06 15:43:00
#> # Sampling frequency: 100Hz
#> # Accelerometer placement: Hip
#> # Subject body mass: 78kg
#> # Filter: No filter applied
#> # Data dimensions: 251 × 3
#> timestamp vertical_peak_acc vertical_peak_grf
#> <dttm> <dbl> <dbl>
#> 1 2021-04-06 15:43:00 1.83 1485.
#> 2 2021-04-06 15:43:03 1.41 1449.
#> 3 2021-04-06 15:43:04 1.59 1464.
#> 4 2021-04-06 15:43:06 1.35 1443.
#> 5 2021-04-06 15:43:09 2.61 1554.
#> 6 2021-04-06 15:43:11 1.38 1446.
#> 7 2021-04-06 15:43:14 1.42 1450.
#> 8 2021-04-06 15:43:16 1.36 1445.
#> 9 2021-04-06 15:43:16 1.46 1454.
#> 10 2021-04-06 15:43:17 1.32 1441.
#> # … with 241 more rows
#> # ℹ Use `print(n = ...)` to see more rows