stepcount (version 0.3.2)

sc_model_params: Run Stepcount Model on Data

Description

Run Stepcount Model on Data

Usage

sc_model_params(model_type, pytorch_device)
stepcount(
  file,
  sample_rate = NULL,
  model_type = c("ssl", "rf"),
  model_path = NULL,
  pytorch_device = c("cpu", "cuda:0"),
  verbose = TRUE,
  keep_data = FALSE
)
stepcount_with_model(
  file,
  model_type = c("ssl", "rf"),
  model,
  sample_rate = NULL,
  pytorch_device = c("cpu", "cuda:0"),
  verbose = TRUE,
  keep_data = FALSE
)

Value

A list of the results (data.frame), summary of the results, adjusted summary of the results, and information about the data.

Arguments

model_type

type of the model: either random forest (rf) or Self-Supervised Learning model (ssl)

pytorch_device

device to use for prediction for PyTorch.

file

accelerometry file to process, including CSV, CWA, GT3X, and GENEActiv bin files

sample_rate

the sample rate of the data. Set to NULL for stepcount to try to guess this

model_path

the file path to the model. If on disk, this can be re-used and not re-downloaded. If NULL, will download to the temporary directory

verbose

print diagnostic messages

keep_data

should the data used in the prediction be in the output?

model

A model object loaded from sc_load_model, but as_python must be TRUE

Examples

file = system.file("extdata/P30_wrist100.csv.gz", package = "stepcount")
if (stepcount_check()) {
  out = stepcount(file = file)
  st = out$step_times
}
if (FALSE) {
  file = system.file("extdata/P30_wrist100.csv.gz", package = "stepcount")
  df = readr::read_csv(file)
  if (stepcount_check()) {
    out = stepcount(file = df)
    st = out$step_times
  }
  if (requireNamespace("ggplot2", quietly = TRUE) &&
      requireNamespace("tidyr", quietly = TRUE) &&
      requireNamespace("dplyr", quietly = TRUE)) {
    dat = df[10000:12000,] %>%
      dplyr::select(-annotation) %>%
      tidyr::gather(axis, value, -time)
    st = st %>%
      dplyr::mutate(time = lubridate::as_datetime(time)) %>%
      dplyr::as_tibble()
    st = st %>%
      dplyr::filter(time >= min(dat$time) & time <= max(dat$time))
    dat %>%
      ggplot2::ggplot(ggplot2::aes(x = time, y = value, colour = axis)) +
      ggplot2::geom_line() +
      ggplot2::geom_vline(data = st, ggplot2::aes(xintercept = time))
  }

}