alcantarar / sacral_accel_manuscript

Repository for "Sacrum accelerations can predict whole-body kinetic and stride kinematics across running speeds"

Open Access Publication 👉 https://peerj.com/articles/11199/

Project Contents

• analysis.R - This is probably the file you're looking for. It trains models, produces RMSE and MAPE values, and generates the subplots used in Figure 1.

• GRF_IMU_data.csv - Data used to train/test Quantile Regression Forest and Linear Regression models.

  Column Header	Contents (units)
  Sub	Subject Number
  IsFemale	Boolean indicating Subject Sex (0 = Male, 1 = Female)
  Mass	Body Mass (kg)
  Speed	Treadmill speed (m/s)
  GRFPeak	Mean Peak vGRF calculated from treadmill data (body weight (BW))
  GRFImpulse	Mean Vertical Impulse calculated from treadmill data (BW*s)
  GRFtc	Mean Ground Contact Time calculated from treadmill data (s)
  IMUPeak	Mean Peak vGRF calculated from sacrum acceleration data (BW)
  IMUImpulse	Mean Vertical Impulse calculated from sacrum acceleration data (BW*s)
  IMUtc	Mean Ground Contact Time calculated from sacrum acceleration data (s)
  IMUsf	Step frequency calculated from sacrum acceleration data (Hz)

Reproducibility Instructions

The analysis was performed with R version 3.6.3 (2020-02-29). Reproducibility results may vary with different versions, in particular those < 3.6.0. See this stack overflow post and the documentation for sample.kind and RNGversion here.

To replicate the environment used to perform the analysis, run the first couple of commented out lines in analysis.R to install the version of the packages used to produce the results and figure in the manuscript:

# Install versioned packages used for analysis (if needed): ----
# A few sub-dependencies that don't always cooperate with install_version():
# install.packages('gower')
# install.packages('systemfonts')
# install.packages('gdtools')
# Primary dependencies:
# install.packages(remotes)
# library('remotes')
# install_version('caret', version = '6.0-85', dependencies = TRUE, repos = 'http://cran.us.r-project.org')
# install_version('quantregForest', version = '1.3-7', dependencies = TRUE, repos = 'http://cran.us.r-project.org')
# install_version('tidyr', version = '1.0.2', dependencies = TRUE, repos = 'http://cran.us.r-project.org')
# install_version('ggthemes', version = '4.2.0', dependencies = TRUE, repos = 'http://cran.us.r-project.org')  # may need {gdtools} and/or {systemfonts}
# install_version('ggridges', version = '0.5.2', dependencies = TRUE, repos = 'https://cran.us.r-project.org')

There may be additional dependencies that should be loaded via install.packages(). If prompted to upgrade packages, skip and press enter (this may take a while):

These packages have more recent versions available.
It is recommended to update all of them.
Which would you like to update?

1: ALL
2: CRAN packages only
3: None
.
.
.
Enter one or more numbers, or an empty line to skip updates:

Running the rest of the script from source will produce the reported MAPE/RMSE values and the subplots in Figure 1.

Contributions

• Ryan Alcantara, University of Colorado
• Evan Day, University of Oregon
• Mike Hahn, University of Oregon
• Alena Grabowski, University of Colorado