r-nonwear-methods

R code for classifying nonwear accelerometry data with Hidden Markov Models and Gaussian Mixture Models

Files

• manuscript.R - Follows the calculations from the manuscript (see citation) and produces the plot of the X, Y, and Z axis centroids, activity state, and log of the determinant of the covariance matrix for one subject. Requires the accelerometR library (instructions below).

• performanceResultsBoxPlots.R - Generates box plots from nonwear methods performance results which are saved in results/performanceResults.csv. The plots can be saved to disk (.tiff) by editing the R code and changing the top level assignment of saveAsTiffs from saveAsTiffs = FALSE to saveAsTiffs = TRUE.

• automateHMM.R - HMM training/testing of 15 subjects with a 50/50 partition. Outputs performance table summary and saves results to "results/hmmPerformance.csv" when saveResultsToCsv = TRUE.

• gmmPartitioned.R - GMM for 15 subjects with a 60/40 training/testing partition. Outputs table summaries. Plots can be enabled by uncommenting these entries in the source code.

• gmmBalanced.R - GMM with a balanced approach using 80% for training and 20% for testing.

• hmmPartitioned.R - HMM for subject "100521" with a 60/40 training/testing partition. Outputs table summaries. Plots can be enabled by uncommenting these entries in the source code.

• hmmMulti.R - HMM with multiple simulations using a hold-one out training and testing scheme.

• hmmMultiNewFeat.R - HMM with multiple simulations using a hold-one out training and testing scheme with features described in the manuscript.

Requirements and installation

accelerometR library

The accelerometR library is required by the R scripts in this repository. The library included in the accelerometR folder but must be built first before it can used with the other R scripts. The terms library and package are used interchangeably here.

Dependencies

The accelerometR library has the following dependencies:

• mhsmm
• randomForest
• resahpe2

These can be installed from the CRAN repository using R-Studio, for example.

Building the accelerometR library

1. Open a command prompt or terminal. One is available in R Studio.
2. Navigate to your repository. For example: cd ~/git/r-non-wear-methods
3. Run R's build command: r CMD build accelerometR

You should see the following, if done successfully:

* checking for file ‘accelerometR/DESCRIPTION’ ... OK
* preparing ‘accelerometR’:
* checking DESCRIPTION meta-information ... OK
* checking for LF line-endings in source and make files and shell scripts
* checking for empty or unneeded directories
* building ‘accelerometR_0.0.0.9000.tar.gz’

If there are dependency ERRORs, then install the missing dependencies and try again.

Installing the accelerometR package

Once the accelerometR package has been built, you may install it from the same command prompt or terminal using R's install command:

r CMD INSTALL accelerometR_0.0.0.9000.tar.gz

You should see somemthing similar to the following, if done successfully:

* installing to library ‘/Library/Frameworks/R.framework/Versions/4.1/Resources/library’
* installing *source* package ‘accelerometR’ ...
** using staged installation
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading
** help
*** installing help indices
** building package indices
** installing vignettes
** testing if installed package can be loaded from temporary location
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (accelerometR)