************
Introduction
************
This notebook provides the code designed to build and evaluate the random forest models presented in the manuscript "Machine learning analysis of wearable sensor data from mobility testing distinguishes Parkinson's disease from other forms of parkinsonism". More details about the signal processing, feature enginnering, and machine learning models are provided under the 'Methods' section of the paper. The code has been tested using R version 4.1.3 (2022-03-10).

************
Setup
************
To view the notebook, open the 'index.html' file in a web browser or open the 'notebook.pdf' file in a PDF viewer application. By following the code in the notebook, the results presented in the paper can be replicated. Also, by loading the R objects under the 'rdata' folder of the supplementary material, the same performance measures and misclassification analysis can be reproduced. Instructions about how to load an R dataset are provided at (https://bookdown.org/rwnahhas/IntroToR/loading-an-r-dataset.html).

************
Dependencies
************
For each section, the required R packages are listed in the first code chunk. R packages can be installed using the instructions listed in (https://www.dataquest.io/blog/install-package-r/).

************
Usage
************
The code reads the sensor data from the 'data' folder which contains a subfolder for each participant. Each folder has seven text files (for the 32-feet walk, standing with eyes closed, standing with eyes opened, two trials of TUG, and two trials of CogTUG tasks) and five subfolders to save the segmented components of the complex tasks.