R/stride_template.R

In neuroconductor-devel/adept: Adaptive Empirical Pattern Transformation

#' @title Walking Stride Pattern Templates
#'
#' @description Walking stride pattern templates derived from accelerometry
#' data collected at four body locations: left wrist, left hip, left ankle,
#' and right ankle.
#'
#' @format A \code{list} with four named elements:
#' \itemize{
#'   \item \code{left_wrist},
#'   \item \code{left_hip},
#'   \item \code{left_ankle},
#'   \item \code{right_ankle}.
#' }
#' Each of the above is a five-element \code{list} of \code{matrix} objects.
#' The matrices are collection of (sub)population-specific stride pattern templates.
#' For example,
#' \itemize{
#'   \item \code{stride_template$left_wrist[[1]]} is a \code{1 x 200} \code{matrix}
#'   of one population-specific stride template derived from accelerometry data
#'   collected at left wrist.
#'   \item \code{stride_template$left_wrist[[2]]} is a \code{2 x 200} \code{matrix}
#'   of two distinct subpopulation-specific stride templates derived from accelerometry data
#'   collected at left wrist. Each row is a one subpopulation-specific stride template.
#'   \item \code{stride_template$right_ankle[[5]]} is a \code{5 x 200} \code{matrix}
#'   of five distinct subpopulation-specific stride templates derived from accelerometry data
#'   collected at right ankle.
#' }
#'
#' @details
#' Raw accelerometry data used to derive walking stride pattern templates
#' were collected as a part of the study on Identification of Walking,
#' Stair Climbing, and Driving Using Wearable Accelerometers, sponsored by the Indiana
#' University CTSI grant and conducted at the Department of Biostatistics,
#' Fairbanks School of Public Health at Indiana University. The study was led by Dr.
#' Jaroslaw Harezlak, assisted by Drs. William Fadel and Jacek Urbanek.
#' It enrolled 32 healthy participants between 23 and 52 years of age.
#' Participants were asked, among others, to perform self-paced, undisturbed,
#' outdoor walking on the sidewalk. Accelerometry data were collected
#' at four body locations: left wrist, left hip, left ankle,
#' and right ankle.
#'
#' To derive empirical stride pattern, firstly, from each body location,
#' 642 data segments corresponding to individual walking strides
#' were manually segmented. Secondly, Vector Magnitude (VM), which is a univariate summary
#' of three-dimensional time-series of raw accelerometry data, was computed.
#' Lastly, 642 univariate vectors of VM were interpolated to have the same vector length,
#' scaled, and clustered into 1-5 clusters via correlation clustering.
#' The respective
#' cluster means
#' were defined to be 1-5 element subpopulation-specific stride templates, respectively.
#'
#'
"stride_template"