create_mask_auto {pressuRe}R Documentation

Automatically mask pressure footprint

Description

Automatically creates mask for footprint data

Usage

create_mask_auto(
  pressure_data,
  masking_scheme,
  foot_side = "auto",
  res_value = c(20000, 20000, 1e+05, 20000),
  plot = TRUE,
  template_mask = NULL
)

Arguments

pressure_data

List. First item is a 3D array covering each timepoint of the measurement. z dimension represents time

masking_scheme

String. "automask_simple", "automask_novel", "pedar_mask1", "pedar_mask2", "pedar_mask3", "template". "simple_automask" applies a simple 3 part mask (hindfoot, midfoot, forefoot) "automask_novel" attempts to apply a 9-part mask (hindfoot, midfoot, mets, hallux, lesser toes), similar to the standard novel automask "pedar_mask1" splits the insole into 4 regions using sensel boundaries: hindfoot, midfoot, forefoot, and toes- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9470545/ "pedar_mask2" splits the insole into 4 regions using percentages: hindfoot, forefoot, hallux, and lesser toes- https://jfootankleres.biomedcentral.com/articles/10.1186/1757-1146-7-18 "pedar_mask3" splits the foot into 9 regions using sensel boundaries: medial hindfoot, lateral hindfoot, medial midfoot, lateral midfoot, MTPJ1, MTPJ2-3, MTPJ4-5, hallux, and lesser toes- https://jfootankleres.biomedcentral.com/articles/10.1186/1757-1146-7-20

foot_side

String. "RIGHT", "LEFT", or "auto". Auto uses auto_detect_side function

res_value

Numeric vector. Adjusting these values can help if the heel, midfoot, toe, and hallux lines aren't correct. Default values are c(10000, 10000, 100000, 10000). These lines are calculated using a least cost function and the parameter essentially adjusts the resistance of the pressure value for that algorithm

plot

Logical. Whether to play the animation

template_mask

Data frame. Mask to be used if "template_mask" is selected as the masking scheme

Value

List. Masks are added to pressure data variable

Examples

emed_data <- system.file("extdata", "emed_test.lst", package = "pressuRe")
pressure_data <- load_emed(emed_data)
pressure_data <- create_mask_auto(pressure_data, "automask_novel",
res_value = c(20000, 20000, 100000, 20000), foot_side = "auto", plot = FALSE)

[Package pressuRe version 0.2.5 Index]