pressR

pressR parses, analyzes, and visualizes pressure distribution data from capacitive sensor systems. It ships with predefined layouts for in-shoe pressure measurement, saddle pressure mapping (equine and bicycle), seating assessment, and barefoot pedography, along with an interactive Shiny application for data exploration.

Installation

# install.packages("pak")
pak::pak("cttir/pressR")

Quick example

library(pressR)

trial <- pr_example_trial("insole")

pr_plot_heatmap(trial)
pr_plot_force_time(trial, show_cycles = TRUE)

pr_summary(trial)
pr_calc_regional(trial)

Features

• Parsers for ASCII pressure data exports, generic CSV, force sensor data, and region mask files (.msa/.msr/.msp).
• Predefined layouts for in-shoe insoles (99-sensor), barefoot pressure platforms, generic sensor mats (16x16 / 32x32), horse and bicycle saddles, wheelchair / car / office seating, and glove sensors.
• Per-frame and per-trial analysis: peak pressure, mean pressure, force, contact area, pressure-time integral, center of pressure, symmetry index, gait cycle detection, and COP rollover pattern.
• Application-specific analysis: saddle bridge and slip detection, wheelchair hotspot identification, plantar-pressure regional analysis.
• Published reference thresholds for saddle fit (von Peinen 2010, Moenkemoeller 2005, Werner 2002), diabetic foot risk, and wheelchair seating.
• Visualization: 2D and 3D heatmaps, dynamics plots, regional bar charts, composite report panels, and side-by-side trial comparison.
• Shiny app (pr_run_app()) for interactive import, analysis, and export.

Vignettes

• vignette("getting-started", package = "pressR")
• vignette("saddle-pressure-analysis", package = "pressR")
• vignette("foot-pressure-analysis", package = "pressR")

Use of LLM tools

Portions of this package were prepared with assistance from large language model tooling for narrowly defined, non-authorial tasks: copyediting, prose smoothing, Markdown/LaTeX formatting, scaffolding of boilerplate files (CI configs, build scripts), code refactoring. The tools used were Chat AI, the LLM service of KISSKI (GWDG), and a self-hosted Mistral Small (24B, Apache-2.0) run locally via Ollama and the ollamar R package — local inference only, with no data sent to third parties for the self-hosted model.

All scientific claims, methodological choices, analyses, interpretations, and conclusions are the author’s own. No LLM-generated text was incorporated without review and revision, and every reference was verified against its DOI, arXiv ID, or ISBN.

License

MIT