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")
install.packages("pressR", repos = c('https://cttir.r-universe.dev', 'https://cloud.r-project.org'))

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")

License

MIT

Citation

If you use this software, please cite it as:

Heller, R. (2026). pressR: Pressure distribution analysis for capacitive sensor systems (Version 0.1.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.21889954

DOI: 10.5281/zenodo.21889954

Use of LLM tools

Where applicable, LLM tools were used for narrowly defined, non-authorial tasks: copyediting, prose refinement, Markdown/LaTeX formatting, boilerplate scaffolding (such as CI configurations and build scripts), and code refactoring. Tools included Chat AI, the KISSKI (GWDG) service, and self-hosted Mistral Small (24B, Apache-2.0) served through a local vLLM architecture on an Ubuntu Linux workstation with Intel processors and NVIDIA GPUs. The self-hosted model ran locally, with no data sent to third parties.

All scientific claims, methodological choices, analyses, interpretations and conclusions remain the authors' own. Where applicable, LLM-generated material must be reviewed and revised before inclusion, and references must be verified against their DOI, arXiv ID or ISBN.

Local software setup: vLLM with Python/PyTorch, plus R and RStudio.

Branding

Editable R artwork and regeneration instructions are in tools/branding.