ArticleScientific reports2023
pressuRe: an R package for analyzing and visualizing biomechanical pressure distribution data.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 7 citations in OpenAlex.
- Quantifying differences in high-pressure region mapping between dynamic in-shoe and barefoot plantar pressure in diabetic subjects.Journal of biomechanics · 2026Article
- Effects of First Metatarsophalangeal Osteoarthritis on Plantar Pressures Across Multiple Activities.Journal of foot and ankle research · 2026Article
- Pathways of load transfer in custom accommodative insoles for people with diabetes.Clinical biomechanics (Bristol, Avon) · 2025Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In many biomechanical analyses, the forces acting on a body during dynamic and static activities are often simplified as point loads. However, it is usually more accurate to characterize these forces as distributed loads, varying in magnitude and direction, over a given contact area. Evaluating these pressure distributions while they are applied to different parts of the body can provide effective insights for clinicians and researchers when studying health and disease conditions, for example when investigating the biomechanical factors that may lead to plantar ulceration in diabetic foot disease. At present, most processing and analysis for pressure data is performed using proprietary software, limiting reproducibility, transparency, and consistency across different studies. This paper describes an open-source software package, 'pressuRe', which is built in the freely available R statistical computing environment and is designed to process, analyze, and visualize pressure data collected on a range of different hardware systems in a standardized manner. We demonstrate the use of the package on pressure dataset from patients with diabetic foot disease, comparing pressure variables between those with longer and shorter durations of the disease. The results matched closely with those from commercially available software, and individuals with longer duration of diabetes were found to have higher forefoot pressures than those with shorter duration. By utilizing R's powerful and openly available tools for statistical analysis and user customization, this package may be a useful tool for researchers and clinicians studying plantar pressures and other pressure sensor array based biomechanical measurements. With regular updates intended, this package allows for continued improvement and we welcome feedback and future contributions to extend its scope. In this article, we detail the package's features and functionality.
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