ReviewSensors (Basel, Switzerland)2025
A Taxonomy of Pressure Sensors for Compression Garment Development.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
- A Simplified Physical Model for the Sensitivity-Pressure Relationship in Textile-Based Piezoresistive Sensors.Sensors (Basel, Switzerland) · 2026Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent pressure sensor research often focuses on developing sensors for impulse applications, including touch sensors, e-skin development, or physiological monitoring. However, static loading applications, such as those needed for compression garment design, are significantly under-researched in comparison. Many technology solutions do not translate across applications, as static loading requires measurements which have high accuracy, high precision, and low drift. To address the gap in sensor development between impulse and static applications, we define a literature-based taxonomy providing two conceptual classifications based on sensor functionality and specific design characteristics. The taxonomy's utility is demonstrated through the mapping of sensors onto compression garment development phases by matching application requirements with sensor performance. The taxonomy developed will advance research and the industry by providing a roadmap of how sensor characteristics influence performance to drive a focused development for future sensors, specifically for compression garment innovation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.