Development of an E‑textile Sensor Array for Early Detection of Pressure‑Ulcer Risk Areas in Wheelchair Users

Resumen

Introduction. Pressure ulcers are a frequent complication in long‑term wheelchair users caused by sustained localized pressure and poor load distribution. Electro‑textile (E‑textile) pressure sensors offer a flexible, low‑cost approach to converting seating surfaces into distributed sensing layers for early detection and prevention of pressure ulcers. Objective. To design, implement, and evaluate an electro‑textile sensor matrix capable of mapping contact pressure on a wheelchair cushion and identifying high‑risk zones for clinical monitoring. Methodology. A two‑stage prototyping approach was followed. Stage 1 validated EeonTex conductive patches (9 × 4 cm patches on 25 × 25 cm foam) read via microcontroller analog input. Stage 2 scaled the design to a 15×15 matrix, built from 30 conductive strips (30 × 2 cm) assembled on EVA sheets, forming 225 intersections. Two 74HC4067 multiplexers routed row/column signals to the microcontroller's input pins; acquisition and thresholding were implemented in Arduino C/C++ using sequential scanning and a configurable detection threshold. Hardware measures to reduce spurious continuity included dielectric separation and organized cabling; software averaged readings and applied per‑channel thresholds. Results. The system successfully performed sequential acquisition of 225 cells and displayed detected contact cells in a serial "board" representation. However, signal-to-noise ratio limitations were observed: baseline input values oscillated (≈200–400 units), resulting in high false-positive rates and inconsistent pressure quantification. Under the current hardware configuration, the matrix could detect contact locations qualitatively but could not reliably discriminate graded pressure levels. Conclusions. The E‑textile matrix prototype demonstrates the feasibility of distributed contact detection but does not reliably quantify pressure levels with the current multiplexing and ESP32 touch‑read architecture. To advance quantitative pressure measurement, priority actions include implementing robust software tools for filtering and managing capacitive noise (e.g., digital filtering, adaptive thresholding, and signal‑processing pipelines) and establishing systematic calibration protocols to convert sensor readings into quantitative pressure units. Further improvements may combine software noise mitigation with optimized sensor geometry, alternative sensing materials, and dedicated analog front‑end