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Related Experiment Video

Updated: Feb 24, 2026

Sensitivity Enhancement of Soft Capacitive Pressure Sensors Using a Solvent Evaporation-Based Porosity Control Technique

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Published on: March 24, 2023

A Compressed Sensing Based Method for Reducing the Sampling Time of A High Resolution Pressure Sensor Array System.

Chenglu Sun¹, Wei Li², Wei Chen^3,4

¹Center for Intelligent Medical Electronics, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai 200433, China. 16110720101@fudan.edu.cn.

Sensors (Basel, Switzerland)

|August 11, 2017

Summary

This summary is machine-generated.

A novel compressed sensing (CS) method significantly reduces smart mat sampling time by over 40%. This technique accurately reconstructs respiratory waveforms and vital signs, enabling faster, unobtrusive physiological monitoring.

Keywords:

compressed sensing noninvasive monitoring pressure distribution imaging pressure sensor array respiratory rate monitoring

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Area of Science:

Biomedical Engineering
Sensor Technology
Signal Processing

Background:

Smart mats with flexible pressure sensor arrays offer unobtrusive physiological monitoring.
High-resolution pressure distribution and accurate respiratory waveform acquisition require extensive sampling time.

Purpose of the Study:

To develop a compressed sensing (CS) based method to reduce sampling time for smart mats.
To maintain high resolution and accuracy in physiological data extraction.

Main Methods:

Proposed a novel method utilizing compressed sensing (CS) theory.
Implemented a smart mat with a flexible pressure sensor array and a seven-layer structure.
Acquired approximately one-third of the original sampling points.

Main Results:

Reduced sampling time by 40%.
Achieved a correlation coefficient of 0.9078 between reconstructed and original respiratory waveforms.
Reached 95.54% accuracy for respiratory rate (RR) extraction.

Conclusions:

The CS-based method is a viable option for reducing smart mat sampling time.
This approach enables high-resolution pressure distribution and accurate respiratory monitoring.
The method supports unobtrusive and comfortable physiological data acquisition.