Flexible Piezoresistive Yarn Sensor for Human Physiological Signal Measurement

TL;DR

This paper presents a flexible piezoresistive yarn sensor designed for accurate, stable, and comfortable physiological signal monitoring, demonstrating high stability and potential for healthcare and sports applications.

Contribution

The study introduces a novel FPY sensor with optimized bonding patterns and dual measurement modes, achieving high sensitivity and stability for physiological signals.

Findings

High similarity in morphological characteristics of signals compared to related studies

Demonstrated high measurement stability with low baseline drift

Achieved a mean absolute error of 0.0051 in ABP waveform baseline measurement

Abstract

Continuous monitoring of physiological signals is essential for the early detection of health problems. A measurement system that ensures high sensitivity, accuracy, and user comfort is needed. In this study, we designed and optimized a flexible piezoresistive yarn (FPY) sensor to achieve a high sensitivity and wide working range for detecting physiological signals. The representative sensor design was constructed by applying an FPY bonding pattern, utilizing tightly arranged triangular patterns and using minimal FPY. The prototype sensor operates in two measurement modes, strain and pressure, and was evaluated for measuring neck motion, finger bending, respiratory signals, and arterial blood pressure (ABP) waveforms. A qualitative evaluation, performed by comparing the characteristics of the measurement results of each physiological signal with those from related studies, indicates a high similarity in its morphological characteristics. Then, a quantitative evaluation through baseline drift analysis demonstrates that the FPY sensor displays high measurement stability. The ABP waveform measurement shows the most stable baseline, with a mean absolute error (MAE) of $0.0051 \pm 0.0029$ in terms of baseline drift, using normalized values from 0 to 1. Based on our results, the prototype sensor can be used as an innovative solution for physiological signal monitoring and can be further enhanced for personalized healthcare and sports applications.