A Temperature-Pressure Dual-Mode Flexible Patch Sensor with Temperature Compensation and Pressure Decoupling Functions for Pulse Detection and Recognition

解耦(概率) 材料科学 压力传感器 补偿(心理学) 声学 脉搏(音乐) 光电子学 电子工程 计算机科学 压力测量 控制理论(社会学)
作者
Yuechao Ma,Wenjie Zhao,Ruitian Yan,Xiaoyang Duan,Haonan Cheng,Jialin Zhang,Hongquan Guo,Yang Xiao,Dongmeng Su,Yinsheng Chen,Genqi Xu,Minghua Chen
出处
期刊:ACS Sensors [American Chemical Society]
卷期号:11 (4): 3166-3178
标识
DOI:10.1021/acssensors.5c04432
摘要

Accurate and continuous detection of human pulse waves is critical for long-term health monitoring and physiological assessment during physical activity. However, most conventional flexible pulse sensors are single-mode, making them susceptible to interference from both ambient and physiological temperature fluctuations. Developing integrated dual-mode sensors remains challenging due to the difficulty in effectively decoupling pressure and temperature signals. Herein, we report a dual-mode flexible patch sensor featuring a unique “compass-like” structural design. The sensor demonstrates a broad pressure detection range (50 Pa−200 kPa) and rapid response/recovery times (60/96 ms), while its integrated temperature-sensing unit achieves a high resolution of 0.1 °C. Through unique structural design, the sensor achieves robust pressure decoupling of the temperature signal, with resistance fluctuations of only 0.102% under pressures up to 30 kPa. This independent temperature signal is subsequently utilized as a reference for the real-time temperature compensation of the pressure signal. The compensated pressure signal exhibits minimal temperature drift (41 × 10 −6 °C −1 across 5−45 °C), enabling stable, interference-resistant monitoring of radial artery pulse waves under dynamic thermal conditions. Furthermore, when integrated with a ResNet-based deep learning model, the sensor successfully classifies diverse pulse signals with an accuracy of 97.6%. This research provides an effective solution for interference-resistant wearable electronics, offering significant potential for intelligent disease diagnosis and sports health management.
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