集成柔性撑鼻器的双通路可穿戴MEMS呼吸监测微系统

赵文浒; 王嘉祥; 胡致远; 苏开明; 李铁; 杨卓青

doi:10.12263/DZXB.20240975

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集成柔性撑鼻器的双通路可穿戴MEMS呼吸监测微系统

学术论文 | 更新时间：2025-12-08

- 集成柔性撑鼻器的双通路可穿戴MEMS呼吸监测微系统
- Dual-Channel Wearable MEMS Respiratory Monitoring Microsystem Integrated with Flexible Nasal Support Device
- 电子学报 2025年53卷第3期页码：790-799
- 作者机构：
  
  1.上海交通大学微米纳米加工技术全国重点实验室，上海 200240
  2.上海交通大学集成电路学院，上海 200240
  3.上海交通大学医学院附属第六人民医院耳鼻咽喉头颈外科，上海 200233
  4.中国科学院上海微系统与信息技术研究所，上海 200050
- 作者简介：
  
  [ "赵文浒男，现为上海交通大学集成电路学院硕士研究生.主要研究方向为MEMS呼吸传感器.E-mail: zhaowenxu@sjtu.edu.cn" ]
  [ "王嘉祥男，现为上海交通大学集成电路学院博士研究生.主要研究方向为3D-MEMS器件.E-mail: wangjiaxiang@sjtu.edu.cn" ]
  [ "胡致远男，现为海交通大学电子信息与电气工程学院微纳电子学系博士研究生.主要研究方向为 MEMS无线传感器.E-mail: hzy_sjtu@sjtu.edu.cn" ]
  [ "苏开明男，现为上海交通大学附属第六人民医院耳鼻咽喉头颈外科主任医师、博士生导师，同时担任鼻科教研组组长.主要研究方向为鼻部疾病与儿童阻塞性睡眠呼吸暂停综合征（OSAS）.E-mail: 021china@sina.com" ]
  [ "李铁男，现为中国科学院上海微系统与信息技术研究所研究员，博士生导师，微系统技术重点实验室副主任.主要研究方向为微纳传感器机理、器件及应用基础.中国电子学会会员编号：E190029803M.E-mail: tli@mail.sim.ac.cn" ]
  [ "杨卓青男，现为上海交通大学集成电路学院教授、博士生导师.主要研究方向为微机电系统（MEMS），MEMS惯性开关器件，超细基底三维（3D）曲面光刻技术，微纳柔性传感技术等." ]
- 基金信息：
  
  国家重点研发计划(2020YFB2008503);国家自然科学基金(61974088)
- DOI：10.12263/DZXB.20240975
  中图分类号： TN98;
- 收稿：2024-10-27，
  
  修回：2024-12-30，
  
  纸质出版：2025-03-25
- 稿件说明：
移动端阅览
赵文浒, 王嘉祥, 胡致远, 等. 集成柔性撑鼻器的双通路可穿戴MEMS呼吸监测微系统[J]. 电子学报, 2025, 53(03): 790-799.

ZHAO Wen-xu, WANG Jia-xiang, HU Zhi-yuan, et al. Dual-Channel Wearable MEMS Respiratory Monitoring Microsystem Integrated with Flexible Nasal Support Device[J]. Acta Electronica Sinica, 2025, 53(03): 790-799.
赵文浒, 王嘉祥, 胡致远, 等. 集成柔性撑鼻器的双通路可穿戴MEMS呼吸监测微系统[J]. 电子学报, 2025, 53(03): 790-799. DOI：10.12263/DZXB.20240975

ZHAO Wen-xu, WANG Jia-xiang, HU Zhi-yuan, et al. Dual-Channel Wearable MEMS Respiratory Monitoring Microsystem Integrated with Flexible Nasal Support Device[J]. Acta Electronica Sinica, 2025, 53(03): 790-799. DOI：10.12263/DZXB.20240975

摘要

呼吸作为维持生命的关键生理过程，与呼吸暂停、哮喘等多种呼吸系统疾病密切相关.为了满足日益增长的健康监测需求，本文创新性地提出了一种集成柔性撑鼻器的双通路可穿戴MEMS（Micro-Electro-Mechanical Systems）呼吸监测微系统.该微系统集成了柔性撑鼻器、呼吸传感器和信号处理模块，能够持续实时监测鼻腔内气流.传感器的敏感单元采用折叠式金属电阻结构，通过平面MEMS技术沉积在玻璃基底上，基于热阻效应，实现信号测量.传感器嵌入柔性撑鼻器后，可同时监测鼻腔内左右两侧的呼吸信号，尤其适合长时间连续监测.通过信号模拟及性能测试，结果表明该传感器在灵敏度、响应速度和抗干扰能力上表现出色，并且在模拟呼吸暂停和哮喘等疾病的测试中，传感器能够准确区分正常与异常的呼吸信号，可用于进一步分析各种呼吸疾病.基于此，本文开发了一种集成柔性撑鼻器的双通路可穿戴MEMS呼吸监测装置，旨在实现连续、实时且长时间的呼吸监测，特别适用于睡眠期间的异常呼吸筛查与健康监控.此外，该系统还能捕捉人体鼻周期的变化信息，为深入分析呼吸模式和生理节律提供了新的数据维度，展现了在长期健康管理中的潜在应用价值.

Abstract

Breathing

as a crucial physiological process for sustaining life

is closely related to various respiratory diseases such as sleep apnea and asthma. To meet the increasing demand for health monitoring

this paper innovatively proposes a dual-channel wearable MEMS (Micro-Electro-Mechanical Systems) respiratory monitoring microsystem integrated with a flexible nasal expander. This microsystem incorporates a flexible nasal expander

a respiratory sensor

and a signal processing module

enabling continuous real-time monitoring of airflow within the nasal cavity. The sensor’s sensitive element adopts a folded metal resistor structure

deposited on a glass substrate through planar MEMS technology

utilizing the thermoresistive effect to achieve signal measurement. When embedded in the flexible nasal expander

the sensor can simultaneously monitor breathing signals from both sides of the nasal cavity

making it especially suitable for long-term continuous monitoring. Signal simulation and performance testing results demonstrate that the sensor exhibits excellent sensitivity

response speed

and anti-interference capability. In tests simulating respiratory conditions such as sleep apnea and asthma

the sensor accurately differentiates between normal and abnormal breathing patterns

supporting further analysis of various respiratory diseases. Based on this

the paper develops a dual-channel wearable MEMS respiratory monitoring device integrated with a flexible nasal expander

aimed at continuous

real-time

and long-term respiratory monitoring

particularly suitable for abnormal breathing screening and health monitoring during sleep. Additionally

this system captures changes in the nasal cycle

providing new data dimensions for in-depth analysis of breathing patterns and physiological rhythms

highlighting its potential application value in long-term health management.

关键词

Keywords

references

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