A Fast-Response Breathing Monitoring System for Human Respiration Disease Detection

This paper presents a sensing system for the real-time monitoring of human respiration. The system is equipped with a fast response thermoresistive micro calorimetric flow (TMCF) sensor and a dedicated data processing algorithm. The TMCF sensor is designed with a proposed nonlinear sensor model and...

Full description

Saved in:
Bibliographic Details
Published in:IEEE sensors journal 2022-06, Vol.22 (11), p.10411-10419
Main Authors: Wang, Xiaoyi, Ke, Zongqin, Liao, Guanglan, Pan, Xiaofang, Yang, Yatao, Xu, Wei
Format: Article
Language:English
Subjects:
Algorithms
Breathing diseases diagnosis
Data processing
microthermal flow sensor
minute ventilation
Monitoring
Monitoring systems
Optical sensors
Respiration
respiration monitoring
Respiratory diseases
Respiratory rate
Response time
Sensor systems
Sensors
Telemedicine
Temperature sensors
Time constant
Time factors
Wearable sensors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a sensing system for the real-time monitoring of human respiration. The system is equipped with a fast response thermoresistive micro calorimetric flow (TMCF) sensor and a dedicated data processing algorithm. The TMCF sensor is designed with a proposed nonlinear sensor model and fabricated in a CMOS compatible process, which obtains a high sensitivity of 114 mV/SLM and a fast response time of less than 6 ms. By using this high-performance micro flow sensor and its proprietary data processing algorithm, critical human respiration information including respiratory rate (RR) and minute ventilation (MV) can be easily obtained. The proposed sensing system achieves a very small mean absolute error (MAE) of less than 2.7 mHz for RR, and the extracted MV is also in good agreement with the commonly reported value of 4 - 6 L/min. In addition, benefiting from the very short time constant of the developed TMCF sensor, the proposed sensing system can successfully distinguish different respiratory diseases, such as apnea, hypopnea, polypnea, etc. Therefore, this proposed human respiration monitoring system will be a promising sensing technology for respiration diagnosis in medical applications.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3167023