TY - JOUR
T1 - A Wireless Integrated System for COPD Monitoring Using a High-Sensitivity, Wide-Dynamic Range MEMS Flow Sensor
AU - Cai, Shiqian
AU - Yue, Daishan
AU - Liu, Ziqi
AU - Xie, Huikai
AU - Shen, Yajing
AU - Li, Shihong
AU - Wang, Xiaoyi
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2025
Y1 - 2025
N2 - This study presents a wireless, integrated, and portable monitoring system for chronic obstructive pulmonary disease (COPD), featuring a highly sensitive and wide-dynamic range MEMS-based flow sensor, alongside a high-accuracy respiratory monitoring algorithm. The flow sensor was fabricated using a CMOS-compatible MEMS process, achieving low-power consumption (3.76 mW), high sensitivity (356.32 mV/(m/s)/W), and a wide dynamic range from –16.7 to 16.7 m/s. To meet the requirements of human respiratory monitoring and spirometric evaluation, a bypass channel was incorporated, enabling bi-directional flow measurement with an extended range of ±1000 L/min. An abnormal respiration detection algorithm was developed based on critical respiratory parameters, including respiratory cycle, respiratory rate (RR), minute ventilation volume (MVV), and tidal volume (TV). Additionally, an enhanced interpolation method was integrated to improve the accuracy of spirometry testing, facilitating early-stage COPD screening. Experimental validation (1.13% volume error, average percentage error of –2.74% of all spirometry indicators) demonstrated that the proposed portable system delivers performance comparable to conventional, bulky medical spirometers, highlighting its strong potential for convenient, real-time respiratory monitoring and spirometric assessment in COPD management.
AB - This study presents a wireless, integrated, and portable monitoring system for chronic obstructive pulmonary disease (COPD), featuring a highly sensitive and wide-dynamic range MEMS-based flow sensor, alongside a high-accuracy respiratory monitoring algorithm. The flow sensor was fabricated using a CMOS-compatible MEMS process, achieving low-power consumption (3.76 mW), high sensitivity (356.32 mV/(m/s)/W), and a wide dynamic range from –16.7 to 16.7 m/s. To meet the requirements of human respiratory monitoring and spirometric evaluation, a bypass channel was incorporated, enabling bi-directional flow measurement with an extended range of ±1000 L/min. An abnormal respiration detection algorithm was developed based on critical respiratory parameters, including respiratory cycle, respiratory rate (RR), minute ventilation volume (MVV), and tidal volume (TV). Additionally, an enhanced interpolation method was integrated to improve the accuracy of spirometry testing, facilitating early-stage COPD screening. Experimental validation (1.13% volume error, average percentage error of –2.74% of all spirometry indicators) demonstrated that the proposed portable system delivers performance comparable to conventional, bulky medical spirometers, highlighting its strong potential for convenient, real-time respiratory monitoring and spirometric assessment in COPD management.
KW - chronic obstructive pulmonary disease (COPD)
KW - flow sensor
KW - IoT
KW - MEMS
KW - respiration monitoring
KW - spirometry
UR - https://www.scopus.com/pages/publications/105012761597
U2 - 10.1109/JIOT.2025.3595198
DO - 10.1109/JIOT.2025.3595198
M3 - Article
AN - SCOPUS:105012761597
SN - 2327-4662
VL - 12
SP - 42590
EP - 42598
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 20
ER -