TY - JOUR
T1 - Respiration pattern recognition by wearable mask device
AU - Tipparaju, Vishal Varun
AU - Wang, Di
AU - Yu, Jingjing
AU - Chen, Fang
AU - Tsow, Francis
AU - Forzani, Erica
AU - Tao, Nongjian
AU - Xian, Xiaojun
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - Compared to heart rate, body temperature and blood pressure, respiratory rate is the vital sign that has been often overlooked, largely due to the lack of easily accessible tool for reliable and natural respiration monitoring. To address this unmet need, we designed and built a wearable, stand-alone, fully integrated mask device for accurate tracking of respiration in free-living conditions. The wearable mask device can provide comprehensive respiration information in a wearable and wireless manner. It can not only accurately measure respiratory rate, tidal volume, respiratory minute volume, and peak flow rate but also recognize unique respiration pattern of the subject via Principle Component Analysis (PCA) algorithms. The reported wearable mask device and respiratory pattern recognition algorithms could be widely used in routine clinical examination, lung function assessment, asthma and chronic obstructive pulmonary disease (COPD) management, metabolic rate measurement, capnography, spirometry, sleep pattern analysis, and biometrics.
AB - Compared to heart rate, body temperature and blood pressure, respiratory rate is the vital sign that has been often overlooked, largely due to the lack of easily accessible tool for reliable and natural respiration monitoring. To address this unmet need, we designed and built a wearable, stand-alone, fully integrated mask device for accurate tracking of respiration in free-living conditions. The wearable mask device can provide comprehensive respiration information in a wearable and wireless manner. It can not only accurately measure respiratory rate, tidal volume, respiratory minute volume, and peak flow rate but also recognize unique respiration pattern of the subject via Principle Component Analysis (PCA) algorithms. The reported wearable mask device and respiratory pattern recognition algorithms could be widely used in routine clinical examination, lung function assessment, asthma and chronic obstructive pulmonary disease (COPD) management, metabolic rate measurement, capnography, spirometry, sleep pattern analysis, and biometrics.
KW - Mask
KW - Principle component analysis
KW - Respiration pattern
KW - Respiratory rate
KW - Steady state
KW - Wearable device
UR - http://www.scopus.com/inward/record.url?scp=85090589366&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090589366&partnerID=8YFLogxK
U2 - 10.1016/j.bios.2020.112590
DO - 10.1016/j.bios.2020.112590
M3 - Article
C2 - 32927349
AN - SCOPUS:85090589366
SN - 0956-5663
VL - 169
JO - Biosensors and Bioelectronics
JF - Biosensors and Bioelectronics
M1 - 112590
ER -