Literature DB >> 32927349

Respiration pattern recognition by wearable mask device.

Vishal Varun Tipparaju1, Di Wang1, Jingjing Yu1, Fang Chen1, Francis Tsow1, Erica Forzani1, Nongjian Tao1, Xiaojun Xian2.   

Abstract

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.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Mask; Principle component analysis; Respiration pattern; Respiratory rate; Steady state; Wearable device

Mesh:

Year:  2020        PMID: 32927349      PMCID: PMC7572779          DOI: 10.1016/j.bios.2020.112590

Source DB:  PubMed          Journal:  Biosens Bioelectron        ISSN: 0956-5663            Impact factor:   10.618


  22 in total

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