Respiration pattern recognition by wearable mask device

Vishal Varun Tipparaju, Di Wang, Jingjing Yu, Fang Chen, Francis Tsow, Erica Forzani, Nongjian Tao, Xiaojun Xian

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
Article number112590
JournalBiosensors and Bioelectronics
StatePublished - Dec 1 2020


  • Mask
  • Principle component analysis
  • Respiration pattern
  • Respiratory rate
  • Steady state
  • Wearable device

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Biomedical Engineering
  • Electrochemistry


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