Respiration monitoring using a paper-based wearable humidity sensor, a step forward to clinical tests

Solak İ., Gençer S., Yıldırım B., Öznur E., HAH D., İÇÖZ K.

Sensors and Actuators A: Physical, vol.355, 2023 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 355
  • Publication Date: 2023
  • Doi Number: 10.1016/j.sna.2023.114316
  • Journal Name: Sensors and Actuators A: Physical
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Biotechnology Research Abstracts, Chemical Abstracts Core, Communication Abstracts, INSPEC, Metadex, Civil Engineering Abstracts
  • Keywords: Chronic obstructive pulmonary disease, Paper-based, Pneumonia, Respiration monitoring, Smoking, Wearable humidity sensing
  • Abdullah Gül University Affiliated: Yes


Monitoring respiratory variables can provide valuable information for clinical applications and sport activities. Paper-based wearable respiration monitoring systems have great advantages and potential, they are low-cost, easily disposable, non-invasive and can provide real-time, reliable data. Despite some examples presented for exhaled breath analysis using paper-based sensors exist, none of them have been validated yet in a study involving many patients. In this work, we present a novel paper-based platform for exhaled breath sensors and validate it on 101 subjects including 41 patients to demonstrate its clinical applicability. By using the paper-based wearable capacitive sensors, we collected respiration data from different groups of people, namely, smokers, non-smokers and patients diagnosed with pneumonia, or chronic obstructive pulmonary disease (COPD). The change in humidity during inhale and exhale was converted to capacitance change and thus an electrical signal was obtained. The electrical signal was transmitted to a nearby computer and capacitance versus time data was post-processed. Four ratio parameters were defined on the recorded data; area, rate, maximum amplitude, and average maximum-minimum difference, all of which were compared between deep breathing and normal breathing. The collected data was statistically analyzed, and the humidity changes were compared among different groups. The results show that the developed sensor and the proposed analysis method can be used to detect the humidity changes in breathing, and to differentiate between smokers and non-smokers, and between non-smokers and patients with pulmonary disease.