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A Portable Self-Powered Turbine Spirometer for Rehabilitation Monitoring on COVID-19

  • Minghao Liu
  • , Puchuan Tan
  • , Jiangtao Xue
  • , Zhuo Liu
  • , Han Ouyang
  • , Dongjie Jiang
  • , Yuan Xi
  • , Wei Sun
  • , Yiqian Wang
  • , Yansong Gai
  • , Bojing Shi
  • , Yang Zou*
  • , Zhou Li*
  • *Corresponding author for this work
  • Guangxi University
  • Chinese Academy of Sciences
  • University of Chinese Academy of Sciences
  • Beijing Institute of Technology
  • Beihang University

Research output: Contribution to journalArticlepeer-review

Abstract

Coronavirus disease 2019 (COVID-19) patients may experience persistent impairment of the lungs after recovery and discharge, which can cause a decline in pulmonary function. Therefore, regular pulmonary function tests are essential for COVID-19 recovered patients, and portable, home-based pulmonary function test devices are of great significance during the pandemic. Herein, a portable self-powered turbine spirometer (PSTS) is designed for respiratory flow measurement and assessment of pulmonary function with high accuracy, humidity resistance, good durability, and low cost. The respiratory airflow can directly drive PSTS to produce a sinusoidal signal with a signal-to-noise of 40.64 dB. By utilizing the long short-term memory (LSTM) model, the flow is successfully predicted, and the “lag-before-start” and “spin-after-stop” defects of the turbine spirometer are eliminated effectively. For pulmonary function tests, the flow-volume loop curve can be obtained from PSTS, and pulmonary function parameters such as inspiratory capacity (IC), forced vital capacity (FVC) and forced expiratory volume in the first 1 s (FEV1) can be calculated. The accuracy of IC is over 95%, and others can reach over 97%. A portable smart pulmonary function assessment system is further developed and used to test the pulmonary function of COVID-19 patients one month after symptom onset, demonstrating potential for assessing rehabilitation trends and long-term follow-up of COVID-19 recovered patients.

Original languageEnglish
Article number2300328
JournalAdvanced Materials Technologies
Volume8
Issue number15
DOIs
StatePublished - 11 Aug 2023

Keywords

  • COVID-19
  • deep learning
  • pulmonary function tests
  • triboelectric nanogenerators
  • turbine spirometer

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