  Objective: Dyspnea is one of the most common symptoms for many pulmonary
diseases including COVID-19. Clinical assessment of dyspnea is mainly performed
by subjective self-report, which has limited accuracy and is challenging for
continuous monitoring. The objective of this research study is to determine if
dyspnea progression in COVID patients can be assessed using a non-invasive
wearable sensor and if the findings are comparable to a learning model of
physiologically induced dyspnea on healthy subjects. Methods: Non-invasive
wearable respiratory sensors were employed to retrieve continuous respiratory
characteristics with user comfort and convenience. Overnight (~16h) respiratory
waveforms were collected on 12 COVID-19 patients, and a benchmark on 13 healthy
subjects with exertion-induced dyspnea were also performed for blind
comparison. The learning model was built from the respiratory features with
self report on 32 healthy subjects under exertion and airway blockage. Results:
High similarity between dyspnea on COVID patients and physiologically induced
dyspnea on healthy subjects was established. COVID patients have consistently
high objective dyspnea scores in comparison with normal breathing of healthy
subjects. We also exhibited continuous dyspnea scoring capability for 12-16
hours on patients. Conclusion: This paper validates the viability to use our
objective dyspnea scoring for clinical dyspnea assessment on COVID patients.
Significance: The proposed system can help the identification of dyspneic
exacerbation in conditions such as COVID, leading to early intervention and
possibly improving their outcome. This approach can be potentially applied to
other pulmonary disorders such as asthma, emphysema, and pneumonia.
