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

Objective dyspnea evaluation on COVID-19 patients learning from exertion-induced dyspnea scores