The COVID-19 pandemic has changed the lives of many people around the world. Based on the available data and published reports, most people diagnosed with COVID-19 exhibit no or mild symptoms and could be discharged home for self-isolation. Considering that a substantial portion of them will progress to a severe disease requiring hospitalization and medical management, including respiratory and circulatory support in the form of supplemental oxygen therapy, mechanical ventilation, vasopressors, etc. The continuous monitoring of patient conditions at home for patients with COVID-19 will allow early determination of disease severity and medical intervention to reduce morbidity and mortality. In addition, this will allow early and safe hospital discharge and free hospital beds for patients who are in need of admission. In this review, we focus on the recent developments in next-generation wearable sensors capable of continuous monitoring of disease symptoms, particularly those associated with COVID-19. These include wearable non/minimally invasive biophysical (temperature, respiratory rate, oxygen saturation, heart rate, and heart rate variability) and biochemical (cytokines, cortisol, and electrolytes) sensors, sensor data analytics, and machine learning-enabled early detection and medical intervention techniques. Together, we aim to inspire the future development of wearable sensors integrated with data analytics, which serve as a foundation for disease diagnostics, health monitoring and predictions, and medical interventions.
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Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19
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September 2023
Review Article|
July 19 2023
Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19

Huijie Li
;
Huijie Li
(Conceptualization, Writing – original draft)
1
Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut
, Storrs, Connecticut 06269, USA
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Jianhe Yuan
;
Jianhe Yuan
(Data curation, Writing – original draft)
2
Department of Electrical Engineering and Computer Science, University of Missouri-Columbia
, Columbia, Missouri 65211, USA
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Gavin Fennell
;
Gavin Fennell
(Data curation, Writing – original draft)
1
Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut
, Storrs, Connecticut 06269, USA
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Vagif Abdulla
;
Vagif Abdulla
(Data curation, Writing – original draft)
1
Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut
, Storrs, Connecticut 06269, USA
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Ravi Nistala
;
Ravi Nistala
(Conceptualization)
3
Division of Nephrology, Department of Medicine, University of Missouri-Columbia
, Columbia, Missouri 65212, USA
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Dima Dandachi;
Dima Dandachi
(Conceptualization)
4
Division of Infectious Diseases, Department of Medicine, University of Missouri-Columbia
, 1 Hospital Drive, Columbia, Missouri 65212, USA
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Dominic K. C. Ho
;
Dominic K. C. Ho
a)
(Conceptualization, Supervision, Writing – review & editing)
2
Department of Electrical Engineering and Computer Science, University of Missouri-Columbia
, Columbia, Missouri 65211, USA
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Biophysics Rev. 4, 031302 (2023)
Article history
Received:
January 30 2023
Accepted:
May 19 2023
Connected Content
A companion article has been published:
Using wearable sensors and data analytics for disease monitoring
Citation
Huijie Li, Jianhe Yuan, Gavin Fennell, Vagif Abdulla, Ravi Nistala, Dima Dandachi, Dominic K. C. Ho, Yi Zhang; Recent advances in wearable sensors and data analytics for continuous monitoring and analysis of biomarkers and symptoms related to COVID-19. Biophysics Rev. 1 September 2023; 4 (3): 031302. https://doi.org/10.1063/5.0140900
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