Rapid Screening of Physiological Changes Associated With COVID-19 Using Soft-Wearables and Structured Activities: A...

Area of Science:

• Biomedical Engineering
• Wearable Technology
• Infectious Disease Detection

Background:

• Controlling COVID-19 spread necessitates scalable testing infrastructure.
• Current wearable-based detection requires continuous, long-term physiological data collection, limiting rapid screening.
• A novel, short-duration physiological data acquisition method is needed for efficient COVID-19 identification.

Purpose of the Study:

• To propose and evaluate a novel
• snapshot
• paradigm using a soft wearable sensor for COVID-19 detection.
• To assess the feasibility of using short-term physiological responses to identify COVID-19 symptoms.

Main Methods:

• Developed a body-conforming soft wearable sensor placed on the suprasternal notch.
• Recorded physiological data (activity, cardio-respiratory function, cough sounds) during a ~2-minute activity sequence.
• Utilized logistic regression classifiers trained on combined physiological features to discriminate between COVID-positive and healthy individuals.

Main Results:

• Pilot study included 14 COVID-19 positive and 14 healthy individuals.
• Detected altered heart rate, respiration rate, and heart rate variability in COVID-19 positive participants.
• Achieved an Area Under the Curve (AUC) of 0.94 for discriminating COVID-positive individuals using combined features.

Conclusions:

• The sensor-based
• snapshot
• paradigm shows potential for non-invasive COVID-19 testing.
• This approach may serve as a repeatable method to alert individuals requiring further screening.
• Preliminary results suggest a promising direction for scalable and efficient infectious disease detection.