Wearable monitoring of Covid-19 biomarkers helps to observe its spread

T reported coughing, talking and laughing, with automated wireless wearable sensors and a machine learning approach to record and interpret mechanical acoustic signatures of a person’s heart rate, respiratory rate and body temperature.These all monitor the progress of COVID-19 (new coronavirus infection) Infection of individuals and infections of the entire population.

Results from 37 Proceedings of the National Academy of Sciences (PNAS) researchers diagnosed with COVID-19 reveal patterns of evolution of these parameters at all stages of the disease, from initial diagnosis to hospital treatment to final I made it. Recovery at home.

Systematic imaging studies have shown a correlation between the time and intensity of coughing, talking, and laughing and total droplet formation as a rough indicator of the likelihood of disease spread. Sensors deployed in COVID-19 patients continuously record cough frequency and intensity, along with other biometric collections, along with healthy control in both inpatient and home environments.

The results show a declining trend in cough frequency and intensity throughout the course of disease recovery, but there is significant variability among patient populations. This methodology creates an opportunity to study biometric patterns between individuals and between different demographic groups.

Because COVID-19 is a respiratory disease, coughing and other sounds from the thoracic cavity, trachea, and esophagus are examples of relevant biometrics. Laboratory-scale studies have shown cough-based diagnoses of various respiratory diseases through measurements of frequency, intensity, persistence, and unique voice function. A survey of voice recordings shows differences in vocalization patterns between positive and negative subjects with COVID-19, such as vocalization, breathing, and coughing sounds. Results may suggest the potential for disease monitoring in asymptomatic patients.

The results presented here avoid these shortcomings, allow continuous evaluation of respiratory biomarkers that correlate with health status and droplet / aerosol production, and provide additional information on a variety of traditional vital signs.

Here, a simple wireless surveillance device is combined with a cloud interface and a data analysis approach to combine traditional (heart rate, respiratory rate, physical activity, body orientation, temperature, etc.) and non-traditional (eg, coughing, speaking) COVID. -19 Physiological parameters directly related.

The results are for 1) detecting early signs of symptoms in healthcare professionals and other high-risk populations, 2) monitoring the progression of symptoms in infected individuals, and 3) tracking responses to treatment in the clinical setting. It serves as a quantitative basis. In addition, the systematic studies presented here show that the cough, talk, and laughter events measured with these devices correlate with the total amount of droplet formation.

This link provides an opportunity to quantify an individual’s infectivity as important information in patient care and to improve risk stratification in the context of contact tracing and individual quarantine.

Statistics may provide insights for developing guidelines for disease management and containment. However, further studies on the expanded patient population, including detailed demographic information, are needed to enable studies of demographic-dependent and / or personally distributed big databases of relevant biometrics.

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