https://www.socictopen.socict.org/files/original/d5b9b38155aed1476cba9be1ed5656cf.pdf 3a502e90ccf62f78cd4d2c1a82196571 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Coronavirus Description An account of the resource Dominio científico: Coronavirus Text A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text. Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Automated, multiparametric monitoring of respiratory biomarkers and vital signs in clinical and home settings for COVID-19 patients. Creator An entity primarily responsible for making the resource Xiaoyue Ni, Wei Ouyang, Hyoyoung Jeong, Jin-Tae Kim, Andreas Tzaveils, Ali Mirzazadeh, Changsheng Wu, Jong Yoon Lee, Matthew Keller, Chaithanya K Mummidisetty, Manish Patel, Nicholas Shawen, Joy Huang, Hope Chen, Sowmya Ravi, Jan-Kai Chang, KunHyuck Lee, Yixin Wu, Ferrona Lie, Youn J Kang, Jong Uk Kim, Leonardo P Chamorro, Anthony R Banks, Ankit Bharat, Arun Jayaraman, Shuai Xu, John A Rogers Description An account of the resource Capabilities in continuous monitoring of key physiological parameters of disease have never been more important than in the context of the global COVID-19 pandemic. Soft, skin-mounted electronics that incorporate high-bandwidth, miniaturized motion sensors enable digital, wireless measurements of mechanoacoustic (MA) signatures of both core vital signs (heart rate, respiratory rate, and temperature) and underexplored biomarkers (coughing count) with high fidelity and immunity to ambient noises. This paper summarizes an effort that integrates such MA sensors with a cloud data infrastructure and a set of analytics approaches based on digital filtering and convolutional neural networks for monitoring of COVID-19 infections in sick and healthy individuals in the hospital and the home. Unique features are in quantitative measurements of coughing and other vocal events, as indicators of both disease and infectiousness. Systematic imaging studies demonstrate correlations between the time and intensity of coughing, speaking, and laughing and the total droplet production, as an approximate indicator of the probability for disease spread. The sensors, deployed on COVID-19 patients along with healthy controls in both inpatient and home settings, record coughing frequency and intensity continuously, along with a collection of other biometrics. The results indicate a decaying trend of coughing frequency and intensity through the course of disease recovery, but with wide variations across patient populations. The methodology creates opportunities to study patterns in biometrics across individuals and among different demographic groups. Date A point or period of time associated with an event in the lifecycle of the resource 2021 Subject The topic of the resource respiratory disease, covid-19, Biomarkers, Digital health, wearable electronics Identifier An unambiguous reference to the resource within a given context 10.1073/pnas.2026610118 Source A related resource from which the described resource is derived Proceedings of the National Academy of Sciences of the United States of America