AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges
The COVID-19 pandemic has overwhelmed the existing healthcare infrastructure in many parts of the world. Healthcare professionals are not only over-burdened but also at a high risk of nosocomial transmission from COVID-19 patients. Screening and monitoring the health of a large number of susceptible...
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| Veröffentlicht in: | Computing Jg. 105; H. 4; S. 783 - 809 |
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01.04.2023
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| Abstract | The COVID-19 pandemic has overwhelmed the existing healthcare infrastructure in many parts of the world. Healthcare professionals are not only over-burdened but also at a high risk of nosocomial transmission from COVID-19 patients. Screening and monitoring the health of a large number of susceptible or infected individuals is a challenging task. Although professional medical attention and hospitalization are necessary for high-risk COVID-19 patients, home isolation is an effective strategy for low and medium risk patients as well as for those who are at risk of infection and have been quarantined. However, this necessitates effective techniques for remotely monitoring the patients’ symptoms. Recent advances in Machine Learning (ML) and Deep Learning (DL) have strengthened the power of imaging techniques and can be used to remotely perform several tasks that previously required the physical presence of a medical professional. In this work, we study the prospects of vital signs monitoring for COVID-19 infected as well as quarantined individuals by using DL and image/signal-processing techniques, many of which can be deployed using simple cameras and sensors available on a smartphone or a personal computer, without the need of specialized equipment. We demonstrate the potential of ML-enabled workflows for several vital signs such as heart and respiratory rates, cough, blood pressure, and oxygen saturation. We also discuss the challenges involved in implementing ML-enabled techniques. |
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| AbstractList | The COVID-19 pandemic has overwhelmed the existing healthcare infrastructure in many parts of the world. Healthcare professionals are not only over-burdened but also at a high risk of nosocomial transmission from COVID-19 patients. Screening and monitoring the health of a large number of susceptible or infected individuals is a challenging task. Although professional medical attention and hospitalization are necessary for high-risk COVID-19 patients, home isolation is an effective strategy for low and medium risk patients as well as for those who are at risk of infection and have been quarantined. However, this necessitates effective techniques for remotely monitoring the patients’ symptoms. Recent advances in Machine Learning (ML) and Deep Learning (DL) have strengthened the power of imaging techniques and can be used to remotely perform several tasks that previously required the physical presence of a medical professional. In this work, we study the prospects of vital signs monitoring for COVID-19 infected as well as quarantined individuals by using DL and image/signal-processing techniques, many of which can be deployed using simple cameras and sensors available on a smartphone or a personal computer, without the need of specialized equipment. We demonstrate the potential of ML-enabled workflows for several vital signs such as heart and respiratory rates, cough, blood pressure, and oxygen saturation. We also discuss the challenges involved in implementing ML-enabled techniques. |
| Author | Narang, Pratik Rohmetra, Honnesh Guizani, Mohsen Chamola, Vinay Raghunath, Navaneeth Lakkaniga, Naga Rajiv |
| Author_xml | – sequence: 1 givenname: Honnesh surname: Rohmetra fullname: Rohmetra, Honnesh organization: Department of CSIS, Birla Institute of Technology and Science, Pilani – sequence: 2 givenname: Navaneeth surname: Raghunath fullname: Raghunath, Navaneeth organization: Department of CSIS, Birla Institute of Technology and Science, Pilani – sequence: 3 givenname: Pratik surname: Narang fullname: Narang, Pratik organization: Department of CSIS, Birla Institute of Technology and Science, Pilani – sequence: 4 givenname: Vinay surname: Chamola fullname: Chamola, Vinay organization: Department of EEE & APPCAIR, Birla Institute of Technology and Science, Pilani – sequence: 5 givenname: Mohsen surname: Guizani fullname: Guizani, Mohsen organization: Qatar University – sequence: 6 givenname: Naga Rajiv orcidid: 0000-0001-8370-2224 surname: Lakkaniga fullname: Lakkaniga, Naga Rajiv email: nlakkaniga@uams.edu organization: Department of Integrative Structural and Computational Biology, The Scripps Research Institute, SmartBio Labs |
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| Keywords | COVID-19 Deep learning 68T45 Coronavirus Artificial intelligence |
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| License | This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
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