AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges

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.

Cough Sounds Recorded via Smart Devices as Useful Non-Invasive Digital Biomarkers of Aspiration Risk: A Case Report

“…Acoustic sounds have been used to detect laryngeal disorders [ 24 ] and respiratory disorders [ 25 ]. Whereas past studies on automatic detection of cough sounds required the use of heavy equipment, recent advancements in digital devices and the rise in telehealth demand with the COVID-19 pandemic [ 4 , 6 , 7 ] have facilitated the capabilities of mobile devices to record cough sounds successfully. New techniques can extract features that can be applied to algorithms for diagnosis [ 5 ], or community-acquired pneumonia [ 1 ].…”

“…With recent advances in digital devices, new sophisticated technologies have been introduced to record and analyze the acoustic properties of cough sounds. These have been validated to be useful in monitoring, diagnosis, and evaluation of response to treatment in patients with airway disorders, such as asthma and chronic obstructive pulmonary disease (COPD), or even in patients with coronavirus disease 2019 (COVID-19) [ 3 , 4 , 5 , 6 , 7 ]. Cough sounds can reflect cough intensity, frequency, or characteristics, with automatic cough frequencies used as endpoints and surrogates for primary endpoints.…”

Mobile Applications for COVID-19: Benefits, Technologies and Future Research Opportunities

“…To this purpose, machine learning algorithms and other advanced artificial intelligence tools can be used. The potentials of machine learning for contactless COVID-19 screenings are enormous [13]. These technologies assist in an efficient detection of patterns and trends for a great amount of data.…”

Soft Transducer for Patient’s Vitals Telemonitoring with Deep Learning-Based Personalized Anomaly Detection

“…In this regard, monitoring patient vitals represents an important aspect of patient care because these signs usually give early information about abnormal physiology. In practice, this can be accomplished by employing soft transducers, which are wearable devices able to acquire and process a large amount of data in real time [12,13]. Indeed, it is crucial for physicians to be able not only to monitor hypertensive patients regularly but also to predict the evolution of this condition.…”