Keywords: Internet of Things;machine and deep learning algorithm;COVID-19 1.Introduction The purpose of our systematic review is to examine the recently published literature on COVID-19 prevention, testing, detection, and treatment, and integrate the insights it configures on machine and deep learning algorithms, computer vision technologies, and Internet of Things-based healthcare monitoring systems. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), COVID19 detection and diagnostic tools (section 4), machine learning techniques, healthcare sensor devices, and computer vision (section 5), machine learning algorithms and Internet of Things-based monitoring systems (section 6), discussion (section 7), synopsis of the main research outcomes (section 8), conclusions (section 9), limitations, implications, and further directions of research (section 10). (Table 3) 5.Machine Learning Techniques, Healthcare Sensor Devices, and Computer Vision Internet of Things-based healthcare monitoring systems are pivotal in accurate and suitable patient treatment (Jain et al., 2021;Li et al., 2021;Rhayem et al., 2021;Zhang et al., 2021a) by integrating medical wearable sensors, actuators, and networked devices. (Table 4) 6.Machine Learning Algorithms and Internet of Things-based Monitoring Systems Internet of Medical Things devices and wearables can be pivotal in contact tracing, early diagnosis, and symptom tracking (Khowaja et al., 2021;Mehrdad et al., 2021;Tai et al., 2021) by use of machine learning techniques, neural network architectures, and data fusion.
Despite the relevance of Internet of Things-based health monitoring systems, artificial intelligence-driven diagnostic algorithms, and body area sensor networks in COVID-19 prevention, screening, and treatment, only limited research has been conducted on this topic. Throughout January 2022, I performed a quantitative literature review of the Web of Science, Scopus, and ProQuest databases, with search terms including "COVID-19" + "Internet of Things-based health monitoring systems," "artificial intelligence-driven diagnostic algorithms," and "body area sensor networks." Keywords: Internet of Medical Things;COVID-19;body area sensor network 1.Introduction The purpose of my systematic review is to examine the recently published literature on COVID-19 prevention, screening, and treatment, and integrate the insights it configures on Internet of Things-based health monitoring systems, artificial intelligence-driven diagnostic algorithms, and body area sensor networks. Sensitive physiological data collected by medical sensor devices can be instrumental in COVID-19 prevention, clinical observation, visual data analysis, patient diagnosis, contact tracing, and intervention processes, optimizing Internet of Medical Things system interoperability. 3.Methodology Throughout January 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including "COVID-19" + "Internet of Things-based health monitoring systems," "artificial intelligence-driven diagnostic algorithms," and "body area sensor networks." Sensitive physiological data collected by medical sensor devices can be instrumental in COVID-19 prevention, clinical observation, visual data analysis, patient diagnosis, contact tracing, and intervention processes, optimizing Internet of Medical Things system interoperability.
Keywords: remote patient monitoring;body sensor network;COVID-19 1.Introduction The purpose of our systematic review is to examine the recently published literature on COVID-19 remote patient monitoring and integrate the insights it configures on machine and deep learning techniques, body sensor networks, and Internet of Things-based smart healthcare systems. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), COVID-19 physiological sensor data measurement and healthcare monitoring (section 4), COVID-19 detection and monitoring tools (section 5), Internet of Medical Things-enabled remote healthcare services (section 6), discussion (section 7), synopsis of the main research outcomes (section 8), conclusions (section 9), limitations, implications, and further directions of research (section 10). Internet of Things-enabled wearable medical devices and biological sensors transfer relevant data to optimize the performance of medical personnel, integrating monitoring and prevention, and treatment strategies. Medical data exchange can result in enhanced healthcare quality and systems, optimizing the feedback time in emergency situations, and precise detection and control of COVID-19.
The aim of this systematic review is to synthesize and analyze Internet of Medical Things-driven remote monitoring systems, big healthcare data analytics, and wireless body area networks in COVID-19 detection and diagnosis. Keywords: COVID-19;Internet of Medical Things;wireless body area network 1.Introduction The purpose of my systematic review is to examine the recently published literature on COVID-19 detection and diagnosis, and integrate the insights it configures on Internet of Medical Things-driven remote monitoring systems, big healthcare data analytics, and wireless body area networks. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), remote monitoring of patients in Internet of Medical Things (section 4), wireless body area networks and wearable Internet of Things-enabled smart devices (section 5), Internet of Medical Things-assistive technologies, data mining techniques, and decision support systems (section 6), discussion (section 7), synopsis of the main research outcomes (section 8), conclusions (section 9), limitations, implications, and further directions of research (section 10). 3.Methodology Throughout January 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including "COVID-19" + "Internet of Medical Things-driven remote monitoring systems," "big healthcare data analytics," and "wireless body area networks."
Keywords: Internet of Medical Things;diagnostic algorithm;COVID-19 1.Introduction The purpose of our systematic review is to examine the recently published literature on COVID-19 detection and treatment and integrate the insights it configures on smart wearable Internet of Medical Things technologies, artificial intelligence-based diagnostic algorithms, and real-time healthcare monitoring systems. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), networked sensors, wearable devices, and smart clinical systems (section 4), real-time healthcare monitoring systems and processing algorithms in Internet of Medical Things (section 5), smart personalized healthcare applications and services (section 6), discussion (section 7), synopsis of the main research outcomes (section 8), conclusions (section 9), limitations, implications, and further directions of research (section 10). 4.Networked Sensors, Wearable Devices, and Smart Clinical Systems Internet of Medical Things is pivotal in heterogeneous clinical trials, disease monitoring, and healthcare procedures (Gul et al., 2021;Maitra et al., 2021;Scrugli et al., 2022) through wireless data collection, analysis, and sharing. Specialized machine learning and predictive algorithms can be pivotal in preventive screenings, monitoring vital signs and life-threatening conditions, and supporting clinical judgment in COVID-19 early recognition and treatment by analyzing patient records and clinical data.
Keywords: Internet of Things;wearable medical sensor device;COVID-19 1.Introduction The purpose of my systematic review is to examine the recently published literature on COVID-19 patient screening, diagnosis, monitoring, and treatment, and integrate the insights it configures on wearable medical sensor devices, machine and deep learning algorithms, and Internet of Things-based healthcare systems. The identified gaps advance how smart healthcare services are essential in remote patient monitoring through medical data storage, transfer, sharing, processing, collection, and analysis. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), machine learning algorithms in COVID-19 patient screening, diagnosis, monitoring, tracking, and treatment (section 4), wireless wearable healthcare networks and smart mobile devices in Internet of Medical Things (section 5), smart healthcare services in remote patient monitoring (section 6), discussion (section 7), synopsis of the main research outcomes (section 8), conclusions (section 9), limitations, implications, and further directions of research (section 10). Taking into account the physiological features of people, distinct treatment replications through medical sensor devices can be performed to evaluate the health risk and establish exemplary medical procedures.
Keywords: smart healthcare wearable device;COVID-19;Internet of Medical Things 1.Introduction The purpose of our systematic review is to examine the recently published literature on COVID-19 prevention, screening, detection, diagnosis, and treatment and integrate the insights it configures on Internet of Medical Things-based clinical decision support systems, smart healthcare wearable devices, and machine learning algorithms. 3.Methodology Throughout February 2022, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including "COVID-19" + "Internet of Medical Things-based clinical decision support systems," "smart healthcare wearable devices," and "machine learning algorithms." COVID-19 data management can integrate real-time analysis of electronic health records (Akinbi et al., 2021;Asadzadeh et al., 2022;Rasheed et al., 2021), assisting in prevention, screening, detection, diagnosis, and treatment. Internet of Medical Things-driven remote monitoring systems can provide in-depth individualized healthcare data collection, analysis, processing, and diagnosis.
Based on an in-depth survey of the literature, the purpose of the paper is to explore remote patient monitoring systems, wearable Internet of Medical Things sensor devices, and deep learning-based computer vision algorithms in COVID-19 screening, detection, diagnosis, and treatment. Keywords: remote patient monitoring;Internet of Medical Things;COVID-19 1.Introduction The purpose of my systematic review is to examine the recently published literature on COVID-19 screening, detection, diagnosis, and treatment, and integrate the insights it configures on remote patient monitoring systems, wearable Internet of Medical Things sensor devices, and deep learning-based computer vision algorithms. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), machine and deep learning-based COVID-19 diagnostic and predicting tools and applications (section 4), wearable Internet of Medical Things devices and sensing technologies (section 5), machine learning algorithms, implantable medical devices, wireless body networks, and computer vision (section 6), discussion (section 7), synopsis of the main research outcomes (section 8), conclusions (section 9), limitations, implications, and further directions of research (section 10). (Table 4) 6.Machine Learning Algorithms, Implantable Medical Devices, Wireless Body Networks, and Computer Vision Internet of Medical Things can be instrumental in COVID-19 prevention and detection accuracy (Douglas Miller and Brown, 2019;Kong et al., 2021;Li et al., 2021;Rhayem et al., 2021) through data collection and processing, healthcare monitoring systems, and intervention measures.
Telehealth can be used to decrease healthcare worker exposure and personal protective equipment donning, doffing, and conservation, while caring for COVID-19 patients and providing virtual urgent care screenings. The manuscript is organized as following: theoretical overview (section 2), methodology (section 3), interconnected and heterogeneous networks in patient diagnosis, monitoring, and treatment (section 4), monitoring systems and wearable sensors integrated in Internet of Medical Things and smart healthcare (section 5), networked wearable devices, machine learning algorithms, and Internet of Medical Things (section 6), discussion (section 7), synopsis of the main research outcomes (section 8), conclusions (section 9), limitations, implications, and further directions of research (section 10). 4.Interconnected and Heterogeneous Networks in Patient Diagnosis, Monitoring, and Treatment Smart healthcare leverages Internet of Medical Things, wireless communication technologies, medical sensors, wearable devices, and machine learning algorithms (Calvillo-Arbizu et al., 2021;Chang et al., 2022;Muhammad et al., 2021) to inspect patient data. Telehealth can be used to decrease healthcare worker exposure and personal protective equipment donning, doffing, and conservation, while caring for COVID-19 patients and providing virtual urgent care screenings. Internet of Medical Things articulates appropriate and inexpensive manners for healthcare delivery by integrating remote access in patient physiological data collection while harnessing machine learning techniques in diagnosis assistance.
(Poppas et al , 2020) 2 Conceptual Framework and Literature Review Patients who have progressed most from the increased convenience of telehealth services encounter obstacles leaving the house as a result of chronic illness, proceed along to see a specialist, or reside in an inadequately serviced location with unsatisfactory access to care Descriptive statistics of compiled data from the completed surveys were calculated when appropriate 4 Survey Methods and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States (Kaplan, 2021) For patients in the process of mental health treatment who are worried about COVID-19 exposure risk, telehealth has enabled uninterruptedness of mental health care (Hirko et al , 2020) Health systems have advanced automated logic flows that transfer moderate-to-high-risk COVID-19 confirmed individuals to nurse triage lines while allowing them to arrange video visits with healthcare providers so as to prevent transit to in-person care settings
(Rathore et al., 2020) With the advancement of Internet of Thingsbased smart healthcare systems and cloud computing, inexpensive health services and associated support, coherent regulation of the centralized administration (Lăzăroiu et al., 2021), and public health monitoring can be carried out. Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States. (Ismail et al., 2020) Real-time remote monitoring applications, through Internet of Things-based medical implants and wearable devices, can decrease clinical visits and hospital care. (Santagati et al., 2020) Internet of Medical Things articulates the networked infrastructure of smart healthcare devices and software applications, ensuring data storage on cloud platforms and leading to accurate diagnoses while preventing and tracking chronic illnesses.
Methodology and Empirical Analysis Building our argument by drawing on data collected from Accenture, Amwell, Deloitte, Ericsson ConsumerLab, Kyruus, The Rockefeller Foundation, Syneos Health, and USAID, we performed analyses and made estimates regarding artificial intelligence-driven biosensors in diagnosis, surveillance, and prevention during the COVID-19 pandemic. Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States. Results and Discussion Artificial intelligence-enabled wearable medical devices for preliminary disease detection and monitoring and physiochemical alterations assist in medical diagnosis, assessing infection levels and subsequent therapeutic decision through artificial intelligence-driven biosensors. (Jaleel et al., 2020) Deep machine learning and cloud computing are pivotal in Internet of Things-based healthcare by enabling data analytics-based smart medical services (Lăzăroiu et al., 2021) in evidence-based decision making, remote monitoring, disease prevention and diagnoses, and risk factor identification.
(Alimadadi et al., 2020) In clinical settings, Internet of Medical Things optimizes patient-centric undertakings with remote patient monitoring, and, in clinical trials, accurately tracks vital signs, blood-sugar levels, and weight trends. (Usak et al., 2020) Internet of Things-assisted cloud-based health monitoring systems deploy heterogeneous physiological and environmental signals to supply contextual data through artificial intelligence-based diagnostic algorithms. Methodology and Empirical Analysis Building our argument by drawing on data collected from Accenture, AIR, Amwell, Ericsson ConsumerLab, Ginger, Kyruus, PwC, and Syneos Health, we performed analyses and made estimates regarding how connected wearable biomedical devices can assist in configuring precise diagnoses. Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States.
Building our argument by drawing on data collected from Accenture, GlobalWebIndex, GoMo Health, KPMG, McKinsey, Oracle, Sermo, STAT, Statista, and Workplace Intelligence, we performed analyses and made estimates regarding how predictive big data analytics, body sensor networks, medical wearable devices, decision support systems, and wireless sensing applications can be harnessed in real-time continuous remote monitoring of patients vital signs configuring clinical data in pervasive mobile patient-centric healthcare. Introduction The extensive data of COVID-19 patients can be assimilated and inspected by cutting-edge machine learning algorithms to grasp the pattern of viral transmission, optimize diagnostic swiftness and precision, advance adequate therapeutic methods, and identify the most vulnerable individuals according to personalized genetic and physiological features. Methodology and Empirical Analysis Building our argument by drawing on data collected from Accenture, Global-WebIndex, GoMo Health, KPMG, McKinsey, Oracle, Sermo, STAT, Statista, and Workplace Intelligence, we performed analyses and made estimates regarding how predictive big data analytics, body sensor networks, medical wearable devices, decision support systems, and wireless sensing applications can be harnessed in real-time continuous remote monitoring of patients' vital signs configuring clinical data in pervasive mobile patient-centric healthcare. Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States.
Introduction Wireless biomedical sensing devices and artificial intelligence-based diagnostic algorithms gather the alterations of vital signs in human body for medical data analysis. Methodology and Empirical Analysis Using and replicating data from Accenture, Deloitte, The Economist, eMarketer, Gartner, GE Healthcare, Ginger, McKinsey, MIT Technology Review Insights, PwC, R2G, SSCG Media Group, Statista, and Sykes, we performed analyses and made estimates regarding the integration of connected wearable medical devices and clinical data. Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States. (Chowdhury et al., 2020) Integrating connected wearable medical devices and clinical data leads to increased informed diagnostics and treatment decisions through remote patient monitoring and support care.
Employing recent research results covering digital epidemiological surveillance, smart telemedicine diagnosis systems, and machine learning-based real-time data sensing and processing in COVID-19 remote patient monitoring, and building our argument by drawing on data collected from Accenture, Amwell, Black Book Market Research, CMA, CFPC, Deloitte, HBR, Kyruus, PwC, RCPSC, Sage Growth Partners, and Sony, we performed analyses and made estimates regarding machine learning algorithms and deep neural network-driven Internet of Things in remote patient monitoring. Methodology and Empirical Analysis Building our argument by drawing on data collected from Accenture, Amwell, Black Book Market Research, CMA, CFPC, Deloitte, HBR, Kyruus, PwC, RCPSC, Sage Growth Partners, and Sony, we performed analyses and made estimates regarding machine learning algorithms and deep neural network-driven Internet of Things in remote patient monitoring. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate. 4.Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States. (Jiang et al., 2020) The efficient deployment and utilization of data fusion (Lazaroiu and Harrison, 2021) enable accurate evaluation in remote patient monitoring, optimizing preventive care for chronic diseases by use of machine learning-based automated diagnostic systems and artificial intelligence-enabled wearable medical devices.
(Usak et al., 2020) Cloud and wireless sensor networks (Lazaroiu et al., 2021) harnessed in data processing and storage (Andronie et al., 2021a, b) can ensure monitoring rehabilitation and recovery processes by analyzing health status and behavioral changes. Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States. (Khan and Algarni, 2020) The advancement of smart and computerized molecular diagnostic tools harnessing biomedical big data analysis, cloud computing, and machine learning-based real-time data sensing and processing (Kovacova and Lazaroiu, 2021) can assist in COVID-19 detection, monitoring, and treatment, and cloud data storage for supportive decisions. Conclusions, Implications, Limitations, and Further Research Directions Internet of Medical Things assists smart healthcare systems in analyzing gathered data, integrating wearable health monitoring sensors, diagnostics tools, and telemedicine equipment during the COVID-19 pandemic by use of wireless biomedical sensing devices.
Empirical evidence on artificial intelligence-powered diagnostic tools, networked medical devices, and cyber-physical healthcare systems in assessing and treating patients with COVID-19 symptoms has been scarcely documented in the literature. (Tsikala Vafea et al., 2020) Internet of Medical Things necessitates the deployment of health data from wearable mobile healthcare and smart sensing devices and applications networked across electronic health records in clinical and diagnostic decision support and remote healthcare systems. (Williams Samuel et al., 2020) COVID-19 detection and monitoring systems can acquire instantaneous symptom data from artificial intelligence-enabled wearable medical devices, identifying potential COVID-19 cases by use of machine learning algorithms. Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States.
(Zhang and Han, 2020) Real-time patient monitoring and biomedical big data are determining in disease prediction, diagnosis, and support clinical decision by use of artificial intelligence-enabled wearable medical devices and machine learning-based automated diagnostic systems. Study Design, Survey Methods, and Materials The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States. (Chen et al., 2020) COVID-19 detection and monitoring systems can be put into action throughout an Internet of Medical Things infrastructure, monitoring both potential and confirmed patients in real time, and as regards the treatment responses of recovered individuals, while grasping the nature of the virus by acquiring, inspecting, and archiving valuable data. (Bordel et al., 2020) Internet of Medical Things deploys networked medical devices and wireless communication to facilitate the sharing of healthcare data through artificial intelligence-based diagnostic algorithms, real-time medical data analytics, and machine learning-based automated diagnostic systems.
