Healthcare and patient monitoring using IoT

Akkaş, Mustafa Alper; Sokullu, Radosveta; Çetin, Hüseyin Ertürk

doi:10.1016/j.iot.2020.100173

Cited by 122 publications

(31 citation statements)

References 10 publications

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“…Akka¸s et al [19] describe an IoT-based monitoring system tested in postop ward units at Ege University Hospital in Turkey. The system collects physiological data from patients using a wireless sensor network, transferring the acquired data to a central database and triggering alarms when monitored values surpass pre-determined thresholds.…”

Section: Related Workmentioning

confidence: 99%

“…Dynamics Reliability Privacy Target (Ubiquitous) Interface to other systems/devices Middleware Health Assessment Mobility / Wireless Design and Technology Data protection Consent Platform [43] Covid-19. Clinical settings N/D N/D Machine Learning Yes / Yes Focused on predictive models N/D N/D [30] Ebola Clinical settings N/D N/D Machine Learning No / Yes High-acuity of monitored data N/D N/D [44] General Clinical settings Only for devices N/D Not clear Yes / Yes Notification layer Yes N/D [19] Chronic diseases Clinical settings N/D N/D Pre-defined thresholds Yes / Yes Radio communication N/D N/D [13] General Clinical settings N/D N/D Machine Learning N/D N/D N/D N/D [45] General. Clinical settings and home Devices Hospital systems Yes Semantic rules Yes / Yes MONERE YOAPY protocol Yes N/D [46] COVID-19 Clinical settings Based on HL7 standard Yes Thresholds Machine Learning Customizable No / Yes IEC standard Yes N/D Our work (*) ...…”

Section: Related Workmentioning

confidence: 99%

“…In the last few years, the increased adoption and use of Internet of Things (IoT) devices enabled the development of IoT-based remote health monitoring solutions [19] [20] . Several studies addressed the technological challenges of keeping track of patients’ health and detecting risk situations based on physiological markers.…”

Section: Introductionmentioning

confidence: 99%

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A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home

et al. 2022

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Due to the COVID-19 pandemic, health services around the globe are struggling. An effective system for monitoring patients can improve healthcare delivery by avoiding in-person contacts, enabling early-detection of severe cases, and remotely assessing patients’ status. Internet of Things (IoT) technologies have been used for monitoring patients’ health with wireless wearable sensors in different scenarios and medical conditions, such as noncommunicable and infectious diseases. Combining IoT-related technologies with early-warning scores (EWS) commonly utilized in infirmaries has the potential to enhance health services delivery significantly. Specifically, the NEWS-2 has been showing remarkable results in detecting the health deterioration of COVID-19 patients. Although the literature presents several approaches for remote monitoring, none of these studies proposes a customized, complete, and integrated architecture that uses an effective early-detection mechanism for COVID-19 and that is flexible enough to be used in hospital wards and at home. Therefore, this article's objective is to present a comprehensive IoT-based conceptual architecture that addresses the key requirements of scalability, interoperability, network dynamics, context discovery, reliability, and privacy in the context of remote health monitoring of COVID-19 patients in hospitals and at home. Since remote monitoring of patients at home (essential during a pandemic) can engender trust issues regarding secure and ethical data collection, a consent management module was incorporated into our architecture to provide transparency and ensure data privacy. Further, the article details mechanisms for supporting a configurable and adaptable scoring system embedded in wearable devices to increase usefulness and flexibility for health care professions working with EWS.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home

et al. 2022

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“…For example, health care sectors in various countries often use remote health monitoring systems as a reference for disease prevention, infection prediction, and emergency decision-making on infectious diseases. 5 Through Information and Communications Technology (ICT), the operational efficiency of the health care sector can be improved 6,7 and a large number of sensors and IoT devices can be integrated to collect and analyze a large amount of data. In doing so, more applications for pandemic prevention and remote care can be created.…”

Section: Introductionmentioning

confidence: 99%

Apply IOT technology to practice a pandemic prevention body temperature measurement system: A case study of response measures for COVID-19

Lin

Hsieh

Chen

et al. 2021

International Journal of Distributed Sensor Networks

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Today, the most serious threat to global health is the continuous outbreak of respiratory diseases, which is called Coronavirus Disease 2019 (COVID-19). The outbreak of COVID-19 has brought severe challenges to public health and has attracted great attention from the research and medical communities. Most patients infected with COVID-19 will have fever. Therefore, the monitoring of body temperature has become one of the most important basis for pandemic prevention and testing. Among them, the measurement of body temperature is the most direct through the Forehead Thermometer, but the measurement speed is relatively slow. The cost of fast-checking body temperature measurement equipment, such as infrared body temperature detection and face recognition temperature machine, is too high, and it is difficult to build Disease Surveillance System (DSS). To solve the above-mentioned problems, the Intelligent pandemic prevention Temperature Measurement System (ITMS) and Pandemic Prevention situation Analysis System (PPAS) are proposed in this study. ITMS is used to detect body temperature. However, PPAS uses big data analysis techniques to prevent pandemics. In this study, the campus field is used as an example, in which ITMS and PPAS are used. In the research, Proof of Concept (PoC), Proof of Service (PoS), and Proof of Business (PoB) were carried out for the use of ITMS and PPAS in the campus area. From the verification, it can be seen that ITMS and PPAS can be successfully used in campus fields and are widely recognized by users. Through the verification of this research, it can be determined that ITMS and PPAS are indeed feasible and capable of dissemination. The ITMS and PPAS are expected to give full play to their functions during the spread of pandemics. All in all, the results of this research will provide a wide range of applied thinking for people who are committed to the development of science and technology.

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“…It has been accelerated with the availability of cheaper component and advancement in wireless communication technology, which has also enabled the application of the Internet of Things (IoT) in the wearable biomedical applications. According to a report by Grand View Research Inc. [1], the Internet of Things (IoT) penetration in the health sector is expected to reach a volume of approximately $ 409.9 billion by 2022. Thus, there is a huge boom in the wearable industry.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Wearable Biomedical Systems

Chowdhury¹,

Khandakar²,

Qiblawey³

et al. 2020

Sports Science and Human Health - Different Approaches

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Wearable technology has added a whole new dimension in the healthcare system by real-time continuous monitoring of human body physiology. They are used in daily activities and fitness monitoring and have even penetrated in monitoring the health condition of patients suffering from chronic illnesses. There are a lot of research and development activities being pursued to develop more innovative and reliable wearable. This chapter will cover discussions on the design and implementation of wearable devices for different applications such as real-time detection of heart attack, abnormal heart sound, blood pressure monitoring, gait analysis for diabetic foot monitoring. This chapter will also cover how the signals acquired from these prototypes can be used for training machine learning (ML) algorithm to diagnose the condition of the person wearing the device. This chapter discusses the steps involved in (i) hardware design including sensors selection, characterization, signal acquisition, and communication to decision-making subsystem and (ii) the ML algorithm design including feature extraction, feature reduction, training, and testing. This chapter will use the case study of the design of smart insole for diabetic foot monitoring, wearable real-time heart attack detection, and smart-digital stethoscope system to show the steps involved in the development of wearable biomedical systems.

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Healthcare and patient monitoring using IoT

Cited by 122 publications

References 10 publications

A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home

A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home

Apply IOT technology to practice a pandemic prevention body temperature measurement system: A case study of response measures for COVID-19

Machine Learning in Wearable Biomedical Systems

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