Recent Patient Health Monitoring Platforms Incorporating Internet of Things-Enabled Smart Devices
31 Jul 2018-International Neurourology Journal (Korean Continence Society)-Vol. 22, Iss: 4, pp 313-313
TL;DR: This review focuses on recently developed patient health monitoring platforms based on IoT-enabled smart devices that can collect real-time patient data and transfer information for assessment by healthcare providers, including doctors, hospitals, and clinics, or for self-management.
Abstract: Synergistic integration of the Internet of Things (IoT), cloud computing, and big data technologies in healthcare have led to the notion of "smart health." Smart health is an emerging concept that refers to the provision of healthcare services for prevention, diagnosis, treatment, and follow-up management at any time or any place by connecting information technologies and healthcare. As a significant breakthrough in smart healthcare development, IoT-enabled smart devices allow medical centers to carry out preventive care, diagnosis, and treatment more competently. This review focuses on recently developed patient health monitoring platforms based on IoT-enabled smart devices that can collect real-time patient data and transfer information for assessment by healthcare providers, including doctors, hospitals, and clinics, or for self-management. We aimed to summarize the available information about recently approved devices and state-of-the-art developments through a comprehensive, systematic literature review. In this review, we also discuss possible future directions for the integration of cloud computing and blockchain, which may offer unprecedented breakthroughs in on-demand medical services. The combination of IoT with real-time, remote patient monitoring empowers patients to assert more control over their care, thereby allowing them to actively monitor their particular health conditions.
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TL;DR: In this article, a literature-based study may guide professionals in envisaging solutions to related problems and fighting against the COVID-19 type pandemic, which is a need to study different applications of IoT enabled healthcare.
Abstract: Background/objectives The Internet of Things (IoT) can create disruptive innovation in healthcare Thus, during COVID-19 Pandemic, there is a need to study different applications of IoT enabled healthcare For this, a brief study is required for research directions Methods Research papers on IoT in healthcare and COVID-19 Pandemic are studied to identify this technology’s capabilities This literature-based study may guide professionals in envisaging solutions to related problems and fighting against the COVID-19 type pandemic Results Briefly studied the significant achievements of IoT with the help of a process chart Then identifies seven major technologies of IoT that seem helpful for healthcare during COVID-19 Pandemic Finally, the study identifies sixteen basic IoT applications for the medical field during the COVID-19 Pandemic with a brief description of them Conclusions In the current scenario, advanced information technologies have opened a new door to innovation in our daily lives Out of these information technologies, the Internet of Things is an emerging technology that provides enhancement and better solutions in the medical field, like proper medical record-keeping, sampling, integration of devices, and causes of diseases IoT’s sensor-based technology provides an excellent capability to reduce the risk of surgery during complicated cases and helpful for COVID-19 type pandemic In the medical field, IoT’s focus is to help perform the treatment of different COVID-19 cases precisely It makes the surgeon job easier by minimising risks and increasing the overall performance By using this technology, doctors can easily detect changes in critical parameters of the COVID-19 patient This information-based service opens up new healthcare opportunities as it moves towards the best way of an information system to adapt world-class results as it enables improvement of treatment systems in the hospital Medical students can now be better trained for disease detection and well guided for the future course of action IoT’s proper usage can help correctly resolve different medical challenges like speed, price, and complexity It can easily be customised to monitor calorific intake and treatment like asthma, diabetes, and arthritis of the COVID-19 patient This digitally controlled health management system can improve the overall performance of healthcare during COVID-19 pandemic days
141 citations
TL;DR: The lifecycle of the context of IoT-based telemedicine healthcare applications is mapped for the first time, including the procedure sequencing and definition for each context, and the crossover in the taxonomy is demonstrated.
138 citations
TL;DR: OFETs are revealed to be one of the best systems for mimicking sensory and nervous systems and their applications in biomimetic systems and future challenges in this research area are provided.
Abstract: The emergence of flexible organic electronics that span the fields of physics and biomimetics creates the possibility for increasingly simple and intelligent products for use in everyday life. Organic field-effect transistors (OFETs), with their inherent flexibility, light weight, and biocompatibility, have shown great promise in the field of biomimicry. By applying such biomimetic OFETs for the internet of things (IoT) makes it possible to imagine novel products and use cases for the future. Recent advances in flexible OFETs and their applications in biomimetic systems are reviewed. Strategies to achieve flexible OFETs are individually discussed and recent progress in biomimetic sensory systems and nervous systems is reviewed in detail. OFETs are revealed to be one of the best systems for mimicking sensory and nervous systems. Additionally, a brief discussion of information storage based on OFETs is presented. Finally, a personal view of the utilization of biomimetic OFETs in the IoT and future challenges in this research area are provided.
132 citations
TL;DR: There is a need to design an efficient intrusion detection/prevention system that cooperates with dynamic shadow honeypots and enhance the immunity of IoMT against cyber-attacks, and this paper proposes a security solution, which is divided into five different layers to detect and prevent attacks.
131 citations
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...inhaler delivery systems, Apple Watch app that monitors depression, Apple’s Research Kit and Parkinson’s Disease and ADAMM intelligence Asthma Monitoring [9], [69] ....
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17 Feb 2021
TL;DR: Recent state-of-the-arts advances in Blockchain for IoT, Blockchain for Cloud IoT and Blockchain for Fog IoT in the context of eHealth, smart cities, intelligent transport and other applications are analyzed.
Abstract: Conventional Internet of Things (IoT) ecosystems involve data streaming from sensors, through Fog devices to a centralized Cloud server. Issues that arise include privacy concerns due to third party management of Cloud servers, single points of failure, a bottleneck in data flows and difficulties in regularly updating firmware for millions of smart devices from a point of security and maintenance perspective. Blockchain technologies avoid trusted third parties and safeguard against a single point of failure and other issues. This has inspired researchers to investigate blockchain’s adoption into IoT ecosystem. In this paper, recent state-of-the-arts advances in blockchain for IoT, blockchain for Cloud IoT and blockchain for Fog IoT in the context of eHealth, smart cities, intelligent transport and other applications are analyzed. Obstacles, research gaps and potential solutions are also presented.
121 citations
References
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TL;DR: This survey is directed to those who want to approach this complex discipline and contribute to its development, and finds that still major issues shall be faced by the research community.
12,539 citations
TL;DR: The fields of application for IoT technologies are as numerous as they are diverse, as IoT solutions are increasingly extending to virtually all areas of everyday.
Abstract: It has been next to impossible in the past months not to come across the term ‘‘Internet of Things’’ (IoT) one way or another. Especially the past year has seen a tremendous surge of interest in the Internet of Things. Consortia have been formed to define frameworks and standards for the IoT. Companies have started to introduce numerous IoTbased products and services. And a number of IoT-related acquisitions have been making the headlines, including, e.g., the prominent takeover of Nest by Google for $3.2 billion and the subsequent acquisitions of Dropcam by Nest and of SmartThings by Samsung. Politicians as well as practitioners increasingly acknowledge the Internet of Things as a real business opportunity, and estimates currently suggest that the IoT could grow into a market worth $7.1 trillion by 2020 (IDC 2014). While the term Internet of Things is now more and more broadly used, there is no common definition or understanding today of what the IoT actually encompasses. The origins of the term date back more than 15 years and have been attributed to the work of the Auto-ID Labs at the Massachusetts Institute of Technology (MIT) on networked radio-frequency identification (RFID) infrastructures (Atzori et al. 2010; Mattern and Floerkemeier 2010). Since then, visions for the Internet of Things have been further developed and extended beyond the scope of RFID technologies. The International Telecommunication Union (ITU) for instance now defines the Internet of Things as ‘‘a global infrastructure for the Information Society, enabling advanced services by interconnecting (physical and virtual) things based on, existing and evolving, interoperable information and communication technologies’’ (ITU 2012). At the same time, a multitude of alternative definitions has been proposed. Some of these definitions exhibit an emphasis on the things which become connected in the IoT. Other definitions focus on Internet-related aspects of the IoT, such as Internet protocols and network technology. And a third type centers on semantic challenges in the IoT relating to, e.g., the storage, search and organization of large volumes of information (Atzori et al. 2010). The fields of application for IoT technologies are as numerous as they are diverse, as IoT solutions are increasingly extending to virtually all areas of everyday. The most prominent areas of application include, e.g., the smart industry, where the development of intelligent production systems and connected production sites is often discussed under the heading of Industry 4.0. In the smart home or building area, intelligent thermostats and security systems are receiving a lot of attention, while smart energy applications focus on smart electricity, gas and water meters. Smart transport solutions include, e.g., vehicle fleet tracking and mobile ticketing, while in the smart health area, topics such as patients’ surveillance and chronic disease management are being addressed. And in the context of Accepted after one revision by Prof. Dr. Sinz.
3,499 citations
TL;DR: This work bridges the technological gap between signal transduction, conditioning, processing and wireless transmission in wearable biosensors by merging plastic-based sensors that interface with the skin with silicon integrated circuits consolidated on a flexible circuit board for complex signal processing.
Abstract: Wearable sensor technologies are essential to the realization of personalized medicine through continuously monitoring an individual's state of health. Sampling human sweat, which is rich in physiological information, could enable non-invasive monitoring. Previously reported sweat-based and other non-invasive biosensors either can only monitor a single analyte at a time or lack on-site signal processing circuitry and sensor calibration mechanisms for accurate analysis of the physiological state. Given the complexity of sweat secretion, simultaneous and multiplexed screening of target biomarkers is critical and requires full system integration to ensure the accuracy of measurements. Here we present a mechanically flexible and fully integrated (that is, no external analysis is needed) sensor array for multiplexed in situ perspiration analysis, which simultaneously and selectively measures sweat metabolites (such as glucose and lactate) and electrolytes (such as sodium and potassium ions), as well as the skin temperature (to calibrate the response of the sensors). Our work bridges the technological gap between signal transduction, conditioning (amplification and filtering), processing and wireless transmission in wearable biosensors by merging plastic-based sensors that interface with the skin with silicon integrated circuits consolidated on a flexible circuit board for complex signal processing. This application could not have been realized using either of these technologies alone owing to their respective inherent limitations. The wearable system is used to measure the detailed sweat profile of human subjects engaged in prolonged indoor and outdoor physical activities, and to make a real-time assessment of the physiological state of the subjects. This platform enables a wide range of personalized diagnostic and physiological monitoring applications.
3,235 citations
"Recent Patient Health Monitoring Pl..." refers background in this paper
...[28,30] also reported an IoT-embedded sweat monitoring system that used a mechanically flexible sensor array for multiplexed in situ perspiration analysis of parameters such as sweat metabolites, electro-...
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..., sodium, potassium, and other trace minerals [27,28])....
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01 Dec 2017
TL;DR: The current status of AI applications in healthcare, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation, are surveyed and its future is discussed.
Abstract: Artificial intelligence (AI) aims to mimic human cognitive functions. It is bringing a paradigm shift to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. We survey the current status of AI applications in healthcare and discuss its future. AI can be applied to various types of healthcare data (structured and unstructured). Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language processing for unstructured data. Major disease areas that use AI tools include cancer, neurology and cardiology. We then review in more details the AI applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation. We conclude with discussion about pioneer AI systems, such as IBM Watson, and hurdles for real-life deployment of AI.
1,785 citations
"Recent Patient Health Monitoring Pl..." refers background in this paper
...Most of all, the application of AI in healthcare can be expected to have overwhelming impacts on patient monitoring due to its predictive capabilities [38-40]....
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