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(searched for: doi:10.1016/j.iot.2020.100251)
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Published: 21 October 2022
by MDPI
Journal: Sensors
Sensors, Volume 22; https://doi.org/10.3390/s22208073

Abstract:
The Internet of Things (IoT) is a complete ecosystem encompassing various communication technologies, sensors, hardware, and software. IoT cutting-edge technologies and Artificial Intelligence (AI) have enhanced the traditional healthcare system considerably. The conventional healthcare system faces many challenges, including avoidable long wait times, high costs, a conventional method of payment, unnecessary long travel to medical centers, and mandatory periodic doctor visits. A Smart healthcare system, Internet of Things (IoT), and AI are arguably the best-suited tailor-made solutions for all the flaws related to traditional healthcare systems. The primary goal of this study is to determine the impact of IoT, AI, various communication technologies, sensor networks, and disease detection/diagnosis in Cardiac healthcare through a systematic analysis of scholarly articles. Hence, a total of 104 fundamental studies are analyzed for the research questions purposefully defined for this systematic study. The review results show that deep learning emerges as a promising technology along with the combination of IoT in the domain of E-Cardiac care with enhanced accuracy and real-time clinical monitoring. This study also pins down the key benefits and significant challenges for E-Cardiology in the domains of IoT and AI. It further identifies the gaps and future research directions related to E-Cardiology, monitoring various Cardiac parameters, and diagnosis patterns.
Published: 16 September 2022
World journal of clinical cases, Volume 10, pp 9207-9218; https://doi.org/10.12998/wjcc.v10.i26.9207

Abstract:
The coronavirus disease 2019 (COVID-19) has currently caused the mortality of millions of people around the world. Aside from the direct mortality from the COVID-19, the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients. Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality, which did not relate to COVID-19 infection. It has in fact increased the risk of death in cardiovascular disease (CVD) patients. For this purpose, it is dramatically inevitable to monitor CVD patients’ vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in death. Internet of things (IoT) and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’ data. The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments. To this end, this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments. Experimental results showed that the proposed method was able to detect cardiovascular events with better performance (95.30% average sensitivity and 95.94% mean prediction values).
Published: 1 August 2022
by MDPI
Journal: Electronics
Abstract:
The past century has seen the ongoing development of amplifiers for different electrophysiological signals to study the work of the heart. Since the vacuum tube era, engineers and designers of bioamplifiers for recording electrophysiological signals have been trying to achieve similar objectives: increasing the input impedance and common-mode rejection ratio, as well as reducing power consumption and the size of the bioamplifier. This review traces the evolution of bioamplifiers, starting from circuits on vacuum tubes and discrete transistors through circuits on operational and instrumental amplifiers, and to combined analog-digital solutions on analog front-end integrated circuits. Examples of circuits and their technical features are provided for each stage of the bioamplifier development. Special emphasis is placed on the review of modern analog front-end solutions for biopotential registration, including their generalized structural diagram and table of comparative characteristics. A detailed review of analog front-end circuit integration in various practical applications is provided, with examples of the latest achievements in the field of electrocardiogram, electroencephalogram, and electromyogram registration. The review concludes with key points and insights for the future development of the analog front-end concept applied to bioelectric signal registration.
Published: 16 July 2022
by MDPI
Journal: Sensors
Sensors, Volume 22; https://doi.org/10.3390/s22145327

Abstract:
In healthcare, there are rapid emergency response systems that necessitate real-time actions where speed and efficiency are critical; this may suffer as a result of cloud latency because of the delay caused by the cloud. Therefore, fog computing is utilized in real-time healthcare applications. There are still limitations in response time, latency, and energy consumption. Thus, a proper fog computing architecture and good task scheduling algorithms should be developed to minimize these limitations. In this study, an Energy-Efficient Internet of Medical Things to Fog Interoperability of Task Scheduling (EEIoMT) framework is proposed. This framework schedules tasks in an efficient way by ensuring that critical tasks are executed in the shortest possible time within their deadline while balancing energy consumption when processing other tasks. In our architecture, Electrocardiogram (ECG) sensors are used to monitor heart health at home in a smart city. ECG sensors send the sensed data continuously to the ESP32 microcontroller through Bluetooth (BLE) for analysis. ESP32 is also linked to the fog scheduler via Wi-Fi to send the results data of the analysis (tasks). The appropriate fog node is carefully selected to execute the task by giving each node a special weight, which is formulated on the basis of the expected amount of energy consumed and latency in executing this task and choosing the node with the lowest weight. Simulations were performed in iFogSim2. The simulation outcomes show that the suggested framework has a superior performance in reducing the usage of energy, latency, and network utilization when weighed against CHTM, LBS, and FNPA models.
, Dongming Peng, Jason Payne, Hamid Sharif
IEEE Access, Volume 10, pp 63684-63697; https://doi.org/10.1109/access.2022.3182704

Abstract:
Remote electrocardiogram (ECG) diagnosis with continuous real-time or near-real-time performance via a wireless wearable computing system would have significant value since it will enable on-time alerts and interventions, leading to life-saving outcomes. In this paper, a novel integration of open-loop and closed-loop switch modes is proposed, where the entire ECG diagnosis process is accomplished in three steps. First, the R-peak detection algorithm for initial diagnosis in local devices is executed, and 100% of the transmission energy is saved when no abnormality is detected. Second, in the case of an abnormality being detected, the edge device performs two-dimensional convolution neural network (2D-CNN) classification on the ECG signals, leading to either open-loop or closed-loop mode transmission based on the seriousness of the ECG signals. Third, in the cloud server, the received ECG signals may be further analyzed with a more sophisticated classification algorithm. The ECG classification accuracy ranges from 92.7 % to 99.1% depending on whether the analysis is executed locally (with reduced communication costs) or remotely (with increased computing resources). Overall, the ECG diagnosis process is partitioned into three components, including 1) irregular heartbeat detection, 2) 2-D CNN classification in the edge device, and 3) further classification in the cloud. The simulation results of such partition of ECG diagnosis in these layers supervised by the open-loop and closed-loop switch modes have demonstrated that the proposed system architecture can achieve efficient ECG diagnosis via wearable technologies with both reliable accuracies and reduced communication energy.
, Parma Nand
International Journal of Information Technology, Volume 14, pp 2093-2103; https://doi.org/10.1007/s41870-022-00922-z

The publisher has not yet granted permission to display this abstract.
, Alex Sandro Roschildt Pinto, Carlos Montez
Published: 30 March 2022
Internet of Things, Volume 19; https://doi.org/10.1016/j.iot.2022.100516

The publisher has not yet granted permission to display this abstract.
Abhishek Kumar, SwarnAvinash Kumar, Vishal Dutt, Ashutosh Kumar Dubey, Vicente García-Díaz
Published: 18 March 2022
Biomedical Signal Processing and Control, Volume 76; https://doi.org/10.1016/j.bspc.2022.103638

The publisher has not yet granted permission to display this abstract.
, S. Padmakala, C. A. Subasini, S. P. Karuppiah
Computer Methods in Biomechanics and Biomedical Engineering, Volume 25, pp 1180-1194; https://doi.org/10.1080/10255842.2022.2034795

Abstract:
In recent years, cardiovascular disease becomes a prominent source of death. The web services connect other medical equipments and the computers via internet for exchanging and combining the data in novel ways. The accurate prediction of heart disease is important to prevent cardiac patients prior to heart attack. The main drawback of heart disease is delay in identifying the disease in the early stage. This objective is obtained by using the machine learning method with rich healthcare information on heart diseases. In this paper, the smart healthcare method is proposed for the prediction of heart disease using Biogeography optimization algorithm and Mexican hat wavelet to enhance Dragonfly algorithm optimization with mixed kernel based extreme learning machine (BMDA–MKELM) approach. Here, data is gathered from the two devices such as sensor nodes as well as the electronic medical records. The android based design is utilized to gather the patient data and the reliable cloud-based scheme for the data storage. For further evaluation for the prediction of heart disease, data are gathered from cloud computing services. At last, BMDA–MKELM based prediction scheme is capable to classify cardiovascular diseases. In addition to this, the proposed prediction scheme is compared with another method with respect to measures such as accuracy, precision, specificity, and sensitivity. The experimental results depict that the proposed approach achieves better results for the prediction of heart disease when compared with other methods.
Vaneeta Bhardwaj, Rajat Joshi,
Published: 20 January 2022
Sn Computer Science, Volume 3, pp 1-11; https://doi.org/10.1007/s42979-022-01015-1

The publisher has not yet granted permission to display this abstract.
Deep Mala, Abhineet Anand, Naresh Kumar Tiwari, M. Arvindhan
Published: 1 January 2022
Abstract:
In healthcare cloud computing is increasing efficiency whilst decreasing the cost in many aspects. Things are becoming easier and safer by using cloud-based Internet of Things (IoT) as well as cloud computing provides so many useful services. But there are somelimitations of cloud computing like high latency and error in a large amount of data transmission, which cannot be tolerated in many healthcare emergencies cases because delay and inaccurate data or results may affect crucially on human life and a wrong decision can be made. The innovation of Fog Computing has solved the problem of high latency and bandwidth, by creating a Fog layer between the end-user and cloud computing. In today’s scenario, Heart disease is a major cause of death and many heart problems happen slowly over time. Fog-cloud computing-based IoT devices are so useful to diagnose heart problems in the early stage and if found any abnormality in the result also notify the end-user and immediate treatment can be given to the patient. Here you find Here Healthfog is a new concept coupled with deep learning sets in leading devices and automatically structured for use in real-time heart disease analysis. It relies on Fog services in IoT devices and handles the data of cardiac patients efficiently. With this model, FogBus is used to set up and measure the latency, bandwidth, accuracy and power consumption efficiency of this model. This model provides the best Quality of Service (QoS).
C. H. Wu, Cathy H. Y. Lam, Fatos Xhafa, Valerie Tang, W. H. Ip
Published: 1 January 2022
The publisher has not yet granted permission to display this abstract.
S. V. K. R. Rajeswari, Vijayakumar Ponnusamy
Published: 1 January 2022
Abstract:
It is very evident by looking at the current technological advancements that the interrelation and association of artificial intelligence (AI) and IoT in the Cloud have transformed the way healthcare has been working. AI and Cloud-empowered IoT boosts operational efficiency enhanced risk management. This combination creates products and services by enhancing the existing products while increasing scalability. To reduce costs, data analytics on the Cloud is much preferred in the current formation of technologies. This chapter focuses on the integration of different AI techniques in Cloud datasets for IoT data analytics. Analyzing, predicting, and making decisions by comparing the current data with historical data. The theory of AI-based IoT analytics will be much investigated with a healthcare application. Different approaches to implementing data analytics on the Cloud for a diabetic management system will be explored (human body). Finally, future trends and possible areas of research are also discussed.
Fen Li, Achyut Shankar, B. Santhosh Kumar
Published: 1 November 2021
Technology and Health Care, Volume 29, pp 1319-1337; https://doi.org/10.3233/thc-213009

Abstract:
BACKGROUND: Internet of Things (IoT) technology provides a tremendous and structured solution to tackle service deliverance aspects of healthcare in terms of mobile health and remote patient tracking. In medicine observation applications, IoT and cloud computing serves as an assistant in the health sector and plays an incredibly significant role. Health professionals and technicians have built an excellent platform for people with various illnesses, leveraging principles of wearable technology, wireless channels, and other remote devices for low-cost healthcare monitoring. OBJECTIVE: This paper proposed the Fog-IoT-assisted multisensor intelligent monitoring model (FIoT-MIMM) for analyzing the patient’s physical health condition. METHOD: The proposed system uses a multisensor device for collecting biometric and medical observing data. The main point is to continually generate emergency alerts on mobile phones from the fog system to users. For the precautionary steps and suggestions for patients’ health, a fog layer’s temporal information is used. RESULTS: Experimental findings show that the proposed FIoT-MIMM model has less response time and high accuracy in determining a patient’s condition than other existing methods. Furthermore, decision making based on real-time healthcare information further improves the utility of the suggested model.
, , Qingqing Gan, Mengting Yao
Published: 29 October 2021
Journal of Information Security and Applications, Volume 63; https://doi.org/10.1016/j.jisa.2021.103022

The publisher has not yet granted permission to display this abstract.
Published: 14 October 2021
by MDPI
Journal: Electronics
Abstract:
Internet of Things (IoT) communication technologies have brought immense revolutions in various domains, especially in health monitoring systems. Machine learning techniques coupled with advanced artificial intelligence techniques detect patterns associated with diseases and health conditions. Presently, the scientific community is focused on enhancing IoT-enabled applications by integrating blockchain technology with machine learning models to benefit medical report management, drug traceability, tracking infectious diseases, etc. To date, contemporary state-of-the-art techniques have presented various efforts on the adaptability of blockchain and machine learning in IoT applications; however, there exist various essential aspects that must also be incorporated to achieve more robust performance. This study presents a comprehensive survey of emerging IoT technologies, machine learning, and blockchain for healthcare applications. The reviewed articles comprise a plethora of research articles published in the web of science. The analysis is focused on research articles related to keywords such as ‘machine learning’, blockchain, ‘Internet of Things or IoT’, and keywords conjoined with ‘healthcare’ and ‘health application’ in six famous publisher databases, namely IEEEXplore, Nature, ScienceDirect, MDPI, SpringerLink, and Google Scholar. We selected and reviewed 263 articles in total. The topical survey of the contemporary IoT-based models is presented in healthcare domains in three steps. Firstly, a detailed analysis of healthcare applications of IoT, blockchain, and machine learning demonstrates the importance of the discussed fields. Secondly, the adaptation mechanism of machine learning and blockchain in IoT for healthcare applications are discussed to delineate the scope of the mentioned techniques in IoT domains. Finally, the challenges and issues of healthcare applications based on machine learning, blockchain, and IoT are discussed. The presented future directions in this domain can significantly help the scholarly community determine research gaps to address.
Venki Balasubramanian, Rehena Sulthana, Andrew Stranieri, G Manoharan, Teena Arora, Ram Srinivasan, K Mahalakshmi, Varun G Menon
Abstract:
Internet of Medical Things (IoMT) is an emerging technology whose capabilities to self-organize itself on-the-fly, to monitor the patient's vital health data without any manual entry and assist early human intervention gave birth to smart healthcare applications. The smart applications can be used to remotely monitor isolated patients during this COVID-19 pandemic. Remote patient monitoring provides an opportunity for COVID-19 patients to have vital signs and other indicators recorded regularly and inexpensively to provide rapid and early warning of conditions that require medical attention using secured edge and cloud computing. However, to gain the confidence of the users over these applications, the performance of healthcare applications should be evaluated in real-time. Our real-time implementation of IoMT based remote monitoring application using edge and cloud computing, along with empirical evaluation, show that COVID-19 patients can be monitored effectively not only with mobility but also helps the health care professionals to generate consolidated health data of the patient that can guide them to obtain medical attention.
IEEE Internet of Things Journal, Volume 9, pp 3631-3641; https://doi.org/10.1109/jiot.2021.3098158

Abstract:
The pandemic/epidemic of COVID-19 has affected people worldwide. A huge number of lives succumbed to death due to the sudden outbreak of this corona virus infection. The specified symptoms of COVID-19 detection are very common like as normal flu; asymptomatic version of COVID-19 has become a critical issue. Therefore, as a precautionary measurement oxygen level needs to be monitored by every individual if no other critical condition is found. It is not the only parameter for COVID-19 detection but, as per the suggestions by different medical organizations such as WHO it is better to use oximeter to monitor the oxygen level in probable patients as a precaution. People are using the oximeters personally; however, not having any clue or guidance regarding the measurements obtained. Therefore, in this paper, we have shown a framework of oxygen level monitoring and severity calculation and probabilistic decision of being a COVID-19 patient. This framework is also able to maintain the privacy of patient information and uses probabilistic classification to measure the severity. Results are measured based on latency of blockchain creation and overall response, throughput, detection and severity accuracy. The analysis finds the solution efficient and significant in the IoT framework for the present health hazard in our world.
Acta Universitatis Sapientiae, Informatica, Volume 13, pp 180-194; https://doi.org/10.2478/ausi-2021-0008

Abstract:
The integration of Wireless Sensor Networks (WSN) and cloud computing brings several advantages. However, one of the main problems with the existing cloud solutions is the latency involved in accessing, storing, and processing data. This limits the use of cloud computing for various types of applications (for instance, patient health monitoring) that require real-time access and processing of data. To address the latency problem, we proposed a fog-assisted Link Aware and Energy E cient Protocol for Wireless Body Area Networks (Fog-LAEEBA). The proposed solution is based on the already developed state-of-the-art protocol called LAEEBA. We implement, test, evaluate and compare the results of Fog-LAEEBA in terms of stability period, end-to-end delay, throughput, residual energy, and path-loss. For the stability period all nodes in the LAEEBA protocol die after 7445 rounds, while in our case the last node dies after 9000 rounds. For the same number of rounds, the end-to-end delay is 2 seconds for LAEEBA and 1.25 seconds for Fog-LAEEBA. In terms of throughput, our proposed solution increases the number of packets received by the sink node from 1.5 packets to 1.8 packets. The residual energy of the nodes in Fog-LAEEBA is also less than the LAEEBA protocol. Finally, our proposed solution improves the path loss by 24 percent.
IEEE Internet of Things Journal, Volume 8, pp 17817-17828; https://doi.org/10.1109/jiot.2021.3081556

Abstract:
With the recent advances of the Internet of Things (IoT), and the increasing accessibility to ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as social relationships explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as artificial social intelligence (ASI) that has the potential to tackle the social relationships explosion problem. This article discusses the role of IoT in social relationships management, the problem of social relationships explosion in IoT, and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.
, I. De León-Damas, Jiri Bila, Jiří Škvor
Published: 23 March 2021
The publisher has not yet granted permission to display this abstract.
Mara Nikolaidou, Christos Kotronis, , Elena Politi, George Dimitrakopoulos, Dimosthenis Anagnostopoulos, Hamza Djelouat, Abbes Amira, Faycal Bensaali
Published: 1 January 2021
Health informatics journal, Volume 27; https://doi.org/10.1177/1460458220982640

Abstract:
Internet of Medical Things (IoMT) systems are envisioned to provide high-quality healthcare services to patients in the comfort of their home, utilizing cutting-edge Internet of Things (IoT) technologies and medical sensors. Patient comfort and willingness to participate in such efforts is a prominent factor for their adoption. As IoT technology has provided solutions for all technical issues, patient concerns are those that seem to restrict their wider adoption. To enhance patient awareness of the system properties and enhance their willingness to adopt IoMT solutions, this paper presents a novel methodology to integrate patient concerns in the design requirements of such systems. It comprises a number of straightforward steps that an IoMT designer can follow, starting from identifying patient concerns, incorporating them in system design requirements as criticalities, proceeding to system implementation and testing, and finally, verifying that it fulfills the concerns of the patients. To showcase the effectiveness of the proposed methodology, the paper applies it in the design and implementation of a fall detection system for elderly patients remotely monitored in their homes.
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