Health Monitoring Based on Internet of Medical Things: Architecture, Enabling Technologies, and Applications

Wei, Kefeng; Zhang, Lincong; Guo, Yi; Jiang, Xin

doi:10.1109/access.2020.2971654

Cited by 82 publications

(36 citation statements)

References 113 publications

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“…The traditional medical ecosystem generally involves patient, doctor, medication (pharmacist), and treatment. In addition to these, IoMT medical ecosystem includes cloud data, applications (online, mobile, real-time, and non-real-time), wearable sensor devices, and security systems ( Wei et al., 2020 ; Yaqoob et al., 2019 ). Fig.…”

Section: The Iomt Ecosystemmentioning

confidence: 99%

IoMT amid COVID-19 pandemic: Application, architecture, technology, and security

Aman

Hassan

Sameen

et al. 2021

Journal of Network and Computer Applications

146

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In many countries, the Internet of Medical Things (IoMT) has been deployed in tandem with other strategies to curb the spread of COVID-19, improve the safety of front-line personnel, increase efficacy by lessening the severity of the disease on human lives, and decrease mortality rates. Significant inroads have been achieved in terms of applications and technology, as well as security which have also been magnified through the rapid and widespread adoption of IoMT across the globe. A number of on-going researches show the adoption of secure IoMT applications is possible by incorporating security measures with the technology. Furthermore, the development of new IoMT technologies merge with Artificial Intelligence, Big Data and Blockchain offers more viable solutions. Hence, this paper highlights the IoMT architecture, applications, technologies, and security developments that have been made with respect to IoMT in combating COVID-19. Additionally, this paper provides useful insights into specific IoMT architecture models, emerging IoMT applications, IoMT security measurements, and technology direction that apply to many IoMT systems within the medical environment to combat COVID-19.

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Section: The Iomt Ecosystemmentioning

confidence: 99%

IoMT amid COVID-19 pandemic: Application, architecture, technology, and security

Aman

Hassan

Sameen

et al. 2021

Journal of Network and Computer Applications

146

View full text Add to dashboard Cite

show abstract

“…The normalization process takes place through multiple data distributions and regression analysis for heart disease prediction. Equation 1 (2) The data samples in equation (2) with the equal variance and error are expressed as ∈ . The least squares values are represented by 0 and respectively.…”

Section: B) Data Normalizationmentioning

confidence: 99%

“…The pervasive development of the Internet of Things (IoT) and its use in medical research has improved the effectiveness of remote health monitoring systems [1]. The IoT is an interconnection of various physical objects to continuously observe physical events [2,3,4]. Using IoT technology, a heart disease monitoring system can capture and transmit the patient's physical parameters to a remote healthcare facility center in real time [5].…”

Section: Introductionmentioning

confidence: 99%

A Healthcare Monitoring System for the Diagnosis of Heart Disease in the IoMT Cloud Environment Using MSSO-ANFIS

2020

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The IoT has applications in many areas such as manufacturing, healthcare, and agriculture, to name a few. Recently, wearable devices have become popular with wide applications in the health monitoring system which has stimulated the growth of the Internet of Medical Things (IoMT). The IoMT has an important role to play in reducing the mortality rate by the early detection of disease. The prediction of heart disease is a key issue in the analysis of clinical dataset. The aim of the proposed investigation is to identify the key characteristics of heart disease prediction using machine learning techniques. Many studies have focused on heart disease diagnosis, but the accuracy of the findings is low. Therefore, to improve prediction accuracy, an IoMT framework for the diagnosis of heart disease using modified salp swarm optimization (MSSO) and an adaptive neuro-fuzzy inference system (ANFIS) is proposed. The proposed MSSO-ANFIS improves the search capability using the Levy flight algorithm. The regular learning process in ANFIS is dependent on gradient-based learning and has a tendency to become trapped in local minima. The learning parameters are optimized utilizing MSSO to provide better results for ANFIS. The following information is taken from medical records to predict the risk of heart disease: blood pressure (BP), age, sex, chest pain, cholesterol, blood sugar, etc. The heart condition is identified by classifying the received sensor data using MSSO-ANFIS. A simulation and analysis is conducted to show that MSSA-ANFIS works well in relation to disease prediction. The results of the simulation demonstrate that the MSSO-ANFIS prediction model achieves better accuracy than the other approaches. The proposed MSSO-ANFIS prediction model obtains an accuracy of 99.45 with a precision of 96.54, which is higher than the other approaches.

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“…Recently, WSN are also being used to support monitoring applications in health, agriculture, industrial and smart cities domains [ 1 ]. Regarding the health domain, also called Internet of Medical Things (IoMT), sensor monitoring applications are proposed for a wide range of applications, but they bring many challenges concerning reliable and timely delivery of critical signals [ 29 ]. For example, heart rate data have stringent time constraints, and one of the major challenges in this context is to guarantee the quality of service for the data transfer of from several sensors that have different priorities, where a delay could have serious consequences for patients [ 30 ].…”

Section: System and Network Modelmentioning

confidence: 99%

“…This last layer is formed by network gateways and cloud services. It is important to highlight that more recent works, e.g., [ 29 ], also define similar architectures for individual and group health monitoring systems, where one of the many issues is to guarantee the message transmission from MNs to the gateway.…”

Section: System and Network Modelmentioning

confidence: 99%

A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks

Casagrande

Moraes

Montez

et al. 2020

Sensors

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Node mobility in multi-hop communication environments is an important feature of Wireless Sensor Network (WSN)-based monitoring systems. It allows nodes to have freedom of movement, without being restricted to a single-hop communication range. In IEEE 802.15.4 WSNs, nodes are only able to transfer data messages after completing a connection with a coordinator through an association mechanism. Within this context, a handover procedure needs to be executed by a mobile node whenever there is a disconnection from a coordinator and the establishment of a connection to another one. Many applications, such as those found in health monitoring systems, strongly need support for node mobility without loss of data during the handover. However, it has been observed that the time required to execute the handover procedure is one of the main reasons why IEEE 802.15.4 cannot fully support mobility. This paper proposes an improvement to this procedure using a set of combined strategies, such as anticipation of both the handover mechanism and the scan phase enhancement. Simulations show that it is possible to reduce latency during the association and re-association processes, making it feasible to develop WSN-based distributed monitoring systems with mobile nodes and stringent time constraints.

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Health Monitoring Based on Internet of Medical Things: Architecture, Enabling Technologies, and Applications

Cited by 82 publications

References 113 publications

IoMT amid COVID-19 pandemic: Application, architecture, technology, and security

IoMT amid COVID-19 pandemic: Application, architecture, technology, and security

A Healthcare Monitoring System for the Diagnosis of Heart Disease in the IoMT Cloud Environment Using MSSO-ANFIS

A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks

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