Wireless Body Sensor Networks: Applications, Challenges, Patient Monitoring, Decision Making, and Machine Learning in Medical Applications

Jaber, Alaa Shawqi; Idrees, Ali Kadhum

doi:10.1007/978-3-030-90618-4_20

Cited by 7 publications

(2 citation statements)

References 84 publications

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“…A battery for uninterrupted data transmission and reception fuels each sensor component. Each sensor node battery has an initial energy capacity of one thousand joules [15][16][17][18][19][20].…”

Section: Proposed Methodologymentioning

confidence: 99%

A Deep learning approach for trust-untrust nodes classification problem in WBAN

Mehdy¹,

Qasim²,

A.Habeeb³

et al. 2023

PEN

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The enormous growth in demand for WBAN services has resulted in a new set of security challenges. The capabilities of WBAN are developing to meet these needs. The complexity, heterogeneity, and instability of the mobile context make it difficult to complete these duties successfully. A more secure and flexible WBAN setting can be attained using a trust-untrust nodes classification, which is one method to satisfy the security needs of the WBAN. Considering this, we present a novel Deep Learning (DL) approach for classifying WBAN nodes using spatial attention based iterative DBN (SA-IDBN). Z-score normalization is used to remove repetitive entries from the input data. Then, Linear Discriminate Analysis (LDA) is employed to retrieve the features from the normalized data. In terms of accuracy, latency, recall, and f-measure, the suggested method's performance is examined and contrasted with some other current approaches. Regarding the classification of WBAN nodes, the results are more favorable for the suggested method than for the ones already in use.

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Section: Proposed Methodologymentioning

confidence: 99%

A Deep learning approach for trust-untrust nodes classification problem in WBAN

Mehdy¹,

Qasim²,

A.Habeeb³

et al. 2023

PEN

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“…e framework is a powerful tool, and they expect it to have a significant impact on the spread of COVID-19 as well as a considerable reduction in healthcare costs [14]. WBSN and contemporary advances in the field were discussed in [15], which emphasized the need for remote monitoring for the elderly and chronically ill. e scientific notion of WBSN architecture, problems, healthcare applications, and their needs was conducted to meet the scientific idea of WBSN. Following that, the key critical part of the WBSN, such as data collection, fusion, risk assessment, and decisionmaking, is explored.…”

Section: Related Workmentioning

confidence: 99%

Remote Monitoring of COVID-19 Patients Using Multisensor Body Area Network Innovative System

Al_Barazanchi

Hashim

Alkahtani

et al. 2022

Computational Intelligence and Neuroscience

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As of late 2019, the COVID19 pandemic has been causing huge concern around the world. Such a pandemic posed serious threats to public safety, the well-being of healthcare workers, and the overall health of the population. If automation can be implemented in healthcare systems, patients could be better cared for and health industries could be less burdened. To combat such challenges, e-health requires apps and intelligent systems. Using WBAN sensors and networks, a doctor or medical professional can advise patients on the best course of action. Patients' fitness could be assessed using WBAN sensors without interfering with their daily activities. When designing a monitoring system, system performance reliability for competent healthcare is critical. Existing research has failed to create a large device capable of handling a large network or to improve WBAN topologies for fast transmitting and receiving patient data. As a result, in this research, we create a multisensor WBAN (MSWBAN) intelligent system for transmitting and receiving critical patient data. To gather information from all cluster nodes and send it to multisensor WBAN, a novel additive distance-threshold routing protocol (ADTRP) is proposed. In small networks where data are managed by the transmitting node and the best data route is determined, this protocol has less redundancy. An edge-cutting-based routing optimization (ES-EC-R ES-EC-RO) is used to find the best route. The Trouped blowfish MD5 (TB-MD5) algorithm is used to encrypt and decrypt data, and the encrypted data are stored in a cloud database for security. The performance metrics of our proposed model are compared to current techniques for the best results. End-to-end latency is 63 ms, packet delivery is 95%, security is 95.7%, and throughput is 9120 bps, according to the results. The purpose of this article is to encourage engineers and front-line workers to develop digital health systems for tracking and controlling virus outbreaks.

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