IOT based wearable sensor for diseases prediction and symptom analysis in healthcare sector

Muthu, BalaAnand; Sivaparthipan, C. B.; Manogaran, Gunasekaran; Sundarasekar, Revathi; Kadry, Seifedine; Shanthini, A.; Dasel, A. Antony

doi:10.1007/s12083-019-00823-2

Cited by 186 publications

(52 citation statements)

References 26 publications

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“…The healthcare platform is visualized using electronic health records (EHRs) in its digital and technical format, providing unrestricted access to the end users. Diagnosis centers and healthcare infrastructures provide different access and data sharing processes for their users through EHRs [2][3][4]. EHR is an organized set of patient-/user-related information that is digitally shared through a secure platform for ubiquitous access [5].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Privacy and Data Security across Healthcare Applications Using Blockchain and Distributed Ledger Concepts

2020

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Nowadays, blockchain is developing as a secure and trustworthy platform for secure information sharing in areas of application like banking, supply chain management, food industry, energy, the Internet, and medical services. Besides, the blockchain can be described in a decentralized manner as an immutable ledger for recording data entries. Furthermore, this new technology has been developed to interrupt a variety of data-driven fields, including the health sector. However, blockchain refers to the distributed ledger technology, which constitutes an innovation in the information recording and sharing without a trusted third party. In this paper, blockchain and Distributed Ledger-based Improved Biomedical Security system (BDL-IBS) has been proposed to enhance the privacy and data security across healthcare applications. Further, our goal is to make it possible for patients to use the data to support their care and to provide strong consent systems for sharing data among different organizations and applications, since this includes managing and accessing a high amount of medical information, and this technology can maintain data to ensure reliability. Finally, results show that new blockchain-based digital platforms allow for fast, easy, and seamless interactions between data suppliers to enhance privacy and data security, including for patients themselves.

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Section: Introductionmentioning

confidence: 99%

Enhancing Privacy and Data Security across Healthcare Applications Using Blockchain and Distributed Ledger Concepts

2020

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“…Remote health monitoring is also an interesting perspective that could be utilized with the proper support of IoT devices and products. The prediction of different symptoms and prevention of potentially life hazardous states and diseases could generally be enabled, ( Muthu et al., 2020 ). Assistance to the elderly could also be ensured by monitoring a patient’s general health condition and nutrition status ( Nivetha et al., 2020 ), that would be supported via IoT devices.…”

Section: Introductionmentioning

confidence: 99%

Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future

Nižetić

Šolić

López-de-Ipiña

et al. 2020

Journal of Cleaner Production

652

165

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The rapid development and implementation of smart and IoT (Internet of Things) based technologies have allowed for various possibilities in technological advancements for different aspects of life. The main goal of IoT technologies is to simplify processes in different fields, to ensure a better efficiency of systems (technologies or specific processes) and finally to improve life quality. Sustainability has become a key issue for population where the dynamic development of IoT technologies is bringing different useful benefits, but this fast development must be carefully monitored and evaluated from an environmental point of view to limit the presence of harmful impacts and ensure the smart utilization of limited global resources. Significant research efforts are needed in the previous sense to carefully investigate the pros and cons of IoT technologies. This review editorial is partially directed on the research contributions presented at the 4th International Conference on Smart and Sustainable Technologies held in Split and Bol, Croatia, in 2019 (SpliTech 2019) as well as on recent findings from literature. The SpliTech2019 conference was a valuable event that successfully linked different engineering professions, industrial experts and finally researchers from academia. The focus of the conference was directed towards key conference tracks such as Smart City, Energy/Environment, e-Health and Engineering Modelling. The research presented and discussed at the SpliTech2019 conference helped to understand the complex and intertwined effects of IoT technologies on societies and their potential effects on sustainability in general. Various application areas of IoT technologies were discussed as well as the progress made. Four main topical areas were discussed in the herein editorial, i.e. latest advancements in the further fields: (i) IoT technologies in Sustainable Energy and Environment, (ii) IoT enabled Smart City, (iii) E-health – Ambient assisted living systems (iv) IoT technologies in Transportation and Low Carbon Products. The main outcomes of the review introductory article contributed to the better understanding of current technological progress in IoT application areas as well as the environmental implications linked with the increased application of IoT products.

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“…Recently, deep learning shows huge success in the medical domain (Anjum et al, 2020). It is used for many imaging (Khan et al, 2020a(Khan et al, , 2020bFernandes, Rajinikanth & Kadry, 2019), disease recognition (Sahlol et al, 2020a(Sahlol et al, , 2020bCapizzi et al, 2020), analysis of biomedical signals (Bakiya et al, 2020), Internet-of-Things domain (Huifeng, Kadry & Raj, 2020;Muthu et al, 2020) and epidemic disease spread forecasting tasks.…”

Section: Introductionmentioning

confidence: 99%

From ECG signals to images: a transformation based approach for deep learning

Naz

Shah

Khan

et al. 2021

PeerJ Computer Science

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Provocative heart disease is related to ventricular arrhythmias (VA). Ventricular tachyarrhythmia is an irregular and fast heart rhythm that emerges from inappropriate electrical impulses in the ventricles of the heart. Different types of arrhythmias are associated with different patterns, which can be identified. An electrocardiogram (ECG) is the major analytical tool used to interpret and record ECG signals. ECG signals are nonlinear and difficult to interpret and analyze. We propose a new deep learning approach for the detection of VA. Initially, the ECG signals are transformed into images that have not been done before. Later, these images are normalized and utilized to train the AlexNet, VGG-16 and Inception-v3 deep learning models. Transfer learning is performed to train a model and extract the deep features from different output layers. After that, the features are fused by a concatenation approach, and the best features are selected using a heuristic entropy calculation approach. Finally, supervised learning classifiers are utilized for final feature classification. The results are evaluated on the MIT-BIH dataset and achieved an accuracy of 97.6% (using Cubic Support Vector Machine as a final stage classifier).

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IOT based wearable sensor for diseases prediction and symptom analysis in healthcare sector

Cited by 186 publications

References 26 publications

Enhancing Privacy and Data Security across Healthcare Applications Using Blockchain and Distributed Ledger Concepts

Enhancing Privacy and Data Security across Healthcare Applications Using Blockchain and Distributed Ledger Concepts

Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future

From ECG signals to images: a transformation based approach for deep learning

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