IoT-based telemedicine for disease prevention and health promotion: State-of-the-Art

Albahri, A. S.; Alwan, Jwan K.; Taha, Zahraa K.; Ismail, Sura F.; Hamid, Rula A.; Zaidan, A. A.; Albahri, O. S.; Zaidan, B. B.; Alsalem, M. A.

doi:10.1016/j.jnca.2020.102873

Cited by 190 publications

(92 citation statements)

References 94 publications

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“…Besides the therapeutical applications, telemedicine is also employed for remote monitoring of patients. The objective of this passive branch of telemedicine is to provide a warning to clinicians or doctors when the neurophysiological and physiological data collected from the patients indicate an adverse clinical event [ 16 , 17 ]. Several studies have already demonstrated the effectiveness of telemedicine in improving patients’ outcomes [ 18 , 19 ], while other studies have showed its benefits in terms of hospitalization reduction, a crucial aspect especially during a severe pandemic such as the COVID-19 one [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

A Video-Based Technique for Heart Rate and Eye Blinks Rate Estimation: A Potential Solution for Telemonitoring and Remote Healthcare

Ronca

Giorgi²,

Rossi

et al. 2021

Sensors

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Current telemedicine and remote healthcare applications foresee different interactions between the doctor and the patient relying on the use of commercial and medical wearable sensors and internet-based video conferencing platforms. Nevertheless, the existing applications necessarily require a contact between the patient and sensors for an objective evaluation of the patient’s state. The proposed study explored an innovative video-based solution for monitoring neurophysiological parameters of potential patients and assessing their mental state. In particular, we investigated the possibility to estimate the heart rate (HR) and eye blinks rate (EBR) of participants while performing laboratory tasks by mean of facial—video analysis. The objectives of the study were focused on: (i) assessing the effectiveness of the proposed technique in estimating the HR and EBR by comparing them with laboratory sensor-based measures and (ii) assessing the capability of the video—based technique in discriminating between the participant’s resting state (Nominal condition) and their active state (Non-nominal condition). The results demonstrated that the HR and EBR estimated through the facial—video technique or the laboratory equipment did not statistically differ (p > 0.1), and that these neurophysiological parameters allowed to discriminate between the Nominal and Non-nominal states (p < 0.02).

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Section: Introductionmentioning

confidence: 99%

A Video-Based Technique for Heart Rate and Eye Blinks Rate Estimation: A Potential Solution for Telemonitoring and Remote Healthcare

Ronca

Giorgi²,

Rossi

et al. 2021

Sensors

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“…In an emergency, telemedicine services help in saving lives specifically in an emergency situation by a timely diagnosis, providing a patient useful information related to his disease and saving the patient's life before a patient faces serious health condition. [ 5 , 6 ]. The services of Telemedicine are beneficial for the people of rural areas where no hospital, highly equipped laboratories, or medical stores are available [ 7 ].…”

Section: Overview and Architecturementioning

confidence: 99%

Integration of 5G and Block-Chain Technologies in Smart Telemedicine Using IoT

Hameed

Bajwa

Sarwar

et al. 2021

Journal of Healthcare Engineering

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The Internet of Health Thing (IoHT) has various applications in healthcare. Modern IoHTintegrates health-related things like sensors and remotely observed medical devices for the assessment and managment of a patient's record to provide smarter and efficient health diagnostics to the patient. In this paper, we proposed an IoT with a cloud-based clinical decision support system for prediction and observation of disease with its severity level with the integration of 5G services and block-chain technologies. A block-chain is a system for storing and sharing information that is secure because of its transparency. Block-chain has many applications in healthcare and can improve mobile health applications, monitoring devices, sharing and storing of the electronic media records, clinical trial data, and insurance information storage. The proposed framework will collect the data of patients through medical devices that will be attached to the patient, and these data will be stored in a cloud server with relevant medical records. Deployment of Block-chain and 5G technology allows for sending patient data securely at a fast transmission rate with efficient response time. Furthermore, a Neural Network (NN) classifier is used for the prediction of diseases and their severity level. The proposed model is validated by employing different classifiers. The performance of different classifiers is measured by comparing the values to select the classifier that is the best for the dataset. The NN classifier attains an accuracy of 98.98. Furthermore, the NN is trained for the dataset so that it can predict the result of the dataset class that is not labeled. The trained Neural Network predicts and intelligently shows the results with more accuracy than other classifiers.

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“…Hash of each block of secret data store in the ledger Fig. 12 Ledger for each node (hospital) in the network 6. If the hashes are similar, then the COVID-19 data content is not tampered.…”

Section: Secret Medical Covid-19 Data (Stego Images) Transmission Stagementioning

confidence: 99%

“…These contributions describe several of the major threats to individual and collective human health, as well as the values and recommendations that need consideration to counteract such threats in the future [ 83 ]. Sustainable health systems and social care need improvements to satisfy the current needs [ 3 , 6 , 11 – 13 , 25 , 42 , 49 , 50 , 54 , 80 , 85 , 88 , 114 , 115 , 119 ]. Considerable healthcare data for COVID-19 are generated daily from medical institutions, hospitals and individuals to enhance the understanding of the disease whilst considering environment and lifestyle when conducting disease treatment.…”

Section: Introductionmentioning

confidence: 99%

PSO–Blockchain-based image steganography: towards a new method to secure updating and sharing COVID-19 data in decentralised hospitals intelligence architecture

Mohsin

Zaidan

et al. 2021

Multimed Tools Appl

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Secure updating and sharing for large amounts of healthcare information (such as medical data on coronavirus disease 2019 ) in efficient and secure transmission are important but challenging in communication channels amongst hospitals. In particular, in addressing the above challenges, two issues are faced, namely, those related to confidentiality and integrity of their health data and to network failure that may cause concerns about data availability. To the authors' knowledge, no study provides secure updating and sharing solution for large amounts of healthcare information in communication channels amongst hospitals. Therefore, this study proposes and discusses a novel steganographybased blockchain method in the spatial domain as a solution. The novelty of the proposed method is the removal and addition of new particles in the particle swarm optimisation (PSO) algorithm. In addition, hash function can hide secret medical COVID-19 data in hospital databases whilst providing confidentiality with high embedding capacity and high image quality. Moreover, stego images with hash data and blockchain technology are used in updating and sharing medical COVID-19 data between hospitals in the network to improve the level of confidentiality and protect the integrity of medical COVID-19 data in grey-scale images, achieve data availability if any connection failure occurs in a single point of the network and eliminate the central point (third party) in the network during transmission. The proposed method is discussed in three stages. Firstly, the pre-hiding stage estimates the embedding capacity of each host image. Secondly, the secret COVID-19 data hiding stage uses PSO algorithm and hash function. Thirdly, the transmission stage transfers the stego images based on blockchain technology and updates all nodes (hospitals) in the network. As proof of concept for the case study, the authors adopted the latest COVID-19 research published in the Computer Methods and Programs in Biomedicine journal, which presents a rescue framework within hospitals for the storage and transfusion of the best convalescent plasma to the most critical patients with COVID-19 on the basis of biological requirements. The validation and evaluation of the proposed method are discussed.

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IoT-based telemedicine for disease prevention and health promotion: State-of-the-Art

Cited by 190 publications

References 94 publications

A Video-Based Technique for Heart Rate and Eye Blinks Rate Estimation: A Potential Solution for Telemonitoring and Remote Healthcare

A Video-Based Technique for Heart Rate and Eye Blinks Rate Estimation: A Potential Solution for Telemonitoring and Remote Healthcare

Integration of 5G and Block-Chain Technologies in Smart Telemedicine Using IoT

PSO–Blockchain-based image steganography: towards a new method to secure updating and sharing COVID-19 data in decentralised hospitals intelligence architecture

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