Secure Health Data Sharing for Medical Cyber-Physical Systems for the Healthcare 4.0

Qiu, Han; Qiu, Meikang; Liu, Meiqin; Memmi, Gérard

doi:10.1109/jbhi.2020.2973467

Cited by 187 publications

(58 citation statements)

References 23 publications

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“…The authors show how their software layer makes several tasks simpler, such as the synchronization of central pattern generator controllers for actuators, the collection of sensory data and the construction of modular robot structures. In Qiu et al (2020) [15], the authors propose a method for securely storing and exchanging data that consists of a specific encryption algorithm along with fragmentation and dispersion for data safety and privacy protection, even when both transmission media and keys are compromised. Furthermore, Chong et al (2017) [16] describe a system consisting of an "image beacon" with the ability of broadcasting color images over a very long period using a set of devices with Bluetooth Low Energy (BLE) technology.…”

Section: -1-information Exchange Techniquesmentioning

confidence: 99%

A Comparative Study of Bluetooth SPP, PAN and GOEP for Efficient Exchange of Healthcare Data

2021

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Objectives: Current research aims to address the challenges of exchanging healthcare information, since when this information has to be shared, this happens by specifically designed medical applications or even by the patients themselves. Among the problems that the Health Information Exchange (HIE) initiative is facing are that (i) third party health data cannot be accessed without internet, (ii) there exist crucial delays in accessing citizens’ data, (iii) the direct HIE can only happen among Healthcare Institutions. Methods: Towards the solution of these issues, a Device-to-Device (D2D) protocol has been specified, running on top of the Bluetooth protocol for efficient data exchange. This research is focused on this D2D protocol, by comparing the different Bluetooth profiles that can be used for transmitting this data, based on specific metrics considering the probabilities of transferring erroneous data. Findings: An evaluation of three Bluetooth profiles takes place, concluding that two of the three profiles must be used to respect the D2D protocol nature and be fully supported by the main market vendors’ operating systems. Novelty:Based on this evaluation, the specified D2D protocol has been built on top of state-of-the-art short-range distance communication technologies, fully supporting the healthcare ecosystem towards the HIE paradigm. Doi: 10.28991/esj-2021-01276 Full Text: PDF

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Section: -1-information Exchange Techniquesmentioning

confidence: 99%

A Comparative Study of Bluetooth SPP, PAN and GOEP for Efficient Exchange of Healthcare Data

2021

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“…These help patients to take their medicine within a prescribed time frame and create an automatic reminder mechanism. These are also helpful for identifying patients at higher risk and enabling the dental staff for operative measures on a priority basis [ [11] , [12] , [13] ]. As a priority for high-risk patients, personalised care can play a huge role.…”

Section: Dentistry 40 For Healthcare In the Period Covid-19 Pandemicmentioning

confidence: 99%

Pedagogy and innovative care tenets in COVID-19 pandemic: An enhancive way through Dentistry 4.0

Javaid

Haleem

Singh

et al. 2021

Sensors International

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The global oral healthcare sector has now woken to implement Dentistry 4.0. The implementation of this revolution is feasible with extensive digital and advanced technologies applications and the adoption of new sets of processes in dentistry & its support areas. COVID-19 has bought new challenges to dental professionals and patients towards their customised requirements, regular dental health checkups, fast-paced and safe procedures. People are not visiting the dentist even for mild cases as they fear COVID-19 infection. We see that this set of technologies will help improve health education and treatment process and materials and minimise the infection. During the COVID-19 pandemic, there is a need to understand the possible impact of Dentistry 4.0 for education and innovative care. This paper discusses the significant benefits of Dentistry 4.0 technologies for the smart education platform and dentistry treatment. Finally, this article identifies twenty significant enhancements in dental education and effective care platforms during the COVID-19 pandemic by employing Dentistry 4.0 technologies. Thus, proper implementation of these technologies will improve the process efficiency in healthcare during the COVID-19 pandemic. Dentistry 4.0 technologies drive innovations to improve the quality of internet-connected healthcare devices. It creates automation and exchanges data to make a smart health care system. Therefore, helps better healthcare services, planning, monitoring, teaching, learning, treatment, and innovation capability. These technologies moved to smart transportation systems in the hospital during the COVID-19 Pandemic. Modern manufacturing technologies create digital transformation in manufacturing, optimises the operational processes and enhances productivity.

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“…Industry 4.0, represented by improvement of the intelligent level of the manufacturing industry, is profoundly converting all walks of life. Smart healthcare that adopts various Industry 4.0 concepts is an era full of opportunities and challenges [1,2]. As a whole, smart healthcare consists of three parts, including the smart hospital system, family health system, and regional health system [3].…”

Section: Introductionmentioning

confidence: 99%

Towards Effective Classification of aMCI Based on Resting‐State Multiscale Brain Features and Machine Learning Approaches

Cai

Yan

Zhou

et al. 2021

Wireless Communications and Mobile Computing

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Smart healthcare has undergone new opportunities and challenges with the arrival of the Industry 4.0 era. The intelligent imaging diagnosis system is a staple part of smart healthcare, helping doctors make clinical decisions. Nevertheless, intelligent diagnosis analysis is still confronted with the issue that it is challenging to extract effective features from the limited and high-dimensional data, particularly in resting-state data of amnesic mild cognitive impairment (aMCI). Furthermore, the intelligent imaging diagnosis system for aMCI is conductive to make timely predicting groups that may convert to Alzheimer’s disease (AD). To improve the system’s detection performance and reduce its data redundancy, we first develop an adaptive structure feature generation strategy (ASFGS) based on the Laplacian matrix and sparse autoencoder to obtain the structural features of brain functional network (BFN). Concurrently, we present a multiscale local feature detection strategy (MLFDS) to overcome the low utilization of local features of BFN. And finally, multiscale features, including structural features and multiscale local features, are fused by concatenation method to further improve the detection performance of aMCI system. Support vector machine based on radial basis function (RBF-SVM) for small data learning is adopted to evaluate the effectiveness of the proposed features. Besides, we employ leave-one-out cross-validation strategy to avoid the overfitting problem of classifier training process. The experiment results elucidate that the accuracy (ACC) and the area under the curve (AUC) in this work provide 86.57% and 86.36%, respectively, which outperforms the traditional methods and offers new insights for accuracy requirements of the aMCI system.

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Secure Health Data Sharing for Medical Cyber-Physical Systems for the Healthcare 4.0

Cited by 187 publications

References 23 publications

A Comparative Study of Bluetooth SPP, PAN and GOEP for Efficient Exchange of Healthcare Data

A Comparative Study of Bluetooth SPP, PAN and GOEP for Efficient Exchange of Healthcare Data

Pedagogy and innovative care tenets in COVID-19 pandemic: An enhancive way through Dentistry 4.0

Towards Effective Classification of aMCI Based on Resting‐State Multiscale Brain Features and Machine Learning Approaches

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