Medical Virtual Instrumentation for Personalized Health Monitoring: A Systematic Review

Adeluyi, Olufemi; Lee, Jeong-A

doi:10.1260/2040-2295.6.4.739

Cited by 3 publications

(5 citation statements)

References 41 publications

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“…Sallai [8], for example, identifies modeling as a separate branch in the IoT research. The state-of-the-art survey [2,9] shows intensive research within the smart healthcare systems field and focuses on healthcare frameworks, platforms, standards, and quality attributes. The authors' main conclusion is that the formal modeling and validation tools are enhancing the reliability and dependability properties in designing healthcare systems; however, these have not yet gained ground in real-world scenarios.…”

Section: Related Workmentioning

confidence: 99%

“…Adeluyi and Lee [2] give a systematic review of the key aspects of personalized health systems. They identified the main challenges for such systems: adaptation according to the profile of the patient; requirements of quality of service and reliability; issues of privacy, security, and authentication; mobility and low energy consumption; and integration with web services.…”

Section: Related Workmentioning

confidence: 99%

“…Technology advances have greatly extended capabilities in engineering of healthcare-related systems such as body area networks (BAN) [1], personalized health monitoring (PHM) [2], and personal health assistants (PHA) [3], to name a few. Multiple medical instrumentations are integrated within those systems and are able to ensure an interruptible remote monitoring of the patient's health through Internet-based communication.…”

Section: Introductionmentioning

confidence: 99%

“…This technological leap opens new possibilities in creating more advanced medical devices as well as medical applications. On the other hand, due to a growing proportion of senior citizens (e.g., in developed societies), there is an evident demand for the more advanced and personalized medical systems to suit user-specific needs [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Model-Driven Framework to Develop Personalized Health Monitoring

et al. 2016

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Both distributed healthcare systems and the Internet of Things (IoT) are currently hot topics. The latter is a new computing paradigm to enable advanced capabilities in engineering various applications, including those for healthcare. For such systems, the core social requirement is the privacy/security of the patient information along with the technical requirements (e.g., energy consumption) and capabilities for adaptability and personalization. Typically, the functionality of the systems is predefined by the patient's data collected using sensor networks along with medical instrumentation; then, the data is transferred through the Internet for treatment and decision-making. Therefore, systems creation is indeed challenging. In this paper, we propose a model-driven framework to develop the IoT-based prototype and its reference architecture for personalized health monitoring (PHM) applications. The framework contains a multi-layered structure with feature-based modeling and feature model transformations at the top and the application software generation at the bottom. We have validated the framework using available tools and developed an experimental PHM to test some aspects of the functionality of the reference architecture in real time. The main contribution of the paper is the development of the model-driven computational framework with emphasis on the synergistic effect of security and energy issues.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Model-Driven Framework to Develop Personalized Health Monitoring

et al. 2016

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“…Commercial systems, in most cases, the concept of VI is realized in an object-oriented programming language [1]. The modern scientific instrumentation boosts the introduction and development of systems based on the virtual instrumentation [[2], [3], [4], [5], [6], [7], [8]].…”

Section: Introductionmentioning

confidence: 99%

Development and implementation of virtual instrumentation based on LabView applied to compression ignition engines operated with diesel-biodiesel blends

et al. 2019

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A Computerized Bioinspired Methodology for Lightweight and Reliable Neural Telemetry

Adeluyi¹,

Castillo

Crespo

et al. 2020

Sensors

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Personalized health monitoring of neural signals usually results in a very large dataset, the processing and transmission of which require considerable energy, storage, and processing time. We present bioinspired electroceptive compressive sensing (BeCoS) as an approach for minimizing these penalties. It is a lightweight and reliable approach for the compression and transmission of neural signals inspired by active electroceptive sensing used by weakly electric fish. It uses a signature signal and a sensed pseudo-sparse differential signal to transmit and reconstruct the signals remotely. We have used EEG datasets to compare BeCoS with the block sparse Bayesian learning-bound optimization (BSBL-BO) technique—A popular compressive sensing technique used for low-energy wireless telemonitoring of EEG signals. We achieved average coherence, latency, compression ratio, and estimated per-epoch power values that were 35.38%, 62.85%, 53.26%, and 13 mW better than BSBL-BO, respectively, while structural similarity was only 6.295% worse. However, the original and reconstructed signals remain visually similar. BeCoS senses the signals as a derivative of a predefined signature signal resulting in a pseudo-sparse signal that significantly improves the efficiency of the monitoring process. The results show that BeCoS is a promising approach for the health monitoring of neural signals.

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Medical Virtual Instrumentation for Personalized Health Monitoring: A Systematic Review

Cited by 3 publications

References 41 publications

A Model-Driven Framework to Develop Personalized Health Monitoring

A Model-Driven Framework to Develop Personalized Health Monitoring

Development and implementation of virtual instrumentation based on LabView applied to compression ignition engines operated with diesel-biodiesel blends

A Computerized Bioinspired Methodology for Lightweight and Reliable Neural Telemetry

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