Hardware/software prototyping of dependable real-time system for elderly health monitoring

Ahmed, Achraf Ben; Kimezawa, Yumiko; Abdallah, Abderazek Ben

doi:10.1109/wccit.2013.6618696

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…It entails altering the methodology using which health and social care is planned and provided, with the end goal of improving patient experience, outcomes, and quality of care being provided. There is a compelling case for modifying the health and social care delivery paradigm to keep up with changing population demographics, requirements and models, which reflects the rise in multimorbidity and the complexity of care requirements (Ahmed et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Do We Really Need Elderly-Friendly Integrated Care Portals?

Gupta

2022

International Journal of Information Systems and Social Change

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Elderly-friendly integrated care portals are a relatively new phenomenon that could be a helpful addition to physical primary care, for example, by lowering costs and increasing access to healthcare. However, such platforms are primarily used by younger generations currently, resulting in a “digital divide” between younger and older generations. The objective of this research study is twofold - to identify and analyse: i) the perceived primary barriers that prevent the elderly from adopting and using these technologies, and ii) what can the providers do to support increasing adoption and implementation by the elderly population. It was observed that a lack of serious commitment across relevant sectors, inadequate resources, low managed information technology and financial coordination and care routes, opposing goals and objectives, and conflict within teams are all obstacles to successful adoption and subsequent implementation of integrated care portals.

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Section: Introductionmentioning

confidence: 99%

Do We Really Need Elderly-Friendly Integrated Care Portals?

Gupta

2022

International Journal of Information Systems and Social Change

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“…While the research in [3] raises doubts about the correctness of the evaluation method when learning rare and data-poor images such as COVID-19. Research work in [13,14] proposed an architecture and preliminary prototyping results of a dependable real-time system for health monitoring. We proposed an AI-enabled Real-time Biomedical System (AIRBiS) [4] based on a deep neural network to detect pneumonia and COVID-19 from X-ray images of langs in real-time.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Study of Coronavirus Disease 2019 (COVID-19) Classification based on Deep Convolution Neural Networks

et al. 2021

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Artificial Intelligence (AI) has recently become a topic of study in diﬀerent applications, including healthcare, in which timely detection of anomalies can play a vital role in patients health monitoring. The coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 virus, colloquially known as the Coronavirus, disrupts large parts of the world. The standard way to test for COVID-19 is Reverse Transcription Polymerase Chain Reaction (RT-PCR), which uses collected samples from the patient. This paper presents an eﬃcient convolution neural network software implementation for COVID-19 and other pneumonia disease detection targeted for an AI-enabled smart biomedical diagnosis system (AIRBiS). From the evaluation results, we found that the classification accuracy of the abnormal (COVID-19 and pneumonia) test dataset is over 97.18%. On the other hand, the accuracy of the normal is no more than 71.37%. We discussed the possible problems and proposals for further optimization.

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“…Efficient and accurate diagnosis of biomedical signals and images plays a significant role in health care systems [1,2]. In the conventional medical system, diagnosis is managed by doctors or experts.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Implementation of a Collaborative Learning Algorithm for an AI-Enabled Real-time Biomedical System

Phea

Wang

et al. 2021

SHS Web Conf.

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Recent years have witnessed a rapid growth of Artificial Intelligence (AI) in biomedical fields. However, an accurate and secure system for pneumonia detection and diagnosis is urgently needed. We present the optimization and implementation of a collaborative learning algorithm for an AI-Enabled Real-time Biomedical System (AIRBiS), where a convolution neural network is deployed for pneumonia (i.e., COVID-19) image classification. With augmentation optimization, the federated learning (FL) approach achieves a high accuracy of 95.66%, which outperforms the conventional learning approach with an accuracy of 94.08%. Using multiple edge devices also reduces overall training time.

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Hardware/software prototyping of dependable real-time system for elderly health monitoring

Cited by 4 publications

References 11 publications

Do We Really Need Elderly-Friendly Integrated Care Portals?

Do We Really Need Elderly-Friendly Integrated Care Portals?

Comprehensive Study of Coronavirus Disease 2019 (COVID-19) Classification based on Deep Convolution Neural Networks

Optimization and Implementation of a Collaborative Learning Algorithm for an AI-Enabled Real-time Biomedical System

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