Towards smart health monitoring system for elderly people

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

doi:10.1109/icawst.2012.6469622

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…Multiprocessor systems are widely adopted by remote medical centres for various advanced healthcare related applications such as patient monitoring (63), drug administration, diagnostics (64; 65), human gait analysis (66), care assistance, and motoring (67). These services using MPSoC are helping to reduce the frequency of patient's visit to the hospitals while enhancing the Quality-of-Life (QoL).…”

Section: Healthcare and Automated Assistancementioning

confidence: 99%

A survey on system level energy optimisation for MPSoCs in IoT and consumer electronics

Ali

Tariq

Hardy

et al. 2021

Computer Science Review

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Section: Healthcare and Automated Assistancementioning

confidence: 99%

A survey on system level energy optimisation for MPSoCs in IoT and consumer electronics

Ali

Tariq

Hardy

et al. 2021

Computer Science Review

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

Ahmed

Kimezawa

Abdallah

2013

2013 World Congress on Computer and Information Technology (WCCIT)

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Towards smart health monitoring system for elderly people

Cited by 3 publications

References 6 publications

A survey on system level energy optimisation for MPSoCs in IoT and consumer electronics

A survey on system level energy optimisation for MPSoCs in IoT and consumer electronics

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

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

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