Towards Monitoring Patients with Alzheimer's Disease Activity using Distributed Tangible Tabletops and Dual Reality

Merrad, Walid; Héloir, Alexis; Kolski, Christophe; Mark, Gloria; Krüger, Antonio

doi:10.1109/cbms49503.2020.00034

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Different ML methods have been used to diagnose various diseases [6,10] and similar principle has been adopted for diagnosing COVID-19 from chest computed tomography (CT scan) images in [14]. On the other hand, audio signals have been successfully utilised to diagnose various respiratory conditions [12].…”

Section: Introductionmentioning

confidence: 99%

CovidEnvelope: A Fast Automated Approach to Diagnose COVID-19 from Cough Signals

Uddin

Ahmed

2021

Preprint

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The COVID-19 pandemic has a devastating impact on the health and well-being of global population. Cough audio signals classification showed potential as a screening approach for diagnosing people, infected with COVID-19. Recent approaches need costly deep learning algorithms or sophisticated methods to extract informative features from cough audio signals. In this paper, we propose a low-cost envelope approach, called CovidEnvelope, which can classify COVID-19 positive and negative cases from raw data by avoiding above disadvantages. This automated approach can pre-process cough audio signals by filter-out background noises, generate an envelope around the audio signal, and finally provide outcomes by computing area enclosed by the envelope. It has been seen that reliable datasets are also important for achieving high performance. Our approach proves that human verbal confirmation is not a reliable source of information. Finally, the approach reaches highest sensitivity, specificity, accuracy, and AUC of 0.92, 0.87, 0.89, and 0.89 respectively. The automatic approach only takes 1.8 to 3.9 minutes to compute these performances. Overall, this approach is fast and sensitive to diagnose the people living with COVID-19, regardless of having COVID-19 related symptoms or not, and thus have vast applicability in human well-being by designing HCI devices incorporating this approach.

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Section: Introductionmentioning

confidence: 99%

CovidEnvelope: A Fast Automated Approach to Diagnose COVID-19 from Cough Signals

Uddin

Ahmed

2021

Preprint

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CovidEnvelope: An Automated Fast Approach to Diagnose COVID-19 from Cough Signals

Hossain

Uddin²,

Yang

et al. 2021

2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)

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COVID-19 pandemic has a devastating impact on human health and well-being. Numerous biological tools have been utilised for COVID detection, but most of the tools are costly, time-extensive and need personnel with domain expertise. Thus, a cost-effective classifier can solve the problem where cough audio signals showed potentiality as an screening classifier for COVID-19 diagnosis. Recent ML approaches on coughbased covid-19 detection need costly deep learning algorithms or sophisticated methods to extract informative features. In this paper, we propose a low-cost and efficient envelope approach, called CovidEnvelope, which can classify COVID-19 positive and negative cases from raw data by avoiding above disadvantages. This automated approach can select correct audio signals (cough) from background noises, generate envelope around the informative audio signal, and finally provide outcomes by computing area enclosed by the envelope. It has been seen that reliable data-sets are also important for achieving high performance. Our approach proves that human verbal confirmation is not a reliable source of information. Finally, the approach reaches highest sensitivity, specificity, accuracy, and AUC of 0.96, 0.92, 0.94, and 0.94 respectively to detect Covid-19 coughs. Our approach outperformed other existing models on data pre-processing and inference times, and achieved accuracy and specificity of 0.91 and 0.99 respectively, to distinguish COVID-19 coughs from other coughs, resulted from respiratory diseases. The automatic approach only takes 1.8 to 3.9 minutes to compute these performances. Overall, our approach is fast and sensitive to diagnose the people living with COVID-19, regardless of having COVID-19 related symptoms or not. In this connection, the model can be implemented easily in mobile-devices or web-based applications, and countries with poor health facilities will be highly beneficiary for covid diagnosis and measuring prognostication.

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Smart Biomedical Assisted System for Alzheimer Patient

Abhishek,

C T

et al. 2023

2023 7th International Conference on Design Innovation for 3 Cs Compute Communicate Control (ICDI3C)

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Towards Monitoring Patients with Alzheimer's Disease Activity using Distributed Tangible Tabletops and Dual Reality

Cited by 3 publications

References 28 publications

CovidEnvelope: A Fast Automated Approach to Diagnose COVID-19 from Cough Signals

CovidEnvelope: A Fast Automated Approach to Diagnose COVID-19 from Cough Signals

CovidEnvelope: An Automated Fast Approach to Diagnose COVID-19 from Cough Signals

Smart Biomedical Assisted System for Alzheimer Patient

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