PCovNet+: A CNN-VAE anomaly detection framework with LSTM embeddings for smartwatch-based COVID-19 detection

The world is slowly recovering from the Coronavirus disease 2019 (COVID-19) pandemic; however, humanity has experienced one of its According to work by Mishra et al. (2020), the study’s first phase included a cohort of 5,262 subjects, with 3,325 Fitbit users constituting the majority. However, among...

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Veröffentlicht in:	Engineering applications of artificial intelligence Jg. 122; S. 106130
Hauptverfasser:	Abir, Farhan Fuad, Chowdhury, Muhammad E.H., Tapotee, Malisha Islam, Mushtak, Adam, Khandakar, Amith, Mahmud, Sakib, Hasan, Anwarul
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	England Elsevier Ltd 01.06.2023
Schlagworte:	Anomaly detection Convolutional neural network COVID-19 Long short-term memory Resting heart rate Variational autoencoder Wearables COVID-19 Resting heart rate Convolutional neural network Wearables Long short-term memory Variational autoencoder Anomaly detection
ISSN:	0952-1976, 1873-6769, 0952-1976
Online-Zugang:	Volltext
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