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

• Published in: Engineering applications of artificial intelligence Vol. 122; p. 106130
• Main Authors: Abir, Farhan Fuad, Chowdhury, Muhammad E.H., Tapotee, Malisha Islam, Mushtak, Adam, Khandakar, Amith, Mahmud, Sakib, Hasan, Anwarul
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.06.2023
• Subjects: 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

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...