mlsanityData to 7 Oct 2026

Fitbeat: COVID-19 Estimation based on Wristband Heart Rate

arXiv 2104.09263 ↗First posted 19 Apr 2021eess.SP cs.HC cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

This study investigates the potential of deep learning methods to identify individuals with suspected COVID-19 infection using remotely collected heart-rate data. The study utilises data from the ongoing EU IMI RADAR-CNS research project that is investigating the feasibility of wearable devices and smart phones to monitor individuals with multiple sclerosis (MS), depression or epilepsy. Aspart of the project protocol, heart-rate data was collected from participants using a Fitbit wristband. The presence of COVID-19 in the cohort in this work was either confirmed through a positive swab test, or inferred through the self-reporting of a combination of symptoms including fever, respiratory symptoms, loss of smell or taste, tiredness and gastrointestinal symptoms. Experimental results indicate that our proposed contrastive convolutional auto-encoder (contrastive CAE), i. e., a combined architecture of an auto-encoder and contrastive loss, outperforms a conventional convolutional neural network (CNN), as well as a convolutional auto-encoder (CAE) without using contrastive loss. Our final contrastive CAE achieves 95.3% unweighted average recall, 86.4% precision, anF1 measure of 88.2%, a sensitivity of 100% and a specificity of 90.6% on a testset of 19 participants with MS who reported symptoms of COVID-19. Each of these participants was paired with a participant with MS with no COVID-19 symptoms.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 63 references that are in the map. First posted on arXiv 19 Apr 2021.

Builds on

19 of 63 references.

References · 19

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  3. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  4. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  5. 2020Supervised Contrastive Learningcited by 2,803
  6. 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,348
  7. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  8. 2018Deep learning for time series classification: a reviewcited by 575
  9. 2018Object Detection with Deep Learning: A Reviewcited by 524
  10. 2018Understanding Batch Normalizationcited by 157
  11. 2016Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Unitscited by 86
  12. 2020Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19cited by 62
7 more
  1. 2017Deep Learning for IoT Big Data and Streaming Analytics: A Surveycited by 59
  2. 2020Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Imagescited by 43
  3. 2018Deep Convolutional AutoEncoder-based Lossy Image Compressioncited by 42
  4. 2018Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representationscited by 17
  5. 2020COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisiscited by 13
  6. 2020An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxietycited by 11
  7. 2017Fast and Scalable Distributed Deep Convolutional Autoencoder for fMRI Big Data Analyticscited by 1

Built on by

Not cited yet.