ArticlePattern recognition2022
Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder.
Article in Pattern recognition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- Article
- Multi-branch LSTM encoded latent features with CNN-LSTM for Youtube popularity prediction.Scientific reports · 2025Article
- Information theory reveals physiological manifestations of COVID-19 that correlate with symptom density of illness.Frontiers in network physiology · 2024Article
- Focused review on artificial intelligence for disease detection in infants.Frontiers in digital health · 2024Review
- The Importance of Data Quality Control in Using Fitbit Device Data From the Research Program.JMIR mHealth and uHealth · 2023Article
- Enabling Multicentric Participatory Disease Surveillance for Global Health Enhancement: Viewpoint on Global Flu View.JMIR public health and surveillance · 2023Article
- Advancements in SARS-CoV-2 Testing: Enhancing Accessibility through Machine Learning-Enhanced Biosensors.Micromachines · 2023Review
- Opportune warning of COVID-19 in a Mexican health care worker cohort: Discrete beta distribution entropy of smartwatch physiological records.Biomedical signal processing and control · 2023Article
- PCovNet+: A CNN-VAE anomaly detection framework with LSTM embeddings for smartwatch-based COVID-19 detection.Engineering applications of artificial intelligence · 2023Article
- Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study.Journal of medical Internet research · 2023Article
- CovidRhythm: A Deep Learning Model for Passive Prediction of Covid-19 Using Biobehavioral Rhythms Derived From Wearable Physiological Data.IEEE open journal of engineering in medicine and biology · 2023Article
- Feasibility and scalability of a fitness tracker study: Results from a longitudinal analysis of persons with multiple sclerosis.Frontiers in digital health · 2023Article
- Audio self-supervised learning: A survey.Patterns (New York, N.Y.) · 2022Review
- Biopsychosocial Response to the COVID-19 Lockdown in People with Major Depressive Disorder and Multiple Sclerosis.Journal of clinical medicine · 2022Article
- PCovNet: A presymptomatic COVID-19 detection framework using deep learning model using wearables data.Computers in biology and medicine · 2022Article
- Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression.Cell reports. Medicine · 2022Article
Corrections and comments
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Authors and funding
30 authors.
Funding
Abstract
This study proposes a contrastive convolutional auto-encoder (contrastive CAE), a combined architecture of an auto-encoder and contrastive loss, to identify individuals with suspected COVID-19 infection using heart-rate data from participants with multiple sclerosis (MS) in the ongoing RADAR-CNS mHealth research project. Heart-rate data was remotely collected using a Fitbit wristband. COVID-19 infection was either confirmed through a positive swab test, or inferred through a self-reported set of recognised symptoms of the virus. The contrastive CAE outperforms a conventional convolutional neural network (CNN), a long short-term memory (LSTM) model, and a convolutional auto-encoder without contrastive loss (CAE). On a test set of 19 participants with MS with reported symptoms of COVID-19, each one paired with a participant with MS with no COVID-19 symptoms, the contrastive CAE achieves an unweighted average recall of
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.