Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Pulse Oximetry

Pulse Oximetry

Pulse oximetry, or SpO2, is a non-invasive method for continuously monitoring arterial oxygen saturation (SaO2). This procedure involves attaching a probe or sensor to the patient's fingertip, forehead, earlobe, or nose bridge. The sensor works by detecting changes in oxygen saturation levels through light signals generated by the oximeter and reflected by the pulsing blood under the probe.
Purpose
Average SpO2 values are greater than 95%. If the readings fall below 90%, it indicates that...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Machine learning-enabled label-free SERS for microbial sensing: Toward robust, generalizable, and deployable workflows.

Talanta·2026

Same author

Machine learning algorithms in the estimation of sex from 3DCT-generated cranial and pelvic measurements.

International journal of legal medicine·2026

Same author

Machine Learning-Based Classification of Gliomas and Tumor Grades with SHAP-Guided Feature Interpretation.

Genes·2026

Same author

RoadSens-4M: A Multimodal Smartphone & Camera Dataset for Holistic Road-way Analysis.

Scientific data·2026

Same author

Free-Running EPI Motion Tracking in MRI via Gradient-Coupled Coil Signal.

IEEE transactions on bio-medical engineering·2026

Same author

Genomic characterization of Comamonas kerstersii isolated from diarrheal patients in Bangladesh.

PloS one·2026

Search research articles

Related Experiment Video

Updated: Jul 3, 2026

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

PCovNet: A presymptomatic COVID-19 detection framework using deep learning model using wearables data.

Farhan Fuad Abir¹, Khalid Alyafei², Muhammad E H Chowdhury³

¹Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, 1000, Bangladesh.

Computers in Biology and Medicine

|June 17, 2022

Summary

This summary is machine-generated.

Wearable devices and AI can detect COVID-19 before symptoms appear. PCovNet, an LSTM-VAE model, identified presymptomatic infections using resting heart rate data, aiding early detection.

Keywords:

Anomaly detection COVID-19 Long short-term memory Presymptomatic Resting heart rate Smartwatch Variational autoencoder

More Related Videos

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Published on: February 21, 2025

Related Experiment Videos

Last Updated: Jul 3, 2026

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies

Published on: February 3, 2023

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health

Published on: February 21, 2025

Area of Science:

Biomedical Engineering
Artificial Intelligence in Healthcare
Infectious Disease Monitoring

Background:

COVID-19 transmission is challenging to control due to presymptomatic spread.
Current diagnostic methods like RT-PCR are often used after symptom onset.
Resting Heart Rate (RHR) from wearables offers a potential continuous monitoring source.

Purpose of the Study:

To propose PCovNet, a deep learning framework for presymptomatic COVID-19 detection.
To utilize Resting Heart Rate (RHR) data from wearable devices for anomaly detection.
To evaluate the feasibility of a wearable-based secondary diagnostic tool for COVID-19.

Main Methods:

Developed a Long Short-term Memory Variational Autoencoder (LSTM-VAE) based anomaly detection framework (PCovNet).
Trained and evaluated PCovNet on a public dataset of 25 COVID-positive individuals using wearable RHR data.
Assessed two configurations of the framework for detecting RHR abnormalities during the infectious period.

Main Results:

The second configuration of PCovNet detected aberrant RHR in 100% of subjects during their infectious period.
PCovNet successfully identified 80% of subjects during the presymptomatic stage of COVID-19 infection.
The framework demonstrated high precision, recall, and F-beta scores in detecting RHR abnormalities.

Conclusions:

PCovNet effectively detects COVID-19 infection using RHR data from wearables, even before symptom onset.
Wearable devices integrated with deep learning offer a promising secondary tool for early COVID-19 detection.
This approach can help mitigate the rapid transmission associated with the presymptomatic phase of COVID-19.