ArticleIEEE sensors journal2020
Detection of Respiratory Infections Using RGB-Infrared Sensors on Portable Device.
Article in IEEE sensors journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 68 citations in OpenAlex.
- Distributed intelligence in industrial and automotive cyber-physical systems: a review.Frontiers in robotics and AI · 2024Review
- Deep learning for deterioration prediction of COVID-19 patients based on time-series of three vital signs.Scientific reports · 2023Article
- A rapid review of machine learning approaches for telemedicine in the scope of COVID-19.Artificial intelligence in medicine · 2022Review
- Low-cost thermal imaging with machine learning for non-invasive diagnosis and therapeutic monitoring of pneumonia.Infrared physics & technology · 2022Article
- A comprehensive review of federated learning for COVID-19 detection.International journal of intelligent systems · 2022Article
- Contactless Measurement of Vital Signs Using Thermal and RGB Cameras: A Study of COVID 19-Related Health Monitoring.Sensors (Basel, Switzerland) · 2022Article
- Deep Learning Models for the Diagnosis and Screening of COVID-19: A Systematic Review.SN computer science · 2022Review
- A Wearable Tele-Health System towards Monitoring COVID-19 and Chronic Diseases.IEEE reviews in biomedical engineering · 2022Review
- Machine Learning-Based Research for COVID-19 Detection, Diagnosis, and Prediction: A Survey.SN computer science · 2022Article
- Automatic Separation of Respiratory Flow from Motion in Thermal Videos for Infant Apnea Detection.Sensors (Basel, Switzerland) · 2021Article
- A cognitive IoT-based framework for effective diagnosis of COVID-19 using multimodal data.Biomedical signal processing and control · 2021Article
- Examination of Potential of Thermopile-Based Contactless Respiratory Gating.Sensors (Basel, Switzerland) · 2021Article
- A Motion Artifact Correction Procedure for fNIRS Signals Based on Wavelet Transform and Infrared Thermography Video Tracking.Sensors (Basel, Switzerland) · 2021Article
- COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review.Interdisciplinary sciences, computational life sciences · 2021Review
- Face Mask Assistant: Detection of Face Mask Service Stage Based on Mobile Phone.IEEE sensors journal · 2021Article
- An Overview of Thermal Infrared Imaging-Based Screenings during Pandemic Emergencies.International journal of environmental research and public health · 2021Review
- The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype.Frontiers in artificial intelligence · 2021Review
- Hybrid Transfer Learning and Broad Learning System for Wearing Mask Detection in the COVID-19 Era.IEEE transactions on instrumentation and measurement · 2021Article
- A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic.Chaos, solitons, and fractals · 2020Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronaviruses 2 (SARS-CoV-2) has become a serious global pandemic in the past few months and caused huge loss to human society worldwide. For such a large-scale pandemic, early detection and isolation of potential virus carriers is essential to curb the spread of the pandemic. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. During the pandemic, many people tend to wear masks to reduce the risk of getting sick. Therefore, in this paper, we propose a portable non-contact method to screen the health conditions of people wearing masks through analysis of the respiratory characteristics from RGB-infrared sensors. We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. Then, a bidirectional GRU neural network with an attention mechanism is applied to the respiratory data to obtain the health screening result. The results of validation experiments show that our model can identify the health status of respiratory with 83.69% accuracy, 90.23% sensitivity and 76.31% specificity on the real-world dataset. This work demonstrates that the proposed RGB-infrared sensors on portable device can be used as a pre-scan method for respiratory infections, which provides a theoretical basis to encourage controlled clinical trials and thus helps fight the current COVID-19 pandemic. The demo videos of the proposed system are available at: https://doi.org/10.6084/m9.figshare.12028032.
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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.