ReviewJournal of pharmaceutical analysis2022
Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review.
Review in Journal of pharmaceutical analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Exhale-Focused Thermal Image Segmentation Using Optical Flow-Based Frame Filtering and Transformer-Aided Deep Networks.Bioengineering (Basel, Switzerland) · 2025Article
- Auscultation-Based Pulmonary Disease Detection through Parallel Transformation and Deep Learning.Bioengineering (Basel, Switzerland) · 2024Article
- An automated ICU agitation monitoring system for video streaming using deep learning classification.BMC medical informatics and decision making · 2024Article
- Challenges and prospects of visual contactless physiological monitoring in clinical study.NPJ digital medicine · 2023Review
- DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays.Sensors (Basel, Switzerland) · 2023Article
- Considerations and Challenges for Real-World Deployment of an Acoustic-Based COVID-19 Screening System.Sensors (Basel, Switzerland) · 2022Article
- Markerless Radio Frequency Indoor Monitoring for Telemedicine: Gait Analysis, Indoor Positioning, Fall Detection, Tremor Analysis, Vital Signs and Sleep Monitoring.Sensors (Basel, Switzerland) · 2022Review
- Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System.Sensors (Basel, Switzerland) · 2022Article
- A Machine Learning Model for Predicting Hospitalization in Patients with Respiratory Symptoms during the COVID-19 Pandemic.Journal of clinical medicine · 2022Article
- ISW-LM: An intensive symptom weight learning mechanism for early COVID-19 diagnosis.Computers in biology and medicine · 2022Article
- Individual Factors Associated With COVID-19 Infection: A Machine Learning Study.Frontiers in public health · 2022Article
- Machine learning applications for COVID-19 outbreak management.Neural computing & applications · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which caused the coronavirus disease 2019 (COVID-19) pandemic, has affected more than 400 million people worldwide. With the recent rise of new Delta and Omicron variants, the efficacy of the vaccines has become an important question. The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions, particularly for healthcare workers. In this paper, we discuss the current literature on invasive/contact and non-invasive/non-contact technologies (including Wi-Fi, radar, and software-defined radio) that have been effectively used to detect, diagnose, and monitor human activities and COVID-19 related symptoms, such as irregular respiration. In addition, we focused on cutting-edge machine learning algorithms (such as generative adversarial networks, random forest, multilayer perceptron, support vector machine, extremely randomized trees, and k-nearest neighbors) and their essential role in intelligent healthcare systems. Furthermore, this study highlights the limitations related to non-invasive techniques and prospective research directions.
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Identifiers
What OpenQuestion holds
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.