ArticleSensors (Basel, Switzerland)2023
Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Stage-Wise IoT Solutions for Alzheimer's Disease: A Systematic Review of Detection, Monitoring, and Assistive Technologies.Sensors (Basel, Switzerland) · 2025Pooled it
- On the Necessity of Multidisciplinarity in the Development of at-Home Health Monitoring Platforms for Older Adults: Systematic Review.JMIR human factors · 2025Pooled it
- Technology-Enhanced Health Care in Smart Homes: Scoping Review of Sensor Technologies, Clinical Applications, Integration Challenges, and Future Directions.JMIR medical informatics · 2026Article
- IoMT-Blockchain framework for secure and real-time heart disease monitoring using hybrid black-winged optimized spherical structural graph convolutional neural networks.Scientific reports · 2026Article
- IoT-aware extreme machine learning for efficient health monitoring.BMC health services research · 2026Article
- Lightweight and interpretable edge intelligence AI with intrusion detection for trustworthy cardiac arrhythmia in medical IoT.Scientific reports · 2026Article
- Challenges in the Classification of Cardiac Arrhythmias and Ischemia Using End-to-End Deep Learning and the Electrocardiogram: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- The detection of cardiovascular biomarkers in dermal interstitial fluid - a step to real-time monitoring.Frontiers in cardiovascular medicine · 2026Review
- AI-Driven Real-Time Monitoring of Cardiovascular Conditions With Wearable Devices: Scoping Review.JMIR mHealth and uHealth · 2025Article
- IoT driven smart health monitoring for heart disease prediction using quantum kernel enhanced sardine diffusion and CNN.Scientific reports · 2025Article
- Improved security for IoT-based remote healthcare systems using deep learning with jellyfish search optimization algorithm.Scientific reports · 2025Article
- Investigation and Validation of New Heart Rate Measurement Sites for Wearable Technologies.Sensors (Basel, Switzerland) · 2025Article
- Enhancing Eldercare: Assessing Clinician's Perception of Linguistic Summaries in Health Monitoring Alert Systems.Applied clinical informatics · 2025Article
- Wearable sensors-based assistive technologies for patient health monitoring.Frontiers in bioengineering and biotechnology · 2025Article
- Triboostcardio ensemble model for cardiovascular disease detection using advanced blockchain-enabled health monitoring.Frontiers in artificial intelligence · 2025Article
- Revolutionising health monitoring: IOT-Based system with machine learning classification.Health systems (Basingstoke, England) · 2025Article
- IoT-enabled biosensors for real-time monitoring and early detection of chronic diseases.Physical activity and nutrition · 2024Article
- Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN.BMC medical informatics and decision making · 2024Article
- Why digital innovation may not reduce healthcare's environmental footprint.BMJ (Clinical research ed.) · 2024Article
- Spectrum Evaluation in CR-Based Smart Healthcare Systems Using Optimizable Tree Machine Learning Approach.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With an aging population and increased chronic diseases, remote health monitoring has become critical to improving patient care and reducing healthcare costs. The Internet of Things (IoT) has recently drawn much interest as a potential remote health monitoring remedy. IoT-based systems can gather and analyze a wide range of physiological data, including blood oxygen levels, heart rates, body temperatures, and ECG signals, and then provide real-time feedback to medical professionals so they may take appropriate action. This paper proposes an IoT-based system for remote monitoring and early detection of health problems in home clinical settings. The system comprises three sensor types: MAX30100 for measuring blood oxygen level and heart rate; AD8232 ECG sensor module for ECG signal data; and MLX90614 non-contact infrared sensor for body temperature. The collected data is transmitted to a server using the MQTT protocol. A pre-trained deep learning model based on a convolutional neural network with an attention layer is used on the server to classify potential diseases. The system can detect five different categories of heartbeats: Normal Beat, Supraventricular premature beat, Premature ventricular contraction, Fusion of ventricular, and Unclassifiable beat from ECG sensor data and fever or non-fever from body temperature. Furthermore, the system provides a report on the patient's heart rate and oxygen level, indicating whether they are within normal ranges or not. The system automatically connects the user to the nearest doctor for further diagnosis if any critical abnormalities are detected.
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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.