Back to Search
Start Over
Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time.
- Source :
-
Sensors (Basel, Switzerland) [Sensors (Basel)] 2023 May 30; Vol. 23 (11). Date of Electronic Publication: 2023 May 30. - Publication Year :
- 2023
-
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.
- Subjects :
- Humans
Aged
Neural Networks, Computer
Heart Rate
Deep Learning
Internet of Things
Subjects
Details
- Language :
- English
- ISSN :
- 1424-8220
- Volume :
- 23
- Issue :
- 11
- Database :
- MEDLINE
- Journal :
- Sensors (Basel, Switzerland)
- Publication Type :
- Academic Journal
- Accession number :
- 37299933
- Full Text :
- https://doi.org/10.3390/s23115204