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Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring
Telehealth and remote patient monitoring (RPM) have been critical components that have received substantial attention and gained hold since the pandemic's beginning. Telehealth and RPM allow easy access to patient data and help provide high-quality care to patients at a low cost. This article p...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8904099/ https://www.ncbi.nlm.nih.gov/pubmed/35273786 http://dx.doi.org/10.1155/2022/8732213 |
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author | Malche, Timothy Tharewal, Sumegh Tiwari, Pradeep Kumar Jabarulla, Mohamed Yaseen Alnuaim, Abeer Ali Hatamleh, Wesam Atef Ullah, Mohammad Aman |
author_facet | Malche, Timothy Tharewal, Sumegh Tiwari, Pradeep Kumar Jabarulla, Mohamed Yaseen Alnuaim, Abeer Ali Hatamleh, Wesam Atef Ullah, Mohammad Aman |
author_sort | Malche, Timothy |
collection | PubMed |
description | Telehealth and remote patient monitoring (RPM) have been critical components that have received substantial attention and gained hold since the pandemic's beginning. Telehealth and RPM allow easy access to patient data and help provide high-quality care to patients at a low cost. This article proposes an Intelligent Remote Patient Activity Tracking System system that can monitor patient activities and vitals during those activities based on the attached sensors. An Internet of Things- (IoT-) enabled health monitoring device is designed using machine learning models to track patient's activities such as running, sleeping, walking, and exercising, the vitals during those activities such as body temperature and heart rate, and the patient's breathing pattern during such activities. Machine learning models are used to identify different activities of the patient and analyze the patient's respiratory health during various activities. Currently, the machine learning models are used to detect cough and healthy breathing only. A web application is also designed to track the data uploaded by the proposed devices. |
format | Online Article Text |
id | pubmed-8904099 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-89040992022-03-09 Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring Malche, Timothy Tharewal, Sumegh Tiwari, Pradeep Kumar Jabarulla, Mohamed Yaseen Alnuaim, Abeer Ali Hatamleh, Wesam Atef Ullah, Mohammad Aman J Healthc Eng Research Article Telehealth and remote patient monitoring (RPM) have been critical components that have received substantial attention and gained hold since the pandemic's beginning. Telehealth and RPM allow easy access to patient data and help provide high-quality care to patients at a low cost. This article proposes an Intelligent Remote Patient Activity Tracking System system that can monitor patient activities and vitals during those activities based on the attached sensors. An Internet of Things- (IoT-) enabled health monitoring device is designed using machine learning models to track patient's activities such as running, sleeping, walking, and exercising, the vitals during those activities such as body temperature and heart rate, and the patient's breathing pattern during such activities. Machine learning models are used to identify different activities of the patient and analyze the patient's respiratory health during various activities. Currently, the machine learning models are used to detect cough and healthy breathing only. A web application is also designed to track the data uploaded by the proposed devices. Hindawi 2022-03-01 /pmc/articles/PMC8904099/ /pubmed/35273786 http://dx.doi.org/10.1155/2022/8732213 Text en Copyright © 2022 Timothy Malche et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Malche, Timothy Tharewal, Sumegh Tiwari, Pradeep Kumar Jabarulla, Mohamed Yaseen Alnuaim, Abeer Ali Hatamleh, Wesam Atef Ullah, Mohammad Aman Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring |
title | Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring |
title_full | Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring |
title_fullStr | Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring |
title_full_unstemmed | Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring |
title_short | Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring |
title_sort | artificial intelligence of things- (aiot-) based patient activity tracking system for remote patient monitoring |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8904099/ https://www.ncbi.nlm.nih.gov/pubmed/35273786 http://dx.doi.org/10.1155/2022/8732213 |
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