Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study
Artificial Intelligence (AI) and Internet of Things (IoT) offer immense potential to transform conventional healthcare systems. The IoT and AI enabled smart systems can play a key role in driving the future of smart healthcare. Remote monitoring of critical and non-critical patients is one such fiel...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/4/1420 |
| _version_ | 1827652863938002944 |
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| author | Saeed Ali Alsareii Mohsin Raza Abdulrahman Manaa Alamri Mansour Yousef AlAsmari Muhammad Irfan Umar Khan Muhammad Awais |
| author_facet | Saeed Ali Alsareii Mohsin Raza Abdulrahman Manaa Alamri Mansour Yousef AlAsmari Muhammad Irfan Umar Khan Muhammad Awais |
| author_sort | Saeed Ali Alsareii |
| collection | DOAJ |
| description | Artificial Intelligence (AI) and Internet of Things (IoT) offer immense potential to transform conventional healthcare systems. The IoT and AI enabled smart systems can play a key role in driving the future of smart healthcare. Remote monitoring of critical and non-critical patients is one such field which can leverage the benefits of IoT and machine learning techniques. While some work has been done in developing paradigms to establish effective and reliable communications, there is still great potential to utilize optimized IoT network and machine learning technique to improve the overall performance of the communication systems, thus enabling fool-proof systems. This study develops a novel IoT framework to offer ultra-reliable low latency communications to monitor post-surgery patients. The work considers both critical and non-critical patients and is balanced between these to offer optimal performance for the desired outcomes. In addition, machine learning based regression analysis of patients’ sensory data is performed to obtain highly accurate predictions of the patients’ sensory data (patients’ vitals), which enables highly accurate virtual observers to predict the data in case of communication failures. The performance analysis of the proposed IoT based vital signs monitoring system for the post-surgery patients offers reduced delay and packet loss in comparison to IEEE low latency deterministic networks. The gradient boosting regression analysis also gives a highly accurate prediction for slow as well as rapidly varying sensors for vital sign monitoring. |
| first_indexed | 2024-03-09T21:07:07Z |
| format | Article |
| id | doaj.art-618aae9f963d4498900b7168d8e43704 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:07:07Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-618aae9f963d4498900b7168d8e437042023-11-23T21:59:13ZengMDPI AGSensors1424-82202022-02-01224142010.3390/s22041420Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot StudySaeed Ali Alsareii0Mohsin Raza1Abdulrahman Manaa Alamri2Mansour Yousef AlAsmari3Muhammad Irfan4Umar Khan5Muhammad Awais6Department of Surgery, College of Medicine, Najran University Saudi Arabia, Najran 11001, Saudi ArabiaDepartment of Computer Science, Edge Hill University, St Helens Rd., Ormskirk L39 4QP, UKDepartment of Surgery, College of Medicine, Najran University Saudi Arabia, Najran 11001, Saudi ArabiaDepartment of Surgery, College of Medicine, Najran University Saudi Arabia, Najran 11001, Saudi ArabiaElectrical Engineering Department, College of Engineering, Najran University, Najran 11001, Saudi ArabiaDepartment of Computer Science, Edge Hill University, St Helens Rd., Ormskirk L39 4QP, UKDepartment of Computer Science, Edge Hill University, St Helens Rd., Ormskirk L39 4QP, UKArtificial Intelligence (AI) and Internet of Things (IoT) offer immense potential to transform conventional healthcare systems. The IoT and AI enabled smart systems can play a key role in driving the future of smart healthcare. Remote monitoring of critical and non-critical patients is one such field which can leverage the benefits of IoT and machine learning techniques. While some work has been done in developing paradigms to establish effective and reliable communications, there is still great potential to utilize optimized IoT network and machine learning technique to improve the overall performance of the communication systems, thus enabling fool-proof systems. This study develops a novel IoT framework to offer ultra-reliable low latency communications to monitor post-surgery patients. The work considers both critical and non-critical patients and is balanced between these to offer optimal performance for the desired outcomes. In addition, machine learning based regression analysis of patients’ sensory data is performed to obtain highly accurate predictions of the patients’ sensory data (patients’ vitals), which enables highly accurate virtual observers to predict the data in case of communication failures. The performance analysis of the proposed IoT based vital signs monitoring system for the post-surgery patients offers reduced delay and packet loss in comparison to IEEE low latency deterministic networks. The gradient boosting regression analysis also gives a highly accurate prediction for slow as well as rapidly varying sensors for vital sign monitoring.https://www.mdpi.com/1424-8220/22/4/1420Internet of Things (IoT)machine learning (ML)Artificial Intelligence (AI)healthcarepatient monitoringhuman activity classification (HAC) |
| spellingShingle | Saeed Ali Alsareii Mohsin Raza Abdulrahman Manaa Alamri Mansour Yousef AlAsmari Muhammad Irfan Umar Khan Muhammad Awais Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study Sensors Internet of Things (IoT) machine learning (ML) Artificial Intelligence (AI) healthcare patient monitoring human activity classification (HAC) |
| title | Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study |
| title_full | Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study |
| title_fullStr | Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study |
| title_full_unstemmed | Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study |
| title_short | Machine Learning and Internet of Things Enabled Monitoring of Post-Surgery Patients: A Pilot Study |
| title_sort | machine learning and internet of things enabled monitoring of post surgery patients a pilot study |
| topic | Internet of Things (IoT) machine learning (ML) Artificial Intelligence (AI) healthcare patient monitoring human activity classification (HAC) |
| url | https://www.mdpi.com/1424-8220/22/4/1420 |
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