Nor Maniha, Abdul Ghani and Lim, Wei Jie and Hoh, Wei Siang and Salmah Anim, Abu Hassan (2024) Rehabilitation and home health monitoring Based-AI scheduling application for coronary artery disease and cardiovascular patients. Instrumentation Mesure Metrologie, 23 (2). pp. 161-173. ISSN 1631-4670. (Published)
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Abstract
Cardiovascular and coronary artery disease diseases complications can be very serious, and it is crucial for a close monitoring and routine rehabilitation activities in order to help patients to get back on their normal lifestyle. Even though most individuals with COVID-19 did recover within weeks of ailment, a few individuals still encounter severe long COVID conditions and 'Silent Hypoxia'. Individuals commonly encounter distinctive combinations of long COVID symptoms such as difficulty in breathing, critical heart palpitations, worst sleep quality, dizziness on standing, etc. Silent hypoxia is a condition where patients have an extremely low oxygen level but do not show any symptoms of breathlessness. In order to navigate and resolve the above issues, this work proposes and implements a real-time monitoring towards the changes of patient health data using continuous clinical surveillance solution. The application is an appropriate rehabilitation course that plays a vital role for post discharge patients in providing an improvement for respiratory, cardiovascular, and psychological components. It is very important to provide the visibility of health data in terms of heart rate and VO2 max values to enable emergency respiratory support, important alerts and real-time monitoring. In this work, a novel configuration for home health monitoring and rehabilitation based-AI scheduling system is proposed for coronary artery disease and cardiovascular patients. The Android application introduces a secure health data sharing and smart alerting system to provide full surveillance towards the patient. It will enable the interaction between the smart wearable by using the health kit and artificial intelligence algorithms to schedule the best fit rehabilitation activity based on the patient’s health status and live monitoring by medical practitioners.
| Item Type: | Article |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Cardiovascular fitness; Coronary artery disease; Health monitoring; Heart rate; Long COVID-19; VO2 max |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Computing Faculty of Electrical and Electronic Engineering Technology |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 31 Jul 2024 00:45 |
| Last Modified: | 31 Jul 2024 00:45 |
| URI: | http://umpir.ump.edu.my/id/eprint/41436 |
| Download Statistic: | View Download Statistics |
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