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Raza, Mohsin, Awais, Muhammad 
ORCID: https://orcid.org/0000-0001-6421-9245, Singh, Nishant, Imran, Muhammad and Hussain, Sajjad
(2021)
Intelligent IoT framework for indoor healthcare monitoring of Parkinson’s disease patient.
IEEE Journal on Selected Areas in Communications, 39 (2).
pp. 593-602.
ISSN 0733-8716
Abstract
Parkinson's disease is associated with high treatment costs, primarily attributed to the needs of hospitalization and frequent care services. A study reveals annual per-person healthcare costs for Parkinson's patients to be 21,482,withanadditional29,695 burden to society. Due to the high stakes and rapidly rising Parkinson's patients' count, it is imperative to introduce intelligent monitoring and analysis systems. In this paper, an Internet of Things (IoT) based framework is proposed to enable remote monitoring, administration, and analysis of patient's conditions in a typical indoor environment. The proposed infrastructure offers both static and dynamic routing, along with delay analysis and priority enabled communications. The scheme also introduces machine learning techniques to detect the progression of Parkinson's over six months using auditory inputs. The proposed IoT infrastructure and machine learning algorithm are thoroughly evaluated and a detailed analysis is performed. The results show that the proposed scheme offers efficient communication scheduling, facilitating a high number of users with low latency. The proposed machine learning scheme also outperforms state-of-the-art techniques in accurately predicting the Parkinson's progression.
| Item Type: | Article |
|---|---|
| Additional Information: | Funding Information: The work of Muhammad Imran was supported by the Deanship of Scientific Research through the Research Group Project under Grant RG-1435-051. |
| Uncontrolled Keywords: | internet of things (iot),parkinson's disease,low latency,machine learning,priority communications,probability of blocking,computer networks and communications,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/1700/1705 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 17 Oct 2023 00:45 |
| Last Modified: | 10 Dec 2024 01:42 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/93313 |
| DOI: | 10.1109/jsac.2020.3021571 |
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