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Remote health monitoring of elderly through wearable sensors

Version 2 2024-03-12, 19:56
Version 1 2023-10-19, 18:48
journal contribution
posted on 2024-03-12, 19:56 authored by Mohammed Al-KhafajiyMohammed Al-Khafajiy, Thar Baker, Carl Chalmers, Muhammad Asim, Hoshang Kolivand, Muhammad Fahim, Atif Waraich

Due to a rapidly increasing aging population and its associated challenges in health and social care, Ambient Assistive Living has become the focal point for both researchers and industry alike. The need to manage or even reduce healthcare costs while improving the quality of service is high government agendas. Although, technology has a major role to play in achieving these aspirations, any solution must be designed, implemented and validated using appropriate domain knowledge. In order to overcome these challenges, the remote real-time monitoring of a person’s health can be used to identify relapses in conditions, therefore, enabling early intervention. Thus, the development of a smart healthcare monitoring system, which is capable of observing elderly people remotely, is the focus of the research presented in this paper. The technology outlined in this paper focuses on the ability to track a person’s physiological data to detect specific disorders which can aid in Early Intervention Practices. This is achieved by accurately processing and analysing the acquired sensory data while transmitting the detection of a disorder to an appropriate career. The finding reveals that the proposed system can improve clinical decision supports while facilitating Early Intervention Practices. Our extensive simulation results indicate a superior performance of the proposed system: low latency (96% of the packets are received with less than 1 millisecond) and low packets-lost (only 2.2% of total packets are dropped). Thus, the system runs efficiently and is cost-effective in terms of data acquisition and manipulation.

History

School affiliated with

  • School of Computer Science (Research Outputs)

Publication Title

Multimedia Tools and Applications

Volume

78

Issue

17

Pages/Article Number

24681-24706

Publisher

Springer

ISSN

1380-7501

Date Submitted

2021-12-20

Date Accepted

2018-12-26

Date of First Publication

2019-01-24

Date of Final Publication

2019-09-15

ePrints ID

47557

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