Intelligent Medical Care Solution for Elderly People Based on Wireless Sensor Network Technology

PhD Thesis


Elsaadi, R. 2024. Intelligent Medical Care Solution for Elderly People Based on Wireless Sensor Network Technology. PhD Thesis University of Derby College of Science and Engineering https://doi.org/10.48773/q4734
AuthorsElsaadi, R.
TypePhD Thesis
Qualification namePhD Doctor of Philosophy
Abstract

The NHS spent millions of pounds every year to provide the necessary services and care for Elderly people in Derbyshire and across the UK. This includes home care, medication, examination, consultation, and many other services. However, these services are still not good enough, due to the lack of technology in use. This in many cases leads to the loss of lives. There is a great need to develop a new monitoring system using recently developed cutting-edge technology that will enable to provision of the required near real-time care, medication record, and information management, at a lower cost and high efficiency. Telehealth is the possible innovative approach to tackle such needs, sustain economic growth, and improve social development. E-Health is the umbrella that includes telehealth, electronic medical records, and other components of Health Information Technology.
One of the research contributions is the innovation of USB plug-and-play programmed in Python on Raspberry Pi as a host to other devices, where many of the ambient assisted living technologies (AALT) available to the end-users as off-shelf products have no common inter-operational protocol (Language). It is the case that each product has its communication protocols, different interfaces and interoperation which limits their solution efficiency for long-term health condition monitoring systems.
This research is to develop this technology and design an intelligent multi-function AD-HOC wireless e-Health monitoring network for Elderly people. The approach will enable the population of elderly to manage their daily life activities within multiple environments i.e., from their comfort home, the care centres and the hospitals using a universal platform system. The system is to include biofeedback monitoring body sensors, such as blood pressure, heart rate, blood glucose and body temperature monitoring devices. All these data are collected accurately and processed in an intelligent system which has all the algorithms to take certain actions with any changes that occur or give health warnings. With this development, it will be possible to analyse most health conditions based on near real-time wireless body sensor monitoring technology.
The research contribution aims to improve the environment by making a real-time wireless sensor network to monitor patients for a regular check of health condition by using devices that transmit data from homes wirelessly to the relevant (caregiver, GP, Hospitals and specialist doctors). The proposed system benefits from the use of algorithms, which are used to provide quality and quick health care advice to patients at home. The algorithm processes the data from the database web server. The database stores the patient’s history of health conditions with all measurements obtained from the devices. This data is processed in a machine learning algorithm to generate notifications for any changes that occur in users’ health and by checking their history records. Machine learning can help predict and find any patterns of patient health changes, these anomalies can be checked by the experts in the field for evaluation and compared to the data sets in the storage where the AI can go through millions of records and give an indication as well as predict possible diseases for the patient might have in the future cross-referencing family or relative conditions. This system includes medication, consultation, home care, and many other services.
This PhD research programme focuses on Assisted Living Technology for elderly patients with long-term health conditions. It is one of the new emerging cutting-edge technologies that will be introduced to communities and people. The technology will help the local National Health Service (NHS) in the UK, EU, and many countries worldwide to improve the healthcare services, and quality of people's lives and tackle the barrier of cost. For example, the cost of treatment of elderly people with long-term health conditions in the UK alone is estimated to be over 70% of the NHS budget. This treatment is including medication, consultation, homecare, and many other services. However, these services are still not satisfactory, due to the lack of implemented technology, which, in many cases leads to the loss of lives and waste of NHS resources.

KeywordsAssisted Living Technologies, Wireless Health Monitoring, Machine Learning Long-term Health Conditions Remote Monitoring.
Year2024
PublisherCollege of Science and Engineering, University of Derby
Digital Object Identifier (DOI)https://doi.org/10.48773/q4734
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Open
Output statusUnpublished
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Deposited21 Feb 2024
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Related outputs

Deployment of assisted living technology solution platform using smart body sensors for elderly people health monitoring.
Elsaadi, Riyad and Mahmoud, Shafik 2017. Deployment of assisted living technology solution platform using smart body sensors for elderly people health monitoring. International Journal of Robotics and Mechatronics.
An intelligent medical care solution for elderly people with long term health condition based on wireless sensors network technology
Elsaadi, Riyad and Shafik, Mahmoud 2015. An intelligent medical care solution for elderly people with long term health condition based on wireless sensors network technology. International Journal of Robotics and Mechatronics. https://doi.org/10.21535%2Fijrm.v2i1.873
Deployment of assisted living technology using intelligent body sensors platform for elderly people health monitoring
Elsaadi, Riyad and Shafik, Mahmoud 2016. Deployment of assisted living technology using intelligent body sensors platform for elderly people health monitoring. Proceedings of the 14th International Conference on Manufacturing Research (ICMR 2016). https://doi.org/10.3233/978-1-61499-668-2-219