Chat online with top universities at our virtual study fair - Tuesday 7th July (12pm - 5pm BST)

University of East Anglia Featured PhD Programmes
Sheffield Hallam University Featured PhD Programmes
University of Reading Featured PhD Programmes

A decentralized, data driven health monitoring and diagnostics platform based on Artificial Intelligence (AI) and wearable/portable Internet of Medical Things (IoMT) sensors


Department of Computing and Information Science

About the Project

Research Group:
Computing, Informatics and Applications Research Group: https://auth-authoring-prod.anglia.ac.uk/science-and-engineering/research/institutes-and-groups/computing-informatics-and-applications

Proposed supervisory team
Dr Mahdi Maktabdar: https://auth-authoring-prod.anglia.ac.uk/people/mahdi-maktab-dar-oghaz
Dr Lakshmi Babu Saheer: https://auth-authoring-prod.anglia.ac.uk/people/lakshmi-babu-saheer

Theme


Artificial Intelligence, Machine Learning, Data Science and Applications.

Summary of the research project


With the advent of the Fourth Industrial Revolution, novel information and communication technologies such as Cloud Computing, Big Data, Internet of Things (IoT), 5G and Artificial intelligence (AI) have been incorporated into organisations and industries to facilitate and support more efficient and flexible processes, services and products. Industry 4.0’s inherent future-forward benefits are driving innovation across various industries and disrupting outdated and inefficient practices (Chanchaichujit et al., 2019; Kumari et al., 2018).
Healthcare is one of the notable industries that has been influenced by the Fourth Industrial Revolution and Healthcare 4.0 is a term that has emerged to resemble this revolution. Healthcare 4.0 is a collective term for data driven digital health technologies such as smart health, mobile health, wireless health, eHealth, online health, medical IT, telehealth/telemedicine, digital medicine, health informatics, pervasive health, and health information system. The revolution in the healthcare industry is already underway, yet, because of the conservative and slower pace of technological adoption by healthcare insiders, as compared to other industries, digitalization in this sector has not been so evident (Pace et al., 2018; Manogaran et al., 2017).

But make no mistake the revolution in healthcare industry is arriving at your nearby hospital in near future. With populations aging, chronic diseases rising and medical costs skyrocketing, the healthcare universe is in desperate need of the improvements that digitalization and industrialization will bring in terms of saving costs, improved diagnostics and more effective care. Moreover, the global shortage of doctors, nurses and technicians demands for improved efficiency and the need for technology to help bridge the demand-supply gap in services(Xu et al.,2019; Xu et al.,2018).

Healthcare industry is gradually moving toward utilization of the Internet of Medical Things (IoMT) devices such as smart wearables, capable to measure vital signs, Artificial Intelligence (AI) and cloud computing, which is leading to great leaps in diagnostic speed, accuracy and transform how we keep people safe and healthy especially as the demand for solutions to lower healthcare costs increase in the coming years. The IoMT, a major driver of this revolution takes patient monitoring to the next level by providing healthcare system with an uninterrupted stream of patient’s vital signs and other critical measures which enables early diagnostic and proactive healthcare invention. The IoMT can improve early diagnoses while allowing data collection for analytics, a win-win for the patient, and patients that benefit from the data down the road (Alsubaei et al., 2017; Joyia et al., 2017).

In this regard, we are proposing a research with the primary aim of creating a decentralized, data driven health monitoring and diagnostics ecosystem based on Artificial Intelligence (AI) and Internet of Medical Things (IoMT) sensors and wearables. The proposed research takes advantage of uninterrupted streams of patient’s vital signs provided by IoMT wearables such as blood pressure, pulse, temperature, respiration and oxygen saturation monitors and temporal data analytic methods in specific Recurrent Neural Networks (RNN) to improve early diagnoses of tens of chronic health conditions. The proposed research benefits a supervised data analytic engine, pretrained with millions of patient’s historical data available in the following dataset:
• The University of Queensland Vital Signs Dataset (https://outbox.eait.uq.edu.au/uqdliu3/uqvitalsignsdataset/index.html)
• PhysioNet Dataset (https://physionet.org/about/database/)
• OpenICE (https://www.openice.info/docs/1_overview.html)

The proposed ecosystem comprising IoMT, Artificial Intelligence and cloud computing enables healthcare system to perform effectively as our population continues to age. The proposed ecosystem also has tremendous potential to help deal with the rising costs of care. The system provides opportunity to help remote caregivers ensure the safety of their loved ones with wearable devices that learn the regular routines of the person who wears the device and can issue a warning if something seems amiss as well as alert if seniors have breached their boundaries which is often of concern for memory-care patients.

There is possibility of several research approaches here which could spin-off as individual doctoral research in itself. The significance of an automatic digital health monitoring platform has been elevated in the recent times with the covid-19 pandemic. The isolated risk groups (like senior citizens or people with respiratory troubles) are advised to have minimal human contact. Such virtual healthcare platforms would be greatly beneficial in such pandemic situations, when there is a scarcity of carers all around. The proposed ecosystem may monitor the vital signs and at the same time could also have a machine learning backend to analyse these inputs to alert carers or appropriate medical authorities. These alert systems could be designed, customized and optimized by carers based on the type of monitoring to be focused at each stage or for each individual.

Another new dimension to add in this framework would be to include the mental wellbeing monitoring. This might require a bit more involved participation from the subjects rather than just vital signs. But, the same system could be extended and customized for general health and mental health monitoring in a broader sense across the whole population.

Where you'll study


Cambridge; https://auth-authoring-prod.anglia.ac.uk/student-life/life-on-campus/cambridge-campus

Next steps


If you wish to be considered for this project, you will need to apply for our Computer and Information Science PhD (https://auth-authoring-prod.anglia.ac.uk/study/postgraduate/computer-science-research). In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.


Funding Notes

This project is self-funded.

Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website (View Website) as they become available.

References

Chanchaichujit, J., Tan, A., Meng, F., & Eaimkhong, S., 2019. Internet of Things (IoT) and Big Data Analytics in Healthcare. In Healthcare 4.0, pp.17-36. Palgrave Pivot, Singapore.
Kumari, A., Tanwar, S., Tyagi, S., & Kumar, N., 2018. Fog computing for Healthcare 4.0 environment: Opportunities and challenges. Computers & Electrical Engineering, 72, pp.1-13.
Manogaran, G., Thota, C., Lopez, D., & Sundarasekar, R., 2017. Big data security intelligence for healthcare industry 4.0. In Cybersecurity for Industry 4.0, pp.103-126. Springer, Cham.
Pace, P., Aloi, G., Gravina, R., Caliciuri, G., Fortino, G., & Liotta, A., 2018. An edge-based architecture to support efficient applications for healthcare industry 4.0. IEEE Transactions on Industrial Informatics, 15(1), pp.481-489.
Xu, L. D., & Duan, L., 2019. Big data for cyber physical systems in industry 4.0: a survey. Enterprise Information Systems, 13(2), pp.148-169.
Xu, L. D., Xu, E. L., & Li, L., 2018. Industry 4.0: state of the art and future trends. International Journal of Production Research, 56(8), pp.2941-2962.
Joyia, G. J., Liaqat, R. M., Farooq, A., & Rehman, S., 2017. Internet of Medical Things (IOMT): applications, benefits and future challenges in healthcare domain. J Commun, 12(4), pp,240-247.
Alsubaei, F., Abuhussein, A., & Shiva, S., 2017. Security and privacy in the internet of medical things: taxonomy and risk assessment. In 2017 IEEE 42nd Conference on Local Computer Networks Workshops (LCN Workshops), pp.112-120. IEEE.

Email Now

Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here

The information you submit to Anglia Ruskin University ARU will only be used by them or their data partners to deal with your enquiry, according to their privacy notice. For more information on how we use and store your data, please read our privacy statement.

* required field

Your enquiry has been emailed successfully





FindAPhD. Copyright 2005-2020
All rights reserved.