A Data-driven Health Monitoring Ecosystem Using Artificial Neural Networks (ANN) and Internet of Medical Things (IoMT) devices and sensors for Proactive Respiratory Care and Diagnoses.

The healthcare industry is moving towards data-driven practices that rely on technologies such as the Internet of Medical Things (IoMT), Artificial Intelligence, and Cloud Computing. One aim of these technologies is to provide proactive patient monitoring and diagnosis and promote accessible care for the public, which is particularly useful in the care of the elderly and vulnerable (Tortorella et al.,2021). One area that can benefit from this transformation is respiratory care. Hospital admissions for respiratory diseases have risen 3-fold over the past 7 years and COVID19 is worsening the situation. According to the NHS, early diagnosis of respiratory diseases can reduce the severity of the condition and the cost of future medication (NHS, 2020). Studies show COVID19 and its variants are here to stay, and their long-term impact, side effects and complications are as yet not fully known (Jabbari and Rezaei, 2021). Technological advances in the way that we monitor, diagnose and provide care for respiratory diseases and conditions such as COVID19, especially for the elderly and vulnerable , not only has the potential to saves lives, but also reduce the cost of care and medication and improve public health.

The aim of this project is to promote public wellbeing through a data-driven, proactive respiratory monitoring and diagnosis ecosystem capable of identifying potential conditions such as asthma, bronchitis and pneumonia using ANNs and IoMT devices and sensors. This is to be aimed primarily at elderly and vulnerable population in care homes, although such systems also have enormous potential in the wider population. 

The successful candidate will:

• Design and deploy an end-to-end data-pipeline that channels vital-signs and other respiratory-related measurements from smart IoMT devices to a data analytical engine.
• Design and develop a data analytical engine comprising a series of data-driven algorithms that exploit ANN techniques to proactively analyse users’ vital signs and respiratory-related measurements.
• Develop a small-scale working prototype of the proposed digital ecosystem.
• Conduct initial data collection in collaboration with care homes across Cambridge.  
• Validate the accuracy and effectiveness of the proposed ecosystem using existing clinical datasets.

The proposed system will employ commercially available IoMT devices to measure vital-signs and respiratory-related readings and channel them to a central data analytical engine that exploits ANN to train a predictive model capable of providing a proactive analysis of the user’s respiratory-related measurements for potential respiratory conditions and diseases. System validation will be based on existing benchmark clinical datasets.

If you would like to discuss this research project please contact Dr Ian van-der-Linde (ian.vanderlinde(@)aru.ac.uk)

Candidate requirements

Applications are invited from UK fee status only. Applicants should have (or expect to achieve) a minimum upper second-class undergraduate degree (or equivalent) in a cognate discipline. A Masters’ degree in a relevant subject is desirable.

Applicants must be prepared to study on a full-time basis, attending at Cambridge campus. The Vice Chancellor’s PhD scholarship awards are open to Home fee status applicants only.

Application Procedures

Applications for a Vice Chancellor’s PhD Scholarship are made through the application portal on our website: https://aru.ac.uk/research/postgraduate-research/phd-studentships

We will review all applications after the submission deadline of 27^th February. We will contact shortlisted applicants in the week commencing 14^th March. Interviews will be held between 21^st March – 1^st April. The interview date for this project can be found on our website.

If you have any queries relating to the application process or the terms and conditions of the scholarships, please email vcphdscholarships(@)aru.ac.uk.

Documentation required

You will need the following documents available electronically to upload them to the application portal (we can accept files in pdf, jpeg or Word format):

1. Certificates and transcripts from your Bachelor and Masters degrees, (if applicable)
2. Your personal statement explaining your suitability for the project
3. Passport and visa (if applicable)
4. Curriculum Vitae