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Development of android middleware for wearable data analytics and recommendations on the go

Wearable technology comes with the promise of improving one’s lifestyles thru data mining of their physiological condition. The potential to generate a change in daily or routine habits thru these devices leaves little doubt. Whilst the hardware capabilities of wearables have evolved rapidly, software apps that interpret and present the physiological data and make recommendations in a simple, clear and meaningful way have not followed a similar pattern of evolution.

Existing fitness apps provide routinely some information to the wearer by mining personal data but the subsequent analysis is limited to supporting ad hoc personal goals. The information and recommendations presented are often either not entirely relevant or incomplete and often not easy to interpret by the wearer.

The primary motivation behind this research is to address this wearable technology software challenge by developing a middleware mobile app that is supported by data analytics and machine learning to assist with interpretation of wearer data and with making of personal lifestyle improvement recommendations on the go which may then be used to feedback to the wearer’s daily goals and activities. The secondary motivation is to correlate and compare with trends in the wearer’s peer community.

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you woold be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: https://www.brunel.ac.uk/research/Research-degrees/Research-degree-funding. The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)

Marios Angelides - Marios C. Angelides is a Computer Scientist, Chartered Engineer (CEng) and a Chartered Fellow of the British Computer Society (FBCS CITP). He holds a BSc (First Class Honours) and a PhD both in Computing and both from the London School of Economics (LSE) where he also began his academic career more than three decades ago specializing in Artificial Intelligence (AI). A symbolic programming language he developed as a degree finalist for coding AI applications was commercialized and then turned into his first book. He continued working on AI throughout his career and for the last two decades, he has been researching the application of creative computing techniques, such as machine learning, serious gaming, and cognitive modelling, recently in developing smart IoT apps. During this period, he published several books, including “Multimedia Information Systems” (Kluwer), “MPEG Applications” (Wiley), and “Digital Games” (IEEE/Wiley). In 2016, several years prior to joining The Computer Journal (Oxford University Press) editorial board, a paper of his that was published in The Computer Journal with a focus on “machine learning in multimedia” was the runner up winner of the annual Oxford University Press “2016 Wilkes Award”. In 2019 he was elected to the Editorial Board of The Computer Journal for which he is now serving as a Deputy Editor.