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CHIC: Behavioural modelling based on opportunistic sensing techniques within IoT (Computing)

Project Description

Connected Health Innovation Centre:

In 2013 Invest NI committed to create a number of multi-million-pound business research centres, in key business areas, through the establishment of industry led competence centres. The Connected Health Innovation Centre (CHIC) is a competence centre that focuses on the Connected Health market. In 2019 Invest NI extended the support of CHIC through funding for a further 3 years. This funding supports a number of strategic research projects as well as Doctoral research in order to ensure research produced by CHIC advances the state of the art.

Connected Health embraces a number of areas and focuses on the use of technology to improve health care. It will utilise technology to provide opportunities for care provision, diagnostics and support beyond the hospital setting.
This studentship will reside within the School of Computing, Faculty of Computing, Engineering and the Built Environment at Ulster University based at the Jordanstown campus. The project is fully aligned with the research strategy of CHIC.

Project Summary:

Understanding human behaviour in an automatic and non-intrusive manner, constitutes an important and emerging area of research within Connected Health. With the rapid development of the Internet of Things (IoT), combined with advances in machine/deep learning, technology-based solutions to automatically detect and model human behaviours are becoming possible. This technology can support services such as activity recognition, fall detection, behaviour modelling and risk determination. There is, however, still much research required in order to ensure these solutions are robust and that they can be adopted for use within real world environments.
This PhD research will investigate the use of unobtrusive sensing technologies for modelling of human behaviours in real world settings. The project will address issues around personalisation and adaption of these models through the application of semi-supervised approaches to data annotation and federated learning to provided smarter, lower latency and less power intensive models that preserve user privacy.

Funding Notes

This project is funded by: Invest NI Connected Health Innovation Centre.

Invest Northern Ireland is acknowledged for supporting this project under the Competence Centre Programme Grant RD1014267- Connected Health Innovation Centre.

Funding will cover all fees and a generous stipend for the successful candidate throughout the three years of study.

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