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Brain computer interface (BCI) to reduce risk of fall in older people


Department of Electrical Engineering and Electronics

About the Project

This project is part of a 4-year Dual PhD degree programme between the National Tsing Hua University (Taiwan) and the University of Liverpool (England). As part of the NTHU-UoL Dual PhD Award students are in the unique position of being able to gain 2 PhD awards at the end of their degree from two internationally recognised world-leading Universities. As well as benefiting from a rich cultural experience, students can draw on large-scale national facilities of both countries and create a worldwide network of contacts across two continents.

The latest set of projects targeted goal #11 from the UN Sustainable Development Goals: Sustainable Cities and Communities.
Falls are a major public health problem [1]. It is the second leading cause of accidental or unintentional injury deaths worldwide. An estimated 646,000 individuals die from falls every year worldwide. Falls are the most common type of injury among older people and a major source of both functional and psychological morbidity. The financial costs from fall-related injuries are huge. The cost of fractures alone caused by falls to the NHS in the UK is staggering and increasing annually (£3.3billion in 2019) in addition to the socio-economic cost that follows [2]. In Taiwan, 1/6 of older people experienced falls in one year, and each spent at least $130,000NTD in the hospital, not including the succeeding care at home.

Although the benefits of exercise for fall prevention have been demonstrated [3], the majority of older people are physically inactive due to several physical and mental limitations they may face. Another alternative treatment such as BCI and motor imagery with providing additional sensory information have the potential enhance movement performance and so decrease the risk of fall [4].
The aim of this project is to explore Brain computer interface and neurofeedback technology as a mechanism to reduce risk of fall in older people.

The suitable applicant will have a
1) BSc (first class or upper second)/MSc/MPhil in Electronics Engineering, Computer Science, Mathematics, Physics or a related subject.
2) Strong theoretical and applied knowledge in signal processing, machine learning, as well as good programming skills in Matlab/C++ or other languages of choice.

It is desirable if the applicant has experience in experimental design, interacting with older people, the application of artificial intelligence in the field, experience in writing academic papers or reports.

Applicants should apply via the University of Liverpool application form, for a PhD in the subject area listed above via: https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/

For academic enquires please contact .
For enquires on the application process or to find out more about the Dual programme please contact School of Electrical Engineering and Electronics Postgraduate Office ()

Funding Notes

This project is a part of a 4-year dual PhD programme between National Tsing Hua University (Taiwan) and the University of Liverpool (England). Students should spend equal time studying in each institution. Both the UoL and NTHU have agreed to waive the tuition fees for the duration of the project and stipend of $11,000 TWD/£280 GBP a month will be provided as a contribution to living costs. When applying please ensure you Quote the supervisor & project title you wish to apply for and note ‘NTHU-UoL Dual Scholarship’ when asked for details of how plan to finance your studies.

References

[1] Sousa et al (2017), Risk for falls among community dwelling older people: A systematic literature review. Rev Gaucha Enferm. 2017 Feb 23;37(4): e55030. doi: 10.1590/1983-1447.2016.04.55030
[2] Age UK, Later life in the United Kingdom 2019, fact sheet. www.ageuk.org.uk/reports-and-publications/later_life_uk_factsheet
[3] Skelton et al (2005) Tailored group exercise (Falls Management Exercise — FaME) reduces falls in community-dwelling older frequent fallers (an RCT), Age and Ageing, Volume 34, Issue 6, November 2005, Pages 636–639, https://doi.org/10.1093/ageing/afi174
[4] Shahrbanian et al (2019). The comparison of the effects of physical activity and neurofeedback training on postural stability and risk of fall in elderly women: A single-blind randomized controlled trial. Physiother Theory Pract. 2019 Jun 20:1-8. doi: 10.1080/09593985.2019.1630877

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