Use of inertial sensors for fall prediction in older people
Falls in older people are a common cause of morbidity, mortality and loss of function. The physiological changes to the motor and sensory functions caused by ageing combined with the presence of other age-related diseases put this population at a high risk of falling.
Inertial sensors have been increasingly used for activity monitoring, classification and event detection with varying degrees of success. A number of algorithms have been specifically developed to detect the incidence of falls with accuracies as high as 100%. However, the prediction of falls using pre-impact data presents a more challenging technical problem. In addition, the focus of current studies has thus far been on detection and pre-impact detection of a fall incidence rather than the assessment of the risk of falling. Risk of falling is a more important parameter to consider for programmes aiming to prevent falls or reduce its risks in these populations. Using inertial sensors for this purpose would potentially identify individuals that require interventions at an early stage.
The aim of this project is to use advanced signal processing techniques to identify movement parameters associated with an increased risk of falling in older people. The project will involve identifying key movement parameters associated with a reduction in movement control and risk of falling, validating measures obtained from inertial sensors with a state-of-the-art movement tracking system and finally using inertial sensors to obtain movement parameters and using those to distinguish between individuals with high and low-risk of falling.
How to apply
If you are interested in applying for the above PhD topic please follow the steps below:
- 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.
- 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.
- Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.
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%.