Hundreds of thousands of people face significant challenges as a result of disease or injury. Patients can improve their independence and quality of life through specialised support but the number of healthcare professionals is inadequate to provide the required level of care. An alternative is the use of specialised devices but unfortunately they do not adapt to users’ evolving and changing needs. The use of intelligent systems in this context offers an opportunity to create highly functional healthcare supportive systems that closely and continuously match users’ needs and capabilities. This project seeks to apply machine learning (ML) techniques to the problem of identifying how the needs of healthcare device users (patients and healthcare professionals) evolve and alter the operation of these devices to better match their new requirements.
In the proposed programme of work the goal will be to develop these new intelligent controller frameworks. The first step will be to identify the best sources of user information to capture current and future needs. The data can vary from data collected by sensors like motion information, EMG, ECG, video etc. to even qualitative and medical data, e.g. GP observations, test results etc. Following the identification of the best data sources ML techniques can be applied, these will involve regression, statistical and nearest neighbours, classification trees, Bayesian methods, as well as neural network and potentially deep learning. Based on the predictions generated by these methods new directives will be given to the device controller that will allow it to intelligently react and offer an improved level of support. The final stage of the project will be to intergrade these directives with the controller of the device.
The project will investigate and offer new methods of development for healthcare devices that can adapt and evolve over time. This is crucial at this time and age where the burden to healthcare systems around the world has increased with demand. By offering new tools future development of devices can be accelerated and novel ideas reach the market faster and benefit patients earlier. The type of devices that will benefit from this work are exoskeletons, motion assistance devices etc. Moreover, this project can provide new development for other kind of medical devices usually deployed in secondary and tertiary, like medical orthopaedic and laparoscopic robots, specialised drug delivery devices etc.
This project is associated with the UKRI CDT in Accountable, Responsible and Transparent AI (ART-AI), which is looking for its first cohort of at least 10 students to start in September 2019. Students will be fully funded for 4 years (stipend, UK/EU tuition fees and research support budget). Further details can be found at: http://www.bath.ac.uk/research-centres/ukri-centre-for-doctoral-training-in-accountable-responsible-and-transparent-ai/
Desirable qualities in candidates include intellectual curiosity, a strong background in maths and programming experience.
Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree. A master’s level qualification would also be advantageous.
Informal enquiries should be directed to Dr Ioannis Georgilas on email address [email protected]
Enquiries about the application process should be sent to [email protected]
Formal applications should be made via the University of Bath’s online application form for a PhD in Computer Science: https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUCM-FP01&code2=0013
Start date: 23 September 2019.
ART-AI CDT studentships are available on a competition basis for UK and EU students for up to 4 years. Funding will cover UK/EU tuition fees as well as providing maintenance at the UKRI doctoral stipend rate (£15,009 per annum for 2019/20) and a training support fee of £1,000 per annum.
We also welcome all-year-round applications from self-funded candidates and candidates who can source their own funding.