Human-Robot Interaction for Dementia Prevention and Research

The prevalence and the trend towards substantial increases in the incidence of dementia make its prevention a major societal challenge. Cognitive activity, physical activity and social engagement are among the strategies identified as promising for dementia prevention [3]. From a research perspective, major efforts are underway which seek to implement targeted collection of observational data in order to assemble knowledge that can be translated into new interventions, in terms of therapy as well as public health. Interactive computing and robotics technology could contribute significantly to these objectives.

Developments in computing technology have opened new opportunities for assessing the health and cognitive well-being of older persons through embedded sensors. This can be done longitudinally, more conveniently and more frequently than is currently possible with existing procedures. Multimodal signal processing and machine learning methods have been successfully employed in different areas for inference of high-level interaction data [2], and could be used for aggregating and analysing data in assisted living settings. In separate developments, assistive social robots [1] have been increasingly used in elderly care, targeting psychological well-being and maintenance of cognitive function, as well as novel therapies for dementia patients [4].

The overall goal of this project is to investigate novel approaches for gathering detailed physiological and cognitive data through human-robot interaction, and assess the applicability of the resulting data sets to dementia prevention research. More specifically, it will seek to answer a number of interrelated questions, including:

• How to design human-robot interaction systems that are engaging and can serve as a means for interaction and cognitive stimulation to the participants,
• How health and well-being related data can be collected through the robot, in the context of its interaction with the user. These data will typically include physiological data, possibly acquired through sensors built into the robot, and cognitive data, possibly acquired through speech, gaze and gesture signals acquired through microphones and cameras as the user engages in interaction with the robot.
• How these data collected through daily-life interaction between older persons and robots could be incorporated into larger datasets, for research and health monitoring purposes.
• How the resulting data and technologies can underpin the development of methods for predicting health and well-being trajectories of participants.

The PhD student will gain knowlwdge of advanced interactive systems for assistive social robotics, and machine learning methods to infer the relative importance of different categories of participant-generated signals in predicting health and well-being trajectories. The PhD student will also be trained in methods for aggregation of different sensor and cognitive (e.g. speech, language) data, and devise strategies for integrating such data into existing population data resources.

Application
This MRC DTP programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

You can apply here via the University of Glasgow: http://www.gla.ac.uk/research/opportunities/howtoapplyforaresearchdegree/
Within the application, at the programme of study search field option, please select ‘MRC DTP in Precision Medicine’.

Please note that, in step 6 within the online application process, you are asked to detail supervisor/project title information. Please ensure that you clearly detail this information from the information provided within this abstract advert. Within the research area text box area, you can also add further details if necessary.

Please ensure that all of the following supporting documents are uploaded at point of application:
• CV/Resume
• Degree certificate (if you have graduated prior to 1 July 2016)
• Language test (if relevant)
• Passport
• Personal statement
• Reference 1 (should be from an academic who has a knowledge of your academic ability from your most recent study/programme)
• Reference 2 (should be from an academic who has a knowledge of your academic ability)
• Transcript

For more information about Precision Medicine at the University of Edinburgh, visit http://www.ed.ac.uk/medicine-vet-medicine/postgraduate/research-degrees/phds/precision-medicine