Human-Robot Interaction for dementia prevention and research with a focus on cognitive well-being

There is an increasing trend in the incidence of dementia, and, particularly Alzheimer's disease. This, along with its current prevalence and the socioeconomic burden it poses, is making its treatment and, even more, its prevention, a major worldwide public health challenge.

This project will investigate the use of novel interactive computing technology, such as human-robot interaction to gather physiological and cognitive data, and new approaches for data analysis to answer a number of interrelated questions that will help dementia prevention.

In order to accomplish this, the first part of the project will consist in learning the state-of-the-art on assistive social robotics: what has been done (classically and recently), what is needed, how would I contribute given my background, what extra steps would I need to take from a theoretical perspective. At the same time, acknowledging and thoroughly understanding the methodological side of these procedures will be key for the project. A number of machine learning and speech processing techniques such as part-of-speech analysis, latent semantic analysis, principal component analysis, Bayesian probability approaches, etc., will be introduced and practised on existing databases using different programming languages. These methods are expected to point out key features of participants' behaviour that may tell us their probability to suffer future dementia, or help us distinguish patients from healthy controls earlier than we do now. Particularly, we might look at speech coherence indexes to test this, first using existing datasets like DementiaBank or Carolina Conversations Collection, and afterwards, using our own data.

The second part of the project will involve designing different experiments on human-robot interaction, gather relevant data and implement the methods mentioned above. For this, we will need to design the experiments according to ethical and clinical constrains, and then we will ask for appropriate advice and carefully decide which robotic platform would be suitable for the planned experiments.

In the third part, we will record large amounts of speech and gesture data, and employ the methods tested during part 1 to analyse these data. We will refine the robot's interaction model iteratively, taking into account the feedback gathered during the experiments in order to improve user engagement and the data collection methods as necessary.