Available Projects
Detecting and tracking changes in behaviour for health and wellbeing
In the current model of care, where people are living healthier and for longer, care is taken out of the clinics and into the home. There is a need for monitoring either healthy (or healthy but "vulnerable") people, or those managing one or more conditions, in the home and unobtrusively - this project concerns the development of algorithms to do just that.
Primary supervisor: Professor Christopher James - Email: c.james@warwick.ac.uk
Project detail:
As humans, as we go about our daily lives, we go through patterns of behaviour - be that through our activities of daily living (sleeping, eating, working , playing, etc) or through our interaction with the world - either way our patterns of behaviour can be linked to our wellbeing. More so, if we can detect - or even predict - changes in these patterns, we can predict changes in behaviour and thus changes in well-being (or wellness - or even illness). This project concerns the analysis of data extracted from a variety of measures of day-to-day living- from simple room occupancy sensors, to complex measures of behaviour and health that can be extracted from so-called smart wearables such as smart watches (or even more sophisticated measures such as from brain activity). This analysed data is used to learn patterns of behaviour, these patterns can be used to define healthy or "normal" activity signatures. With such signatures it then becomes possible to track change, and when applied to particular patient groups (e.g. allowing older people to live independently for longer, or people with managed mental health conditions) it become possible to track and even predict possible "good" or "bad" episodes. This project builds on previous work in this field developing machine learning based techniques that learn from data as it is developed over time. The algorithms that are developed will be tested on two distinct cohorts: a) using behaviour monitoring to promote and improve well-being and b) using behaviour monitoring on a group of mental health/ intellectual difficulty patients to track health trajectories. Ultimately this would result in algorithms that can be used to promote healthy living, in the home, and in a diverse population or healthy individuals as well as those with managed (health) conditions.
The University of Warwick provides an inclusive working and learning environment, recognising and respecting every individual’s differences. We welcome applications from individuals who identify with any of the protected characteristics defined by the Equality Act 2010.