Team Lead: Brian Caulfield
In recent years we have witnessed a proliferation of sensors pervading virtually all aspects of life. This includes sensors that measure the environment in which we live or that sense humans directly from wearable or ambient devices. Our capacity to generate data from such sensors has increased faster than our capacity to generate meaning and value from that data.
Availability of data is not sufficient when addressing health or wider societal challenges. We need to enhance the processes of data acquisition to be better prepared for extracting and using knowledge from that data, with due regard to the way people need to live their lives. We then need to develop effective and safe strategies for visualising the data and using insights from it to effect behaviour change.
This poses a range of fundamental science challenges around novel forms of data acquisition (sensing) and the ability to drive a verifiable change in behaviour based on that data (actuation).
Our primary objectives in this regard are to
(i) develop the next generation of sensor platforms for accessing new sensing targets
(ii) optimise the process of data acquisition, signal processing, and knowledge extraction within different application contexts
(iii) leverage personalised and adaptive recommendation models to power effective strategies for measurement and enhancement of behaviour