Unobtrusive Sensing for Care Applications

This research programme will investigate the use of RF and microwave sensing for practical, care-driven technologies that are fit for people in later life. In particular, this means exploring and developing data-driven IoT platforms that can produce accurate data about instant events (e.g. vital signs and serious incidents such as falls), short-term activities (e.g. those of daily living) and long-term pursuits (e.g. physical and mental activities over weeks and months) in order to extract (predictive) information and patterns that can be used, among other things, for effective interventions and the prevention of adverse outcomes. 

We will create a community for collaboration that will provide the long-term context for the development and evaluation of multiple new technologies of care in a collaboration between academics from several disciplines, people in later life and their families, health and social care professionals, and businesses in this market.  The collaboration will be formed around the implementation of routine monitoring in a highly vulnerable community in order to detect deterioration early, with a process to inform the focus of future work. We will both make a contribution to knowledge and practice, and support long-term development of new technical innovation.

We will investigate the use of a number of available sensing methods to validate their effectiveness in capturing key physiological parameters, as well as, detect and predict the onset of falls through a number of key parameters. In the first instance, sensors will be embedded into fixed objects. Another approach would be to investigate integration of these sensors with personal items, such as bedsheets, clothes, patches, and other wearable items that are available in the care environment. 

We will be working in close collaboration with the Advanced Care Research Centre on WP6: New Technologies of Care. Specifically on Task 2, which will allow us to implement our developed RF sensors and evaluate their performance amongst other sensors and devices.


Recent Publications:

  • Gillani, Nazia, and Tughrul Arslan. "Intelligent Sensing Technologies for the Diagnosis, Monitoring and Therapy of Alzheimer’s Disease: A Systematic Review." Sensors 21.12 (2021): 4249.