Computing, Informatics and Applications Research Group in collaboration with the Exeter Biomechanics Research Team (ExBiRT), University of Exeter
Proposed supervisory team
Dr Domenico Vicinanza
Dr Genevieve Williams (University of Exeter)
Dr Jin Zhang
Wireless sensors, Remote Sensing and Internet of Things, data sonification, coaching and rehabilitation
Summary of the research project
This research project is part of a series of activities carried out with Cambridge Centre for Sport and Exercise Sciences, using smart sensors/wireless sensors and audio analysis in biomechanics and biomedical sciences.
Remote sensors and Wireless Sensor Networks (WSN) are increasingly being applied to retrieve data from environmental measurements to motion and position tracking. Remote sensing provides the potential to collect data at spatial and temporal scales that could be either not feasible or difficult to implement with existing instrumentation. While remote sensing and wireless sensing is currently accepted as an adequate mechanism to gather remote data and share it over networks, very little has been currently done in the actual deployment of networked sensor-based infrastructures for sport and rehabilitation applications.
When coupled with remote sensing and networks, data sonification (mapping/converting measurements to audio signals) can provide physicians, physiotherapists and sport patients with uniquely effective ways to analyse data and provide accurate and personalised feedback without having to travel to a particular hospital. Consultants can analyse sonograms generated by the sonification of sensors in real-time from anywhere in the world and give immediate and accurate feedback.
Remote access to data and measurements are especially relevant when dealing with rehabilitation. While working with injured patients, having the possibility of thoroughly assessing the progress of a certain therapy, measuring in a quantitative way the success of a surgery can have a huge impact on the patience prognosis. Data sonification can display extremely accurately the progress of recovery in terms of subtle changes in spectral lines of kinematic/kinetic sensor audification.
Where you'll study
This project is self-funded.
Details of studentships for which funding is available are selected by a competitive process and are advertised on our jobs website as they become available.
If you wish to be considered for this project, you will need to apply for our Computer and Information Science PhD. In the section of the application form entitled 'Outline research proposal', please quote the above title and include a research proposal.