Smart Percussion for automated diagnostics

The aim of this research is to develop a smart percussion device to help medical practitioners diagnose patients more efficiently, reducing the need for harmful scans and traumatising interventions. The device could be deployed outside of primary and secondary care.
The key elements in the development of this device are a phantom on which to perform percussion experiments and two different approaches to modelling the behaviour: a physics-based model developed by solving the governing equation for sound propagation, and a machine-learning based model developed by training a classifier using phantom observations.

The project falls under Digital Economies and Healthcare Technologies themes of EPSRC and under Healthcare Technologies Grand Challenges. It would contribute directly by supporting the development of novel devices to enhance the efficacy of diagnosis, minimising costs while reducing risk to patients.