Multimodal Real-Life Assessment of Functional Performance in Lower-Limb Prosthetic Users

We are developing a real-world assessment framework for lower-limb prosthetic users, funded by the EPSRC Impact Acceleration Account (IAA). The project integrates wearable sensing, single-lead ECG monitoring, and advanced machine learning to objectively quantify mobility, physiological effort, and functional performance during everyday activities. Mustafa Ahmed is leading the modelling and signal analysis components of the project. His work focuses on developing machine learning algorithms for continuous multimodal data, combining lower-limb inertial measurements with ECG-derived physiological features to estimate energy expenditure and functional load. This approach seeks to bridge biomechanics and cardiovascular response, producing clinically meaningful insights to support personalised rehabilitation and long-term prosthetic outcomes.

Selected Publications