I am a researcher focused on developing computational and neuromuscular models, digital twins, and intelligent control strategies to support and restore human movement. My work combines biomechanics, signal processing, and human-robot interaction to create personalised, adaptive solutions for rehabilitation and assistive technology.

My primary research interests involve modelling and optimising FES-based and wearable-assisted movement, with applications in cycling, rowing, gait, and functional recovery for individuals with spinal cord injury and stroke. I develop neuromusculoskeletal simulations, predictive models, and real-time controllers to understand lower limb dynamics better and improve performance and safety in both experimental and clinical settings.

Across my projects, I aim to bridge the gap between computational models and practical rehabilitation by advancing digital twin frameworks that integrate motion data, EMG, and wearable sensor data. My goal is to build technologies that enhance autonomy, facilitate motor recovery, and expand access to personalised rehabilitation through evidence-based, computationally informed design.