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Research activities

Biomechanics

Biomechanics is concerned with the study of human joints and the musculo-skeletal system. We are concerned with understanding the function of the musculoskeletal system, and pursue this via anatomical, experimental functional, clinical and computer-simulation methods.

A main interest is in developing better orthopaedic surgical procedures. This includes stress analysis of novel designs of joint replacement implants, development of novel 'tissue engineered' materials to replace damaged tissue (e.g. bone), and mechanical evaluation of methods to reconstruct injured knee ligaments. We also develop clinical testing/diagnostic methods, principally via specialised imaging or kinematic means.

For further information, visit the Biomechanics Group website.

Biomechatronics Lab

The ³Ô¹ÏºÚÁÏ Biomechatronics Laboratory was established in 2011 within the Department of Mechanical Engineering to addresses theoretical and experimental challenges posed by the application of robotic and electro-mechanical systems to real-world issued faced in medicine and biology. Mechatronics is the synergistic combination of precision engineering, electronic control, and systems thinking in the design of products and manufacturing processes.

For further information, visit the  website. 

Mechatronics in medicine

Mechatronics in medicine is developing a number of robotic and mechatronic systems to aid in medical and surgical tasks. In addition, it is also involved in basic research into the aspects of control, surgical planning and active constraints required to make these robots a reality.

For further information, visit the Mechatronics in Medicine Group website.

Magnetic Resonance Imaging (MRI)

Multidisciplinary research involves disciplines including magnetic design, MRI systems, control, signal processing, engineering analysis and design, shape modelling.

For further information, view the magnetic resonance imaging pages.

Autonomous Systems Group

The Autonomous Systems Group develops safe‑by‑design control methods for autonomous systems, with the aim of providing rigorous safety and performance guarantees in real‑world operation. The research focuses on principled and modular control design pipelines that integrate data‑driven modelling, uncertainty‑aware control, and optimisation‑based decision making. It lies at the intersection of control theory, machine learning, optimisation, and robotics, with particular emphasis on applications to autonomous robots and medical systems.

For further information, visit the website.

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