Human biomechanics research for better health solutions.
The Kedgley Lab is working to build our understanding of the mechanics of human upper limb in an effort to provide more efficient and effective methods of diagnosis and treatment for patients.
The Kedgley Lab employs a variety of in-vivo and in-vitro approaches to examine the effects of ageing and joint pathologies in the upper limb. These techniques include both experimental and computational methods, employing optical motion analysis, imaging, and experimental testing. We aim to work with clinicians to aid in the assessment of therapeutic interventions and the development of new techniques to promote early diagnosis of joint pathologies, leading to preventative rather than reactive treatment strategies.
Our in vivo research
Using tools such as optical motion capture, EMG, radiography, and ultrasound, we work with clinicians to quantify novel ways to quantify function, assess performance during everyday activities, and improve surgical and therapeutic interventions, such as orthoses.
Our in vitro research
A key feature of our in vitro research is the design and use of physiological simulators. These devices enable us to recreate healthy or pathological motion of the hand and wrist under intact, injured, and surgically repaired conditions. Our control algorithms generate motion based on targeted outcomes of movement or force, without any pre-determined relationships between the muscle forces that drive the motion. With our clinical collaborators, our simulators have been used to investigate the effects of surgical techniques on muscle and joint biomechanics.
Our in silico research
To study parameters that we can’t measure directly, such as the muscle and joint forces that occur during movement, we use computer models. Our musculoskeletal model has been developed in-house using the most complete anatomical dataset currently available of the human hand. To examine the interplay between bone shape, stability, and load transmission at the joints, we use statistical shape models. These enable us to look for patterns in large datasets; for example, some of our current work is investigating potential links between osteoarthritis progression and joint shape.