Non-linear methods in statistical shape analysis have become increasingly important in orthopedic research as they allow for more accurate and robust analysis of complex shape data such as articulated joints, bony defects and cartilage loss. These methods involve the use of non-linear transformations to describe shapes, rather than the traditional linear approaches, and have been shown to improve the precision and sensitivity of shape analysis in a variety of applications. In orthopedic research, non-linear methods have been used to study a range of topics, including the analysis of bone shape and structure in relation to osteoarthritis, the assessment of joint deformities and their impact on joint function, and the prediction of patient outcomes following surgical interventions. Overall, the use of non-linear methods in statistical shape analysis has the potential to advance our understanding of the relationship between shape and function in the musculoskeletal system and improve the diagnosis and treatment of orthopedic conditions.

For many years, marker-based systems have been used for motion analysis. However, the emergence of new technologies, such as 4D scanners provide exciting new opportunities for motion analysis. In 4D scanners, the subjects are measured as a dense mesh, which enables the use of shape analysis techniques. In this talk, we will explore how the combination of the rising new motion analysis methods and shape modelling may change the way we think about movement and its analysis.