SCAPHOID AND LUNATE CARPAL MECHANICS OVER THE SPECTRUM OF HEALTHY FUNCTION

When ligaments within the wrist are damaged, the resulting loss in range of motion and grip strength can lead to reduced earning potential and restricted ability to perform important activities of daily living. Left untreated, ligament injuries ultimately lead to arthritis and chronic pain. Surgical repair can mitigate these issues but current procedures are often non-anatomic and unable to completely restore the wrist’s complex network of ligaments. An inability to quantitatively assess wrist function clinically, both before and after surgery, limits the ability to assess the response to clinical intervention. Previous work has shown that bones within the wrist move in a similar pattern across people, but these patterns remain challenging to predict and model. In an effort to quantify and further develop the understanding of normal carpal mechanics, we performed two studies using 3D in vivo carpal bone motion analysis techniques. For the first study, we measured wrist laxity and performed CT scans of the wrist to evaluate 3D carpal bone positions. We found that through mid-range radial-ulnar deviation range of motion the scaphoid and lunate primarily flexed and extended; however, there was a significant relationship between wrist laxity and row-column behaviour. We also found that there was a significant relationship between scaphoid flexion and active radial deviation range of motion. For the second study, an analysis was performed on a publicly available database. We evaluated scapholunate relative motion over a full range of wrist positions, and found that there was a significant amount of variation in the location and orientation of the rotation axis between the two bones. Together the findings from the two studies illustrate the complexity and subject specificity of normal carpal mechanics, and should provide insights that can guide the development of anatomical wrist ligament repair surgeries that restore normal function.

The application of PCA as a movement pattern recognition technique: A proof of principle

Quantitative methods can help us understand how underlying attributes contribute to movement patterns. Applying principal components analysis (PCA) to whole-body motion data may provide an objective data-driven method to identify unique and statistically important movement patterns. Therefore, the primary purpose of this study was to determine if athletes’ movement patterns can be differentiated based on skill level or sport played using PCA. Motion capture data from 542 athletes performing three sport-screening movements (i.e. bird-dog, drop jump, T-balance) were analyzed. A PCA-based pattern recognition technique was used to analyze the data. Prior to analyzing the effects of skill level or sport on movement patterns, methodological considerations related to motion analysis reference coordinate system were assessed. All analyses were addressed as case-studies. For the first case study, referencing motion data to a global (lab-based) coordinate system compared to a local (segment-based) coordinate system affected the ability to interpret important movement features. Furthermore, for the second case study, where the interpretability of PCs was assessed when data were referenced to a stationary versus a moving segment-based coordinate system, PCs were more interpretable when data were referenced to a stationary coordinate system for both the bird-dog and T-balance task. As a result of the findings from case study 1 and 2, only stationary segment-based coordinate systems were used in cases 3 and 4. During the bird-dog task, elite athletes had significantly lower scores compared to recreational athletes for principal component (PC) 1. For the T-balance movement, elite athletes had significantly lower scores compared to recreational athletes for PC 2. In both analyses the lower scores in elite athletes represented a greater range of motion. Finally, case study 4 reported differences in athletes’ movement patterns who competed in different sports, and significant differences in technique were detected during the bird-dog task. Through these case studies, this thesis highlights the feasibility of applying PCA as a movement pattern recognition technique in athletes. Future research can build on this proof-of-principle work to develop robust quantitative methods to help us better understand how underlying attributes (e.g. height, sex, ability, injury history, training type) contribute to performance.