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.

Rowing Biomechanics: Technique Changes with an Increase of Power Demand

The sport of rowing has become more popular in the past decade. While it is a relatively low impact sport, injuries can occur, specifically to the ribs (Karlson K. A., 1998) and more often in female athletes (Hickey, Fricker, & McDonald , 1997). It has been proposed that as the athlete rows, applying a cyclical load to the body, the mid trapezius fatigues and is unable to resist the force produced during the drive phase (Warden S. J., Gutschlag, Wajswelner, & Crossley, 2002). Once this happens, the scapulae are then pulled anterio-laterally which increases the compression force on the ribs, increasing the risk of injury. The rowing motion of 12 female varsity and club rowers was tracked as they completed a fatiguing rowing test on a rowing ergometer. Results showed that the curvature of thoracic spine changed throughout the rowing cycle but did not change with increasing power level. The transverse shoulder angle decreased (the upper back was less straight) as power level increased (R2=-0.69±19), suggesting that the scapula moved anterio-laterally. This may be that as it tired, the mid-trapezius was unable to hold the scapulae in position. The decreasing transverse shoulder angle when the power level is increased indirectly supports the fatiguing of the retractor muscles as a mechanism of injury. It would be valuable to understand the limitations of each athlete and to be able to prescribe the optimal training zone to reduce the risk of injury.