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The application of principal component analysis to quantify technique in sports
Author(s) -
Federolf P.,
Reid R.,
Gilgien M.,
Haugen P.,
Smith G.
Publication year - 2014
Publication title -
scandinavian journal of medicine and science in sports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.575
H-Index - 115
eISSN - 1600-0838
pISSN - 0905-7188
DOI - 10.1111/j.1600-0838.2012.01455.x
Subject(s) - principal component analysis , movement (music) , perspective (graphical) , focus (optics) , point (geometry) , computer science , athletes , functional principal component analysis , contrast (vision) , principal (computer security) , physical medicine and rehabilitation , statistics , psychology , artificial intelligence , mathematics , physical therapy , medicine , philosophy , physics , geometry , optics , operating system , aesthetics
Analyzing an athlete's “technique,” sport scientists often focus on preselected variables that quantify important aspects of movement. In contrast, coaches and practitioners typically describe movements in terms of basic postures and movement components using subjective and qualitative features. A challenge for sport scientists is finding an appropriate quantitative methodology that incorporates the holistic perspective of human observers. Using alpine ski racing as an example, this study explores principal component analysis ( PCA ) as a mathematical method to decompose a complex movement pattern into its main movement components. Ski racing movements were recorded by determining the three‐dimensional coordinates of 26 points on each skier which were subsequently interpreted as a 78‐dimensional posture vector at each time point. PCA was then used to determine the mean posture and principal movements ( PM k ) carried out by the athletes. The first four PM k contained 95.5 ± 0.5% of the variance in the posture vectors which quantified changes in body inclination, vertical or fore‐aft movement of the trunk, and distance between skis. In summary, calculating PM k offered a data‐driven, quantitative, and objective method of analyzing human movement that is similar to how human observers such as coaches or ski instructors would describe the movement.