シングルスカリングにおける機能的クラスタリングを用いたローイングバイオメカニクスの評価

Rowing technique to achieve optimal boat velocity depends on individual rowing style. Traditionally, quantification of rowing technique has involved discrete point analysis, limiting the understanding and interpretation of the stroke cycle with data loss occurring between the reported metrics. However, higher dimensional statistical approaches, such as functional data analysis (FDA), can facilitate enhanced understandings of temporal patterns within time series data such as force and acceleration profiles. The aim of the study was to distinguish technique characteristics during single sculling using a novel functional clustering method considering the whole stroke cycle for analysis. Twenty-five elite rowers (12 females, 25 ± 2.5 years and 13 males, 27 ± 2.8 years) completed an on-water single sculling biomechanics assessment. Gate force, foot-stretcher force and boat acceleration were independently fitted with a clustering model, with separate models created for each gender. Boat acceleration exhibited the most variability of the three independent variables in cluster group patterns and individual rowers. Results revealed more than one approach to achieving optimal boat velocity at the elite level and technical coaching strategies should be based on the individual rather than attempting to replicate successful elite rowers' technique who may exhibit a different set of physical and technical attributes.