Biomechanical analyses of alpine skiing performance typically separate data into turns to define the cyclic motor abilities of the athlete. Detecting the beginning and end of a turn is a challenging taskfor researchers and mainly depends on the definition chosen and the technologies used. When aiming to detect the moment of switching from one turn to another, there are two main approaches: 1) consider the end of a turn at the beginning of the proceeding one (Approach 1), in which a ski run is strictly an alternation of right and left turns separated by a turn switch (TS) event, and 2) a straight-line transition period is considered between two turns, which defines the alternation between straight and turn phases as separate. These two approaches can be implemented on both kinetic and kinematic data signals. The current `gold-standard` in kinematic measurement techniques (relevant to Approach 1) projects the skier`s center of mass (COM) onto the snow surface and detects a TS when COM and ski trajectories cross paths (Supej & Nemec, 2003). This event corresponds to the switch of skier`s direction of inclination. While accurate, this method requires a complex set up of 3D motion capture cameras and post processing. Consequently, analyses of more than a few turns are difficult. COM and ski trajectory can also be estimated using inertial measurement units (IMU) and Global Navigation Satellite Systems (GNSS) (Fasel et al., 2016); these sensors are accurate but similarly require complex data processing. A less computationally demanding method of detecting the TS is to use GNSS data to determine the inflexion point (Trajinfi) of the COM trajectory (Adelsberger et al., 2014). Notably, this method is comparable to the gold-standard (lag <30 ms; Fasel & Gilgien, 2017) and can provide data over much greater course lengths. Finally, kinematic data can be used to detect TS events via Approach 2; namely, the COM trajectory straight and turn phases can be separated when the turn radius of COM crosses 30m (Traj>30, Traj<30) (Spörri et al., 2012). These events correspond to the end or the beginning of substantial COM direction change, which can be subsequently linked to the beginning and the end of the turn phase. While less common, TS events can be computed using kinetic data. The gold Standard corresponding to Approach 1 defines TS as the minimum summated normal ground reaction force (nGRF) exhibited over a given turn cycle (Fmin; Nakazato et al., 2011). As such, the TS phase is associated with a period when force applied to the skis is close to zero (i.e. an unweighting between turns), which is surrounded by large force application on the relevant outside ski (Schaff et al., 1996). Further to the specific TS moment, these data can also define distinct events within the turn phase (e.g. turn initiation) (Nakazato et al., 2011), and detecting the beginning and end of the nGRF on the outside ski (relevant to Approach 2) could be an interesting new method to define the beginning and end of a turn. Indeed, the outside ski supports higher nGRF during turn than inside ski and is mostly involved in direction change of the skier. The beginning of the force application of the outside limb induces bending of the ski (Yoneyama et al., 2008) and could be directly linked trajectory changes. There are several methods and approaches to segment a ski run into turns. Consequently, researchers or practitioners with access to multiple data streams lack guidance on which method or technology is optimal for a specific Situation or aim. Furthermore, there is sparse information regarding the link between kinetic and kinematic events at turn initiation. The aim of this study is to explore time difference between cutting turns methods and evaluate if clear relationships exist between kinematic and kinetic definitions of the beginning and the end of a turn.
© Copyright 2020 Science and Skiing VIII. Book of the 8th International Congress on Science and Skiing. Julkaistu Tekijä University of Jyväskylä; Vuokatti Sports Technology Unit of the Faculty of Sport and Health Sciences of the University of Jyväskylä. Kaikki oikeudet pidätetään. / Kääntänyt https://www.DeepL.com/Translator