During alpine skiing turns, skiers turn by generating an angle between the ski edges and snow (edge angle (EA)), and by applying force to the ski (Federolf et al., 2010a).Modern carving skis are shaped with the widest part at the tip, the narrowest part underneath the boot, and a slightly wider part at the tail. When skiers increase the force and angle between the ski and the snow, the ski is bent creating a curve. As this curve tracks through the snow, the tip of the ski creates a groove in the snow, which the tail follows, steering the ski through the snow and creating a "self-steering" effect (Federolf et al., 2010b, Jentschura and Fahrbach, 2004). As EA increases, the turn radius produced by this effect decreases. Even when this self-steering effect does not take place, and the skier drifts the skis through the snow, the EA still determines the size and shape of the ski turn (Müller and Schwameder, 2003). Given this critical relationship between EA and turn radius, EA measurement is a useful parameter for coaches or instructors to provide feedback about skiing ability or turning quality (Hebert-Losier et al., 2014). However, the measurement of kinematics such as EA in the field can be difficult (Yu et al., 2016, Spörri et al., 2012a, Spörri et al., 2012b, Spörri et al. 2016a, Spörri et al., 2016b). While laboratory based motion capture methods have the distinct advantage of high accuracy and precision, they are not easily applied in field settings as they are limited to small capture volumes, and require calibration or reference points within the capture volume (Spörri et al., 2016c). Recently, Inertial Measurement Units (IMU) have gained popularity as a field based motion capture option, especially in alpine skiing (Camomilla et al., 2016, Supej, 2010). These Systems have the advantage of capturing consecutive turns or turn sequences continuously, while being relatively non-obtrusive to users due to their small size and wireless function. Previous studies using IMU based motion capture for alpine skiing have reported accuracy between -3.3° to 4.2° and precision between 2.6° to 3.6° for lower extremity frontal plane joint angles during skiing simulations on a ski treadmill (Fasel et al., 2013). When a similar methodology was applied in the field, this group observed accuracy and precision of shank inclination, a Surrogate measure of EA, of 1.1° ± 3.6° and 2.6° ± 1.0° respectively for the inside leg, and -1.8° ± 3.5° and 2.9° ± 1.2° respectively for the outside leg (Fasel et al., 2018). While these values represent a high degree of accuracy and precision, the system proposed by Fasel and colleagues (2018) is notfully automated. It requires multiple IMU`s placed on relevant segments, as well as functional calibration procedures and post-processing to align sensor coordinate Systems with anatomic or global coordinate Systems and correct for gyroscope drift. Therefore, such a system would be usable in a research setting, but not by everyday recreational users. Recently, Martinez and colleagues (2019a/2019b) proposed a system of IMU`s mounted to the posterior cuff of each ski boot to automatically detect and segment turns and turn sequences during alpine skiing (Martinez et al., 2019a, Martinez et al., 2019b). This system provided a framework for EA estimation on a turn-by-turn level. Therefore, the aim of the present study was to develop and validate a wearable system for fully automated EA estimation during simulated alpine skiing.
© 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