ECSS Paris 2023: CP-BM14
INTRODUCTION: Youth figure skaters are often exposed to substantial overuse injury risks due to repetitive and high-impact jump practices. Previous figure skating biomechanical studies have mostly focused on jump kinematics, and there are limited studies on examining on-ice force generation patterns and impact on the lower limbs across different skill-level skaters. Therefore, this study aims to compare the ground reaction force characteristics between jumps of two different skill-level skaters. METHODS: One novice level skater and one junior level skater performed single salcow, double salcow, single loop and double loop jumps. Two high speed cameras (Sony FDR-AX700, 100Hz) were used to record skaters’ kinematics. W-INSHOE system from Medicapteur (8 sensors per foot, 100 Hz) was used for plantar pressure measurements at jump’s take-off and landing. Ground reaction force (GRF) data obtained using plantar pressure insole were normalized using body weight (BW). Peak GRF (BW) and loading rate (BW/s) between skaters were compared. Due to limited data, only descriptive analysis was conducted. RESULTS: Percent differences of take-off peak GRF between novice and junior skaters for single salcow, double salcow, single loop and double loop are 93.4%, 90.6%, 18.1% and 25.8% respectively. Percent differences of landing peak GRF between novice and junior skaters for single salcow, double salcow, single loop and double loop are 12.6%, 13.6%, 41.8% and 44.5% respectively. Percent differences of take-off loading rate between novice and junior skaters for single salcow, double salcow, single loop and double loop are 147.9%, 66.3%, 121.4% and 124.0% respectively. Percent differences of landing loading rate between novice and junior skaters for single salcow, double salcow, single loop and double loop are 76.1%, 69.0%, 48.9% and 72.3% respectively. Junior skater’s peak GRF and loading rate at jump take-off and landing are generally greater than novice skater. While junior skater’s take-off force-time curves during salcow jumps followed monomodal patterns, novice skater’s curves followed bimodal patterns. Landing force-time curves of all jumps showed similar patterns, which the landing leg’s GRF spikes upon initial contact at the landing, then slightly decreases as lower limbs flex to stabilize the body and to absorb impact force. CONCLUSION: The differences in salcow jump take-off force-time curve patterns between two skaters potentially indicate technical differences between them. Moreover, junior skater demonstrated higher peak GRF and loading rate at take-off, which indicate that junior skater applies more explosive and faster force generation to achieve greater take-off vertical velocity and jump height. However, higher impact forces especially at the landing also indicate higher risks of lower limb injuries. Novice skaters are in earlier stage of their technical development and focused more on controlled force distribution rather than explosiveness. Thus, they may benefit from more strength training.
Read CV Ami KogaECSS Paris 2023: CP-BM14
INTRODUCTION: Big Air training relies heavily on video-based methods. While video analysis provides rich qualitative information, it requires multiple camera setups, expert interpretation, and time for processing and analysis, limiting its use during independent training sessions. Wearable inertial sensors offer a complementary approach by enabling automated, objective, and easy-to-use analysis without external infrastructure. The aim of this study is to evaluate whether jump types in elite Big Air can be classified using boot-mounted IMU data. METHODS: Five professional male freestyle skiers performed Big Air jumps during training sessions. IMU data were collected bilaterally using the connected boot system [1], complemented by synchronized video recordings from four cameras. Differences in jump difficulty and athlete ability resulted in a skewed class distribution within the dataset. Jump types performed fewer than four times were excluded, resulting in a dataset comprising seven jump classes (straight, 360, cork 3, cork 5, switch cork 5, cork 7, lincoln loop). From each jump, statistical time-domain features were extracted, including mean, sd, min., max., RMS, 25th, 50th and 75th percentiles, and IQR. Models emphasizing interpretability and robustness to imbalance were evaluated, including Balanced Random Forest, XGBoost, and Extremely Randomized Trees. Hyperparameters were optimized using random search with 100 iterations and group 5-fold cross-validation. Model evaluation was performed using leave-one-participant-out cross-validation. Performance metrics included balanced accuracy (BA), weighted F1-score (wF1), and Cohen's kappa (κ), reported as mean and sd across folds. Feature importance was assessed using impurity-based and permutation-importance methods. RESULTS: Models achieved meaningful classification performance despite strong class imbalance. The Balanced Random Forest (BA=0.72, wF1=0.69, κ=0.6) showed the most stable results, while XGBoost (BA=0.66, wF1=0.66, κ=0.6) and Extremely Randomized Trees (BA=0.75, wF1=0.69, κ=0.66) achieved comparable peak performance. Misclassifications occurred predominantly between biomechanically similar jump types, such as 360 and cork 3. Gyroscope-derived features, particularly those related to rotation around the vertical and mediolateral axes, and variability-based descriptors dominated feature importance across all models, highlighting the relevance of rotational dynamics. CONCLUSION: IMU data provide a promising way for classification of Big Air ski jump types in elite athletes, even under imbalanced conditions. These findings support the use of wearable sensors as a practical tool for objective jump analysis in freestyle skiing training and performance monitoring, with future potential to extend towards scoring-related quality metrics, error detection, and more targeted feedback for motor learning and performance evaluation. [1] Snyder et al., Sensors, 2021
Read CV Sebastian MayrECSS Paris 2023: CP-BM14
INTRODUCTION: Freestyle snowboard halfpipe involves complex aerial rotations requiring precise in-flight control. Trampolines provide a safe off-snow environment to develop and stabilize these skills. Biomechanical analyses have shown distinct execution strategies and phase-dependent variability in snowboard aerials [1]. Research on expertise suggests that skill level influences how movement variability is organised and controlled [2]. This study investigates execution variability during trampoline-based snowboard rotations across different skill levels to identify differences between novice and elite athletes and explore indicators of movement consistency that may improve readiness for progression to on-snow training. METHODS: Four elite male snowboard halfpipe athletes (23 ± 4 years; 74 ± 8.8 kg; 178 ± 7.8 cm; 10 ± 9 years experience), members of the German national team competing at an international level (World Cup), performed a total of 181 tricks with rotations of 360° in different directions on a freestyle trampoline using a bounce board. Twelve novices (4 female, 8 male; 23 ± 3 years; 66 ± 6.9 kg; 174 ± 7.9 cm) with an artistic gymnastics background but no halfpipe experience performed 783 trick rotations under the same conditions. The novices learned different 360° rotational tricks through standardised video-based instruction. Body kinematics were captured using inertial motion capture (Xsens, 18 IMUs, 240 Hz). RESULTS: Analyses showed performance-level differences in airtime variability and movement amplitudes, with elite athletes displaying lower coefficients of variation (CV: 5–7%) than novices (CV: 11–17%). Rotational amplitudes were more variable in novices than elites for head motion (CV: 90–152% vs 54–90%) and hip motion (CV: 95–106% vs 79–95%), whereas flexion/extension amplitudes were lower (head: 40–56% vs 26–40%; hip: 34–42% vs 27–34%), indicating more stable coordination in elite athletes. Linear models revealed group effects for hip rotation and flexion/extension amplitudes (all p<0.01), while head amplitudes showed no group differences; peak timing occurred earlier in novices and showed strong group effects for hip and head with additional trick effects (p<0.01). CONCLUSION: Lower execution variability in elite athletes suggests more stable coordination during flight. Quantifying execution stability during trampoline training may support skill learning regarding readiness to progress to on-snow practice. [1] Bacik, B., Kurpas, W., Marszałek, W., Wodarski, P., Sobota, G., Starzyński, M., & Gzik, M. (2020). Movement variability during the flight phase in a single back sideflip (wildcat) in snowboarding. Journal of Human Kinetics, 72(1), 29–38. https://doi.org/10.2478/hukin-2019-0006 [2] Wagner, H., Pfusterschmied, J., Klous, M., von Duvillard, S. P., & Müller, E. (2012). Movement variability and skill level of various throwing techniques. Human Movement Science, 31(1), 78–90. https://doi.org/10.1016/j.humov.2011.05.005
Read CV Lina FayECSS Paris 2023: CP-BM14