In order to gain insight into skiing performance, it is necessary to determine the point where each turn begins. Recent developments in sensor technology have made it possible to develop simpler automatic turn detection methodologies, however they are not feasible for regular use. The aim of this study was to develop a sensor set up and an algorithm to precisely detect turns during alpine ski, which is feasible for a daily use. An IMU was attached to the posterior upper cuff of each ski boot. Turn movements were reproduced on a ski-ergometer at different turn durations and slopes. Algorithms were developed to analyze vertical, medio-lateral, anterior-posterior axes, and resultant accelerometer and gyroscope signals. Raw signals, and signals filtered with 3, 6, 9, and 12 Hz cut-offs were used to identify turn switch points. Video recordings were assessed to establish a reference turn-switch and precision (mean bias = 5.2, LoA = 51.4 ms). Precision was adjusted based on reference and the best signals were selected. The z-axis and resultant gyroscope signals, filtered at 3Hz are the most precise signals (0.056 and 0.063 s, respectively) to automatically detect turn switches during alpine skiing using this simple system.

In alpine skiing, four commonly used turning styles are snowplow, snowplow-steering, drifting and carving. They differ significantly in speed, directional control and difficulty to execute. While they are visually distinguishable, data-driven classification is underexplored. The aim of this work is to classify alpine skiing styles based on a global navigation satellite system (GNSS) and inertial measurement units (IMU). Data of 2000 turns of 20 advanced or expert skiers were collected with two IMU sensors on the upper cuff of each ski boot and a mobile phone with GNSS. After feature extraction and feature selection, turn style classification was applied separately for parallel (drifted or carved) and non-parallel (snowplow or snowplow-steering) turns. The most important features for style classification were identified via recursive feature elimination. Three different classification methods were then tested and compared: Decision trees, random forests and gradient boosted decision trees. Classification accuracies were lowest for the decision tree and similar for the random forests and gradient boosted classification trees, which both achieved accuracies of more than 93% in the parallel classification task and 88% in the non-parallel case. While the accuracy might be improved by considering slope and weather conditions, these first results suggest that IMU data can classify alpine skiing styles reasonably well.

Recent developments in sensing technology have made wearable computing smaller and cheaper. While many wearable technologies aim to quantify motion, there are few which aim to qualify motion. (2) To develop a wearable system to quantify motion quality during alpine skiing, IMUs were affixed to the ski boots of nineteen expert alpine skiers while they completed a set protocol of skiing styles, included carving and drifting in long, medium, and short radii. The IMU data were processed according to the previously published skiing activity recognition chain algorithms for turn segmentation, enrichment, and turn style classification Principal component models were learned on the time series variables edge angle, symmetry, radial force, and speed to identify the sources of variability in a subset of reference skiers. The remaining data were scored by comparing the PC score distributions of variables to the reference dataset. (3) The algorithm was able to differentiate between an expert and beginner skier, but not between an expert and a ski instructor, or a ski instructor and a beginner. (4) The scoring algorithm is a novel concept to quantify motion quality but is limited by the accuracy and relevance of the input data.

Background: Breakdancing or breaking will enter the Olympics in 2024, however, there is a paucity of literature exploring the epidemiology, demands, and performance. Purpose: The purpose of this study was to describe injury and training profiles, along with the results of a short performance test battery, in a group of elite breakers. Study Design: Cross-sectional study (retrospective). Methods: Fourteen breakdancers (breakers) (4 Bgirls, 10 Bboys) participated in an interview regarding their injury and training history, endurance test (cycle VO_2max testing), counter movement jump, squat jump, drop jump, isometric hip abduction, adduction, shoulder external and internal rotation strength testing on a fixed-frame dynamometer. Breakers were divided into elite (n=10) and developing (n=4) based on their qualification for a world finals competition; Wilcoxen rank sums were used to compare the two groups, or in the case of strength testing between those with and without an injury history. Results: The breakers had a median 11.0 [10.0 - 14.0] years breaking experience and trained 24.4 [20.5 - 30.0] hours per week. The knee was the most commonly injured body part and most frequently injured joint, with the thigh being the most common site for muscle injuries. There were no differences in endurance testing or jump height testing results between elite and developing breakers. There was no difference in shoulder external or internal rotation strength between athletes with a history of shoulder injury and those without. Similarly, there was no difference in hip abduction or adduction strength in those with a history of hip injury and those without. Conclusion: The results of this study should be viewed with caution due to the small sample size. However, this study is the first to publish functional and physiological descriptives on breakers. The authors hope these results support clinicians treating breakers as well as encourages future research related to breaking. Level of Evidence: 2b

Recent studies have developed wearable sensor systems to detect, classify and evaluate performance during alpine skiing. In order to enrich skiing data to provide motion quality feedback, edge angle (EA) and radial force (F_r) are parameters of interest. However, the estimation of these parameters via calibration‐free wearable technologies has not been validated. The purpose of this study was to develop and validate a wearable method to estimate EA and F_r. Participants completed simulated skiing trials on an indoor skiing carpet. Two IMU's mounted to the ski boots estimated EA and F_r and compared to reference values measured with a 3D motion capture system. The performance of the wearable system was quantified by accuracy and precision. The overall accuracy and precision of the wearable system was 97.6 ± 12.4% and 15.5 ± 17.6% for EA, and 105.5 ± 5.7% and 29.8 ± 10.0%, respectively for F_r. The developed wearable system was accurate for the estimation of EA and F_r, but was highly variable with low precision for both metrics. Further research is needed to improve the precision of field‐relevant skiing metrics during in‐field studies using simple measurement setups that can easily be implemented by recreational and expert skiers alike.Highlights IMU's mounted on the boots are sufficient tools for accurate estimation of edge angle and radial force during both long and short style turns on a skiing simulator. As the estimation of edge angle and radial force are dependent on other estimated parameters (i.e. turn switch), the precision of these metrics is relatively low. The results of the current study apply only to simulated alpine skiing on a treadmill, and further work is required to prove the accuracy and precision of this system on snow.

The instant of turn switch (TS) in alpine skiing has been assessed with a variety of sensors and TS concepts. Despite many published methodologies, it is unclear which is best or how comparable they are. This study aimed to facilitate the process of choosing a TS method by evaluating the accuracy and precision of the methodologies previously used in literature and to assess the influence of the sensor type. Optoelectronic motion capture, inertial measurement units, pressure insoles, portable force plates, and electromyography signals were recorded during indoor treadmill skiing. All TS methodologies were replicated as stated in their respective publications. The method proposed by Supej assessed with optoelectronic motion capture was used as a comparison reference. TS time differences between the reference and each methodology were used to assess accuracy and precision. All the methods analyzed showed an accuracy within 0.25 s, and ten of them within 0.05 s. The precision ranged from ~0.10 s to ~0.60 s. The TS methodologies with the best performance (accuracy and precision) were Klous Video, Spörri PI (pressure insoles), Martinez CTD (connected boot), and Yamagiwa IMU (inertial measurement unit). In the future, the specific TS methodology should be chosen with respect to sensor selection, performance, and intended purpose.