In this study, we developed a deep learning-based 3D markerless motion capture system for skate skiing on a treadmill and evaluated its accuracy against marker-based motion capture during G1 and G3 skating techniques. Participants performed roller skiing trials on a skiing treadmill. Trials were recorded with two synchronized video cameras (100 Hz). We then trained a custom model using DeepLabCut, and the skiing movements were analyzed using both DeepLabCut-based markerless motion capture and marker-based motion capture systems. We statistically compared joint centers and joint vector angles between the methods. The results demonstrated a high level of agreement for joint vector angles, with mean differences ranging from −2.47° to 3.69°. For joint center positions and toe placements, mean differences ranged from 24.0 to 40.8 mm. This level of accuracy suggests that our markerless approach could be useful as a skiing coaching tool. The method presents interesting opportunities for capturing and extracting value from large amounts of data without the need for markers attached to the skier and expensive cameras.

This study evaluated two approaches for estimating the total propulsive force on a skier’s center of mass (COM) with double-poling (DP) and V2-skating (V2) skiing techniques. We also assessed the accuracy and the stability of each approach by changing the speed and the incline of the treadmill. A total of 10 cross-country skiers participated in this study. Force measurement bindings, pole force sensors, and an eight-camera Vicon system were used for data collection. The coefficient of multiple correlation (CMC) was calculated to evaluate the similarity between the force curves. Mean absolute force differences between the estimated values and the reference value were computed to evaluate the accuracy of each approach. In both DP and V2 techniques, the force–time curves of the forward component of the translational force were similar to the reference value (CMC: 0.832–0.936). The similarity between the force and time curves of the forward component of the ground reaction force (GRF) and the reference value was, however, greater (CMC: 0.879–0.955). Both approaches can estimate the trend of the force–time curve of the propulsive force properly. An approach by calculating the forward component of GRF is a more appropriate method due to a better accuracy.

Roller skiing is one primary form of training method as it is an off-snow equivalent to cross-country (XC) skiing during the dry land preseason training, but the results could only be applied to on-snow skiing with appropriate caution. The aim of this present study was to investigate the similarities and differences in roller skiing and on-snow skiing with the diagonal stride (DS) technique. Six youth (age: 14.3 ± 2.9 years) skiers participated in this study. Two high-definition video camcorders and FastMove 3D Motion 2.23.3.3101 were used to obtain the three-dimensional kinematic data. The cycle characteristics and joint angle ROM of the DS technique while skiing on different surfaces were similar. Almost all joint angle–time curves that were obtained from roller skiing showed a moderate-to-high degree of similarity to the angle–time curves obtained from on-snow skiing, except the hip adduction–abduction angle. The differences between roller skiing and on-snow skiing were mainly found in the body and calf anteversion angles, and the joint angles at critical instants. DS roller skiing can simulate DS on-snow skiing to a large extent in youth athletes. The hip movement, knee flexion, and calf anteversion at ski/roller ski touchdown and take-off, pole inclination at pole touchdown, body anteversion angle, and trunk anteversion angle at pole touchdown were the points that required caution when transferring preseason practice roller skiing to on-snow skiing.

Background: Most of the studies about the effects of incline on cross-country skiing are related to the metabolic efficiency. The effective skiing biomechanics has also been indicated to be among the key factors that may promote good performance. The aims of this study were to provide biomechanical characteristics and investigate the relative contribution and effectiveness of ski and pole forces in overcoming the total external resistance with double poling (DP) and Gear 3 (G3) techniques at varying moderate uphill inclines.Methods: 10 male cross-country skiers participated in this study. Custom-made force measurement bindings, pole force sensors, and an 8-camera Vicon system were used to collect force data and ski and pole kinematics at 3°, 4° and 5° with 10 km/h skiing speed.Results: The cycle length (CL) decreased by 10% and 7% with DP and G3 technique from 3° to 5° (p < 0.001,p < 0.001). The cycle rate (CR) increased by 13% and 9% from 3° to 5° with DP and G3 technique respectively. From 3° to 5°, the peak pole force increased by 25% (p < 0.001) and 32% (p < 0.001) with DP and G3 technique. With DP technique, the average cycle propulsive force (ACPF) increased by 46% (p < 0.001) from 3° to 5°and with G3 technique, the enhancement for ACPF was 50% (p < 0.001). In G3 technique, around 85% was contributed by poles in each incline.Conclusion: The higher power output in overcoming the total resistance was required to ski at a greater incline. With DP technique, the upper body demands, and technical effectiveness were increasing with incline. With G3 technique, the role of external pole work for propulsion is crucial over different terrains while role of legs may stay more in supporting the body against gravity and repositioning body segments.

Several methods could be used to measure the forces from skis or roller skis in cross-country skiing. Equipment that could measure medio-lateral forces may be of good help for investigating the relevant skating techniques. The aim of this study was to validate a pair of newly designed two-dimensional force measurement roller skis. The vertical and medio-lateral forces which were perpendicular to the body of the roller ski could be measured. Forces were resolved into the global coordinate system and compared with the force components measured by a force plate. A static and dynamic loading situation for the force measurement roller ski was performed to reveal the validity of the system. To demonstrate whether the force measurement roller ski would affect roller skiing performance on a treadmill, a maximum speed test with the V2 technique was performed by using both normal and force measurement roller skis. The force-time curves obtained by these two different force measurement systems were shown to have high similarity (coefficient of multiple correlations > 0.940). The absolute difference for the forces in the X and Z directions over one push-off cycle was 3.9–33.3 N. The extra weight (333 g) of the force measurement roller ski did not affect the performance of the skiers. Overall, the newly designed two-dimensional force measurement roller ski in this study is valid for use in future research during daily training for skate skiing techniques.

This study aimed to identify key biomechanical and physiological parameters affecting cross-country skiing performance and develop a neural network model for predicting skiing speed. Biomechanical attributes (cycle length and rate, vertical displacement of the center of mass, and angular kinematics) and physiological factors (maximal oxygen uptake, 30 s anaerobic power), along with physical fitness (standing long jump, pull-ups) were assessed for 82 cross-country skiers (52 men and 30 women). Random forest analysis was utilized to identify the most influential parameters on skiing speed, which were subsequently used as input parameters to develop a neural network aimed at predicting this speed. The findings identified the primary predictors of skiing speed as the cycle length on both flat and uphill terrains, vertical displacement of the center of mass during the poling phase on uphill terrain, maximal oxygen uptake, and 30 s anaerobic power. The developed neural network model demonstrated high precision in predicting skiing speeds, evidenced by a strong correlation with actual speeds (correlation coefficient of 0.953) and 97.1% of predictions falling within the 95% Bland–Altman agreement limits, affirming the model’s reliability and effectiveness in forecasting skiing performance.