CONTRIBUTION TO THE BIOMECHANICAL ANALYSIS OF THE LONG JUMP

The Austrian national outdoor-record for men’s long jump is 8.30 m. This record was set in 1988. Since then the national record has not been broken. Therefore the purpose of this study was to examine the various parameters affecting performance during the last support phase of the long jump. The current work is an attempt to help both the coaches and the athletes to improve performance

MEASURING DYNAMIC SKI BEHAVIOR WITH STRAIN GAUGES

The performance of a ski is influenced not only by geometric properties such as length, width, camber height, side cut and thickness; but also by the deformation behaviour caused by the mechanical properties (Kaps et al. 2001). The purpose of this work was to evaluate the influence of flexural and torsional stiffness on the deformation of a ski during turns

Improved 2D Keypoint Detection in Out-of-Balance and Fall Situations -- combining input rotations and a kinematic model

Injury analysis may be one of the most beneficial applications of deep learning based human pose estimation. To facilitate further research on this topic, we provide an injury specific 2D dataset for alpine skiing, covering in total 533 images. We further propose a post processing routine, that combines rotational information with a simple kinematic model. We could improve detection results in fall situations by up to 21% regarding the [email protected] metric.Comment: extended abstract, 4 pages, 3 figures, 2 table

Deep learning-based 2D keypoint detection in alpine ski racing – A performance analysis of state-of-the-art algorithms applied to regular skiing and injury situations

Objectives: In this study, we examined the practicability of deep learning-based 2D keypoint detection applied to regular skiing and injury situations (i.e., out-of-balance situations and fall situations) on an alpine ski racing track. Methods: We therefore created a regular skiing- and injury situation-specific dataset (hereinafter called "Injury Ski Dataset"), on which the state-of-the-art keypoint detection algorithms OpenPose, Mask-R-CNN, AlphaPose and DCPose were compared. The performance of each keypoint detector was evaluated by calculating the mean per joint position error (MPJPE) and the percentage of correct keypoints (PCK). Failure cases and common error patterns were further investigated by a visual analysis. Results: We observed the best results for regular skiing, with 81%–92% of all keypoints detected correctly at an MPJPE of 9 (2) to 14 (3) pixels. In injury situations, self-occlusions and rare poses became more likely, similar to occlusions due to snow spray and motion blur. As a result, the performance in out-of-balance situations decreased to 68%–80% (PCK), while in fall situations, only 35%–54% of all keypoints were detected correctly, with mean errors of 26–36 pixels. Among all algorithms, AlphaPose was the most robust and achieved the best results. Conclusions: PCK and MPJPE for regular skiing were in the range of manual annotation errors and can be considered low enough for further biomechanical analysis. For fall situations, keypoint detection should be further improved. Regarding the development of a deep learning tool for injury analysis in alpine skiing in the future, we propose to fine-tune a well-performing keypoint detector, such as AlphaPose, on a ski- and injury-specific dataset, such as ours

Kinetic Friction of Sport Fabrics on Snow

After falls, skiers or snowboarders often slide on the slope and may collide with obstacles. Thus, the skier’s friction on snow is an important factor to reduce incidence and severity of impact injuries. The purpose of this study was to measure snow friction of different fabrics of ski garments with respect to roughness, speed, and contact pressure. Three types of fabrics were investigated: a commercially available ski overall, a smooth downhill racing suit, and a dimpled downhill racing suit. Friction was measured for fabrics taped on a short ski using a linear tribometer. The fabrics’ roughness was determined by focus variation microscopy. Friction coefficients were between 0.19 and 0.48. Roughness, friction coefficient, and friction force were highest for the dimpled race suit. The friction force of the fabrics was higher for the higher contact pressure than for the lower one at all speeds. It was concluded that the main friction mechanism for the fabrics was dry friction. Only the fabric with the roughest surface showed friction coefficients, which were high enough to sufficiently decelerate a sliding skier on beginner and intermediate slopes

Deep learning-based 2D keypoint detection in alpine ski racing – A performance analysis of state-of-the-art algorithms applied to regular skiing and injury situations

Objectives: In this study, we examined the practicability of deep learning-based 2D keypoint detection applied to regular skiing and injury situations (i.e., out-of-balance situations and fall situations) on an alpine ski racing track. Methods: We therefore created a regular skiing- and injury situation-specific dataset (hereinafter called ''Injury Ski Dataset''), on which the state-of-the-art keypoint detection algorithms OpenPose, Mask-R-CNN, AlphaPose and DCPose were compared. The performance of each keypoint detector was evaluated by calculating the mean per joint position error (MPJPE) and the percentage of correct keypoints (PCK). Failure cases and common error patterns were further investigated by a visual analysis. Results: We observed the best results for regular skiing, with 81%–92% of all keypoints detected correctly at an MPJPE of 9 (2) to 14 (3) pixels. In injury situations, self-occlusions and rare poses became more likely, similar to occlusions due to snow spray and motion blur. As a result, the performance in out-of-balance situations decreased to 68%–80% (PCK), while in fall situations, only 35%–54% of all keypoints were detected correctly, with mean errors of 26–36 pixels. Among all algorithms, AlphaPose was the most robust and achieved the best results. Conclusions: PCK and MPJPE for regular skiing were in the range of manual annotation errors and can be considered low enough for further biomechanical analysis. For fall situations, keypoint detection should be further improved. Regarding the development of a deep learning tool for injury analysis in alpine skiing in the future, we propose to fine-tune a well-performing keypoint detector, such as AlphaPose, on a ski- and injury-specific dataset, such as ours

Distribution of injury mechanisms and related factors in ACL-injured female carving skiers

Abstract While ACL injury mechanisms in skiers using traditional skis are well studied, no study has yet investigated the distribution of injury mechanisms in carving skiers. In traditional skiers, the backward twisting fall seems to be the dominant injury mechanism, especially in female skiers. Female recreational skiers have a threefold higher risk to sustain an ACL injury than male skiers; therefore, it is important to determine if carving skis influence the distribution of injury mechanisms and the related frequencies of ACL injuries in female skiers. We investigated the frequencies of injury mechanisms and related factors in 65 ACL-injured female carving skiers by questionnaire. The forward twisting fall was the most reported ACL injury mechanism with about 51%, followed by the backward twisting fall within 29% of cases. Catching an edge of the ski (59 vs. 24%, P = 0.03) when executing turns (69 vs. 41%, P = 0.053) was a more frequent cause for forward twisting falls than for the other types of falling. While 29% of bindings released during a forward twisting fall, only 3.1% released during the remaining mechanisms. In contrast to traditional skiers, the forward twisting fall was the dominant injury mechanism in female carving skiers with ACL injury