Compared to 25 years ago, the climbing sport itself has changed dramatically. From a rock climbing modification to a separation in three independent disciplines, the requirements to athletes and trainers increased rapidly. To ensure continuous improvement of the sport itself, the usage of measurement and sensor technology is unavoidable. Especially in the field of the discipline speed climbing, which will be performed as a single discipline at the Olympic Games 2024 in Paris, the current state of the art of movement analysis only consists of video analysis and the benefit of the experience of trainers. Therefore, this paper presents a novel method, which supports trainers and athletes and enables analysis of motion sequences and techniques. Prerecorded video footage is combined with existing feature and human body keypoint detection algorithms and standardized boundary conditions. Therefore, several image processing steps are necessary to convert the recorded movement of different speed climbing athletes to significant parameters for detailed analysis. By studying climbing trials of professional athletes and the used techniques in different sections of the speed climbing wall, the aim among others is to get comparable results and detect mistakes. As a conclusion, the presented method enables powerful analysis of speed climbing training and competition and serves with the aid of a user-friendly designed interface as a support for trainers and athletes for the evaluation of motion sequences.

Increasing media attention of and progress in competitive climbing demand for novel solution in sport analysis to accompany and contribute to the evolution of climbing. We present a holistic approach to acquire movements in sport climbing. Knowledge on force and corresponding body motion helps to understand technique, performance and might even lead to injury prevention during training. Our system allows detection of peaks in force on each climbing hand and foot hold while recording the associated body position and motion. To achieve this, we equipped each hold with a three-axis force sensor and combined this with a three-dimensional markerless motion capture system based on a depth camera. Existing methods to determine finger strength and other parameters such as temperature are employed to ensure comparable and repeatable results. In addition, pre-assesement of athletes will be used to evaluate inter- and intra-individual variability among athletes.

Competitive sport climbing progressed massively within the last quarter century. Development of technology enabling qualitative and quantitative analysis is required to withstand the challenges for athletes and trainers. This paper deals with the statistical study of a data set generated by the application of several image processing algorithms and neural networks on competition recordings. Therefore, calculated parameters are combined with random variables for the implementation of a linear mixed effect model. The resulting model enables the prediction of the end time of different athletes and the determination of its correlation with the input variables. Furthermore, analysis of velocity and path of the centre of gravity in different wall sections is done for all available speed climbing athletes. The observed data set consists of 297 runs in total divided into two subsets of 202 observations of 47 male and 95 of 25 female athletes. Among others, the statistical model was used for the validation of the measured parameters and the review and impact of proven techniques like the Tomoa skip in the start section. Likewise interesting is the high influence of the parameters, measured especially in the middle section of the wall, on the end time.

This study presents a comprehensive analysis of Speed Climbing athletes by examining motion parameters critical to elite performance. As such, several key values are extracted from about 900 competition recordings in order to generate a dataset for the identification of patterns in athletes’ technique and efficiency. A CNN-based framework is used to automate the detection of human keypoints and features, enabling a large-scale evaluation of climbing dynamics. The results revealed significant variations in performance for single sections of the wall, particularly in relation to start reaction times (with differences of up to 0.27 s) and increased split times the closer the athletes are to the end of the Speed Climbing wall (from 0.39 s to 0.45 s). In addition, a more detailed examination of the movement sequences was carried out by analyzing the velocity trajectories of hands and feet. The results showed that coordinated and harmonic movements, especially of the lower limbs, correlate strongly with the performance outcome. To ensure an individualized view of the data points, a comparison was made between multiple athletes, revealing insights into the influence of individual biomechanics on the efficiency of movements. The findings provide both trainers and athletes with interesting insights in relation to tailoring training methods by including split time benchmarks and limb coordination.

This study presents a comprehensive analysis of Speed Climbing athletes by examining motion parameters critical to elite performance. Thereby, several key values are extracted from about 900 competition recordings to generate a dataset for the identification of patterns in athletes’ technique and efficiency. A CNN-based framework is used to automate detection of human keypoints and feature, enabling large-scale evaluation of climbing dynamics. The results revealed significant variations of performance in single sections of the wall, particularly in start reaction times (up-to 0.27 s differences) and increased split times the closer the athletes get to the end of the Speed Climbing wall (from 0.39 s to 0.45 s). In addition, a more detailed examination of the movement sequences was carried out by analyzing the velocity trajectories of hands and feet. The results showed that coordinated and harmonic movements, especially of the lower limbs, correlate strongly with the performance outcome. To ensure an individualized view of the data points, a comparison was made between multiple athletes, revealing insights into the influence of individual biomechanics on the efficiency of movements. The findings provide trainers as well as athletes with interesting insights for tailoring training methods by including split time benchmarks and limb coordination.