Automatic gate-to-gate time recognition from audio recordings in slalom skiing using neural networks

Abstract

We introduce a novel approach for computing gate-to-gate time automatically from audio recordings. In slalom skiing, gate-to-gate timing is a valuable metric for athletes and trainers, capturing the time elapsed between slalom gates. The availability of these measurements immediately after each run allows for prompt feedback. This study specifically concentrates on gate-to-gate timing in alpine slalom skating, serving as a foundational step towards its future application in slalom skiing. While existing methods for measuring gate-to-gate time vary in their feasibility, accuracy, and compliance with regulations, we propose a solution utilizing a convolutional neural network (CNN) to predict gate locations using the audio signals generated upon gate contact. By leveraging these predictions, we achieve fully automated computation of gate-to-gate timings. We conduct a comparative analysis between the CNN’s predictions and data obtained from an inertial measurement unit. Our findings reveal a strong predictive correlation between the two methods, with an R-squared value of 0.94 and a root mean squared error of 0.036. The majority of predictions demonstrate high accuracy, falling within a range of thousandths of a second. However, a few outliers negatively impact the overall performance. Notably, we observe no deterioration in predictive quality based on the distance between the camera and the gate. Finally, we delve into the challenges and limitations associated with our approach and provide a comprehensive discussion. To conclude, we outline potential avenues for future research and extensions of our methodology to the realm of slalom skiing.

Keywords

• performing analysis
• inline slalom
• alpine skiing
• slalom
• machine learning