Szczegóły publikacji
Opis bibliograficzny
Turn detection in alpine skiing using smartphone sensors / Jakub ROBAK, Wojciech TUREK // W: Computational Science – ICCS 2025 Workshops : 25th international conference : Singapore, Singapore, July 7–9, 2025 : proceedings, Pt. 4 / eds. Maciej Paszyński, Amanda S. Barnard, Yongjie Jessica Zhang. — Cham : Springer Nature Switzerland, cop. 2025. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 15910). — ISBN: 978-3-031-97566-0; e-ISBN: 978-3-031-97567-7. — S. 86–94. — Bibliogr., Abstr. — Publikacja dostępna online od: 2025-07-04
Autorzy (2)
Dane bibliometryczne
| ID BaDAP | 161064 |
|---|---|
| Data dodania do BaDAP | 2025-07-18 |
| DOI | 10.1007/978-3-031-97567-7_8 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2025 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Alpine skiing is a complex sport where technique is the key. The ability to detect turns, along with their intricate patterns, can provide valuable insights into performance analysis and injury prevention for skiers. Modern turn detection systems are often costly or cumbersome to use, limiting accessibility for recreational skiers, professionals, and coaches alike. In this paper, we present our AI-based solution focused on IMU sensors embedded in smartphones, which are widely available to the general public. Our approach addresses the challenges posed by noisy IMU data from mobile devices, varying skiing techniques, and diverse environmental conditions. We collected skiing data from 11 skiers of varying skill levels, who skied freely (without designated tracks) across three different ski resorts. The dataset consists of measurements captured by smartphones, including IMU signals from accelerometers, gyroscopes, and orientation sensors. To process the data and extract individual turns, we developed a gradient-based algorithm paired with optimization techniques specifically tailored to the constraints of smartphone sensors. Our proposed algorithm achieves robust turn detection while maintaining computational efficiency, enabling analysis on mobile devices. Experiments demonstrate that the model achieves an F1 score of 0.943 on test data. This highlights the potential of using smartphone-embedded sensors for sports analytics, making advanced motion detection more accessible to a broader audience. Our findings open pathways for personalized feedback systems and scalable solutions in ski analytics.
