OptiPath: A Multidimensional Educational Platform for Enhancing Technical Skills in Alpine Skiing
DOI:
https://doi.org/10.18662/rrem/17.2/985Keywords:
alpine skiing, artificial intelligence, image processing, ideal path, algorithm, mathematical mode, performance analysisAbstract
OptiPath is a multidimensional desktop application designed to enhance alpine skiing technique by integrating video analysis, artificial intelligence, and machine learning. The platform enables users to upload skiing footage, from which it performs frame-by-frame detection of gates and skier trajectory, allowing for a comparative analysis between the actual and optimal skiing paths. This feedback mechanism supports athletes and coaches in identifying technical execution errors and improving performance outcomes. Developed in Python, the system utilizes object detection and image processing algorithms to deliver accurate visualizations, all without requiring an internet connection—making it particularly suited for training in remote environments. Tested under real-world giant slalom conditions, OptiPath demonstrated both reliability and ease of use, offering a lightweight, data-private solution that does not rely on user accounts or cloud storage. Its minimalist design and efficient processing capabilities make it accessible across a range of hardware configurations. By bridging traditional coaching methods with modern technological tools, OptiPath provides a precise, adaptable, and user-friendly means of technical evaluation, ultimately contributing to more effective skill development and injury prevention in alpine skiing.
References
Baca, A., & Kornfeind, P. (2006). Rapid feedback systems for elite sports training. IEEE Pervasive Computing, 5(4), 70-76. https://doi.org/10.1109/MPRV.2006.82
Cao, Z., Hidalgo, G., Simon, T., Wei, S. E., & Sheikh, Y. (2021). Openpose: Realtime multi-person 2d pose estimation using part affinity fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1), 172-186. https://doi.org/10.1109/TPAMI.2019.2929257
Claudino, J. G., Capanema, D. O., de Souza, T. V., Serrão, J. C., Pereira, A. C., & Mujika, I. (2019). Current approaches to the use of artificial intelligence for injury risk assessment and performance prediction in team sports: A systematic review. Sports Medicine Open, 5(1). https://doi.org/10.1186/s40798-019-0202-3
Federolf, P., Reid, R., Gilgien, M., Haugen, P., & Smith, G. (2014). The application of principal component analysis to quantify technique in sports. Scandinavian Journal of Medicine & Science in Sports, 24(3), 491–499. https://doi.org/10.1111/j.1600-0838.2012.01455.x
Franks, I., & Hughes, M. (Eds.). (2004). Notational analysis of sport: Systems for better coaching and performance in sport (2nd ed.). Routledge. https://doi.org/10.4324/9780203641958
Jentschura, U. D., & Fahrbach, F. (2004). Physics of skiing: The ideal carving equation and its applications. Canadian Journal of Physics, 82(4), 249-261.
https://doi.org/10.48550/arXiv.physics/0310086
Kautz, T., Groh, B., Hannink, J., Jensen, U., Strubberg, H., & Eskofier, B. (2017). Activity recognition in beach volleyball using a deep convolutional neural network. Data Mining and Knowledge Discovery, 31, 1678–1705. https://doi.org/10.1007/s10618-017-0495-0
Kirby, R. (2009). Development of a real‐time performance measurement and feedback system for alpine skiers. Sports Technology, 2(1-2), 43-52. https://doi.org/10.1080/19346182.2009.9648498
Liu, H., Gómez, M. A., Gonçalves, B., & Sampaio, J. (2015). Technical performance and match-to-match variation in elite football teams. Journal of Sports Sciences, 34(6), 509–518. https://doi.org/10.1080/02640414.2015.1117121
Müller, E., & Schwameder, H. (2003). Biomechanical aspects of new techniques in alpine skiing and ski-jumping. Journal of Sports Sciences, 21(9), 679–692. https://doi.org/10.1080/0264041031000140284
Müller, E., Schwameder, H., Kornexl, E., & Raschner, C. (Eds.). (1997). Science and Skiing (1st ed.). Taylor & Francis. https://doi.org/10.4324/9780203476178
Nath, T., Mathis, A., Chen, A., Patel, A., Bethge, M., & Mathis, M. (2019). Using DeepLabCut for 3D markerless pose estimation across species and behaviors. Nature Protocols, 14, 2152–2176. https://doi.org/10.1038/s41596-019-0176-0
Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48, 137-141. https://doi.org/10.1007/s11747-019-00710-5
Supej, M., & Holmberg, H. C. (2011). A new time measurement method using a high-end global navigation satellite system to analyze alpine skiing. Research Quarterly for Exercise and Sport, 82(3), 400–411. https://doi.org/10.1080/02701367.2011.10599772
Supej, M., & Holmberg, H. C. (2019). Recent Kinematic and Kinetic Advances in Olympic Alpine Skiing: Pyeongchang and Beyond. Frontiers in physiology, 10, 111. https://doi.org/10.3389/fphys.2019.00111
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