Wearable Trick Classification in Freestyle Snowboarding
Author(s): Groh B, Fleckenstein M, Eskofier B
Publication year: 2016
Conference Proceedings Title: IEEE EMBS 13th Annual International Body Sensor Networks Conference
Event: IEEE EMBS 13th Annual International Body Sensor Networks Conference
Event location: San Francisco, CA, USA
Digital motion analysis in freestyle snowboarding requires a stable trick detection and accurate classification. Freestyle snowboarding contains several trick categories that all have to be recognized for an application in training sessions or competitions. While previous work already addressed the classification of specific tricks or turns, there is no known method that contains a full pipeline for detection and classification of tricks from multiple categories. In this paper, we suggest a classification pipeline containing the detection, categorization and classification of tricks of two major freestyle trick categories. We evaluated our algorithm based on data from two different acquisitions with a total number of eleven athletes and 275 trick events. Tricks of both categories were categorized with recall results of 96.6% and 97.4%. The classification of the tricks was evaluated to an accuracy of 90.3% for the first and 93.3% for the second category.
FAU Authors / FAU Editors How to cite
APA: Groh, B., Fleckenstein, M., & Eskofier, B. (2016). Wearable Trick Classification in Freestyle Snowboarding. In IEEE EMBS 13th Annual International Body Sensor Networks Conference.
MLA: Groh, Benjamin, Martin Fleckenstein, and Björn Eskofier. "Wearable Trick Classification in Freestyle Snowboarding." Proceedings of the IEEE EMBS 13th Annual International Body Sensor Networks Conference, San Francisco, CA, USA 2016.