Classification and visualization of skateboard tricks using wearable sensors

Groh B, Fleckenstein M, Kautz T, Eskofier B (2017)

Publication Type: Journal article, Original article

Publication year: 2017

Journal

Pervasive and Mobile Computing Elsevier BV

Book Volume: 40

Pages Range: 42-55

URI: https://authors.elsevier.com/a/1VDaj5bwSmo0qn

DOI: 10.1016/j.pmcj.2017.05.007

Abstract

The application of wearables and customized signal processing methods offers new opportunities for motion analysis and visualization in skateboarding. In this work, we propose an automatic trick analysis and visualization application based on inertial-magnetic data. Skateboard tricks are detected and classified in real-time and visualized by means of an animated 3D-graphic. We achieved a trick detection recall of 96.4%, a classification accuracy of 89.1% (considering correctly performed tricks) and an error of the board orientation visualization of 2.2° ± 1.9°. The system is extendable in its application and can be incorporated as support for skateboard training and competitions.

Authors with CRIS profile

Benjamin Groh Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Thomas Kautz Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics) Björn Eskofier Lehrstuhl für Informatik 14 (Machine Learning and Data Analytics)

Additional Organisation(s)

Machine Learning and Data Analytics Lab

How to cite

APA:

Groh, B., Fleckenstein, M., Kautz, T., & Eskofier, B. (2017). Classification and visualization of skateboard tricks using wearable sensors. Pervasive and Mobile Computing, 40, 42-55. https://dx.doi.org/10.1016/j.pmcj.2017.05.007

MLA:

Groh, Benjamin, et al. "Classification and visualization of skateboard tricks using wearable sensors." Pervasive and Mobile Computing 40 (2017): 42-55.

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