Classification and visualization of skateboard tricks using wearable sensors

Journal article
(Original article)


Publication Details

Author(s): Groh B, Fleckenstein M, Kautz T, Eskofier B
Journal: Pervasive and Mobile Computing
Publication year: 2017
Volume: 40
Pages range: 42-55
ISSN: 1574-1192


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.



FAU Authors / FAU Editors

Eskofier, Björn Prof. Dr.
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Groh, Benjamin
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)
Kautz, Thomas
Lehrstuhl für Informatik 14 (Maschinelles Lernen und Datenanalytik)


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.

BibTeX: 

Last updated on 2018-19-04 at 04:03