Classification and visualization of skateboard tricks using wearable sensors
| Autor: | Thomas Kautz, Martin Fleckenstein, BjoernM. Eskofier, Benjamin H. Groh |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Signal processing
Motion analysis Computer Networks and Communications Computer science Orientation (computer vision) business.industry Wearable computer 020207 software engineering 030229 sport sciences 02 engineering and technology Computer Science Applications Visualization 03 medical and health sciences 0302 clinical medicine Hardware and Architecture Computer graphics (images) 0202 electrical engineering electronic engineering information engineering Computer vision Artificial intelligence business Software Information Systems |
| Zdroj: | Pervasive and Mobile Computing. 40:42-55 |
| ISSN: | 1574-1192 |
| Popis: | 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 inertialmagnetic 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 2.21.9. The system is extendable in its application and can be incorporated as support for skateboard training and competitions. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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