Identification of EMG frequency patterns in running by wavelet analysis and support vector machines
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| Deutscher übersetzter Titel: | Identifikation der EMG-Frequenz-Bewegungsmuster beim Laufen durch Wavelet-Analyse und Support- Vector-Machine-Klassifizierung |
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| Autor: | Jaitner, Thomas; Janssen, Daniel; Burger, Ronald; Wenzel, Uwe |
| Erschienen in: | Proceedings of the 28th Conference of the International Society of Biomechanics in Sports : July 19-23, 2010, Northern Michigan University, Marquette, Michigan USA [28 International Conference on Biomechanics in Sports (2010) : Marquette, Michigan, USA, July 19 – 23, 2010] |
| Veröffentlicht: | Marquette (Mich.): Northern Michigan Univ. (Verlag), 2010, S. 376-379, Lit. |
| Herausgeber: | Universität Konstanz |
| Format: | Literatur (SPOLIT) |
| Publikationstyp: | Sammelwerksbeitrag |
| Medienart: | Elektronische Ressource (online) Gedruckte Ressource |
| Dokumententyp: | Graue Literatur |
| Sprache: | Englisch |
| Schlagworte: | |
| Online Zugang: | |
| Erfassungsnummer: | PU201106005075 |
| Quelle: | BISp |
Abstract des Autors
The purpose of this study was to identify EMG pattern of running at different speed and incline based on a trial-to-trial analysis. Eight subjects performed treadmill running at five different conditions (4, 5 and 6 m/s, 5m/s at 5° incline, 5m/s at 2° decline). EMG data of eight leg muscles were recorded and transformed by a wavelet analysis (van Tscharner, 2000). Ten subsequent steps of each subject and condition were classified by support vector machines. Between 93 and 100% of all EMG patterns were assigned correctly to the individual. According to the different running conditions recognition rates ranged between 78 and 88%. Hence, support vector machines can be considered as powerful nonlinear tool for the classification of dynamic EMG patterns. Verf.-Referat