Diagnosing fatigue in gait patterns by support vector machines and self-organizing maps
(Diagnose von Ermüdung im Gangmuster von Support Vector Machines und Self-Organizing Maps)
The aim of the study was to train and test support vector machines (SVM) and self-organizing maps (SOM) to correctly classify gait patterns before, during and after complete leg exhaustion by isokinetic leg exercises. Ground reaction forces were derived for 18 gait cycles on 9 adult participants. Immediately before the trials 7-12, participants were required to completely exhaust their calves with the aid of additional weights (44.4 ± 8.8 kg). Data were analyzed using: (a) the time courses directly and (b) only the deviations from each individual`s calculated average gait pattern. On an inter-individual level the person recognition of the gait patterns was 100% realizable. Fatigue recognition was also highly probable at 98.1%. Additionally, applied SOMs allowed an alternative visualization of the development of fatigue in the gait patterns over the progressive fatiguing exercise regimen.
© Copyright 2011 Human Movement Science. Elsevier. Veröffentlicht von Elsevier. Alle Rechte vorbehalten.
| Schlagworte: | |
|---|---|
| Notationen: | Ausdauersportarten |
| Tagging: | Bewegungsmuster |
| Veröffentlicht in: | Human Movement Science |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier
2011
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| Online-Zugang: | http://doi.org/10.1016/j.humov.2010.08.010 |
| Jahrgang: | 30 |
| Heft: | 5 |
| Seiten: | 966-975 |
| Dokumentenarten: | elektronische Publikation |
| Level: | hoch |