Diagnosing fatigue in gait patterns by support vector machines and 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. Julkaistu Tekijä Elsevier. Kaikki oikeudet pidätetään.
Aiheet: | juoksu liikkeen ominaisuus uupumus biomekaniikka analyysi tutkimusmenetelmä |
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Aihealueet: | kestävyys urheilu |
Tagging: | Bewegungsmuster |
DOI: | 10.1016/j.humov.2010.08.010 |
Julkaisussa: | Human Movement Science |
Toimittajat: | J. Barreiros, D. Araujo, W. I. Schöllhorn |
Julkaistu: |
Elsevier
2011
|
Vuosikerta: | 30 |
Numero: | 5 |
Sivuja: | 966-975 |
Julkaisutyypit: | elektroninen julkaisu |
Kieli: | englanti (kieli) |
Taso: | kehittynyt |