Diagnosing fatigue in gait patterns by support vector machines and self-organizing maps |
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Authors: | Janssen Daniel Schöllhorn Wolfgang I Newell Karl M Jäger Jörg M Rost Franz Vehof Katrin |
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Affiliation: | aTraining and Movement Science, University of Mainz, Albert Schweitzer Strasse 22, 55099 Mainz, Germany;bDepartment of Kinesiology, The Pennsylvania State University, University Park, USA;cDepartment of Sport Science, University of Muenster, Germany |
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Abstract: | 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. |
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Keywords: | PsycINFO classification: 3720 4160 |
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