Diagnosing fatigue in gait patterns by support vector machines and self-organizing maps - Janssen, D.; Schöllhorn, W. I.; Newell, K. M.; Jäger, J. M.; Rost, F.; Vehof, K. (Human Movement Science, 2011)
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.