6533b872fe1ef96bd12d3144

RESEARCH PRODUCT

Diagnosing fatigue in gait patterns by support vector machines and self-organizing maps

Katrin VehofFranz RostKarl M. NewellWolfgang I. SchöllhornJörg M. JägerDaniel Janssen

subject

AdultMaleSelf-organizing mapmedicine.medical_specialtySupport Vector MachineWeight LiftingComputer scienceIndividualityBiophysicsExperimental and Cognitive PsychologyPattern Recognition AutomatedYoung AdultPhysical medicine and rehabilitationmedicineHumansOrthopedics and Sports MedicineGround reaction forceGaitArtificial neural networkMuscle fatiguebusiness.industryBiomechanicsGeneral MedicineGaitBiomechanical PhenomenaSupport vector machineNonlinear DynamicsMuscle FatiguePattern recognition (psychology)Artificial intelligencebusinesshuman activities

description

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±8.8kg). 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.

yearjournalcountryeditionlanguage
2009-08-26Human Movement Science
https://doi.org/10.1016/j.humov.2010.08.010