title: Diagnosing fatigue in gait patterns by support vector machines and self-organizing maps.
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±8.8kg). 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.
authors.affiliation: Training and Movement Science, University of Mainz, Albert Schweitzer Strasse 22, 55099 Mainz, Germany. djanssen@uni-mainz.de
authors.forename: Daniel
authors.initials: D
authors.lastname: Janssen
authors.forename: Wolfgang I
authors.initials: WI
authors.lastname: Schöllhorn
authors.forename: Karl M
authors.initials: KM
authors.lastname: Newell
authors.forename: Jörg M
authors.initials: JM
authors.lastname: Jäger
authors.forename: Franz
authors.initials: F
authors.lastname: Rost
authors.forename: Katrin
authors.initials: K
authors.lastname: Vehof
mesh_terms: D000328:Adult; D001696:Biomechanical Phenomena; D005684:Gait / Q000502:physiology*; D006801:Humans; D007206:Individuality; D008297:Male; D018763:Muscle Fatigue / Q000502:physiology*; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated; D060388:Support Vector Machine; D014891:Weight Lifting / Q000502:physiology; D055815:Young Adult