Article

An automated gait classification method is developed in this study, which can be
applied to analysis and to classify pathological gait patterns using 3D ground
reaction force (GRFs) data. The study involved the discrimination of gait
patterns of healthy, cerebral palsy (CP) and multiple sclerosis subjects. The
acquired 3D GRFs data were categorized into three groups. Two different
algorithms were used to extract the gait features; the GRFs parameters and the
discrete wavelet transform (DWT), respectively. Nearest neighbor classifier (NNC)
and artificial neural networks (ANN) were also investigated for the
classification of gait features in this study. Furthermore, different feature
sets were formed using a combination of the 3D GRFs components (mediolateral,
anterioposterior, and vertical) and their various impacts on the acquired results
were evaluated. The best leave-one-out (LOO) classification accuracy 85% was
achieved. The results showed some improvement through the application of a
features selection algorithm based on M-shaped value of vertical force and the
statistical test ANOVA of mediolateral and anterioposterior forces. The optimal
feature set of six features enhanced the accuracy to 95%. This work can provide
an automated gait classification tool that may be useful to the clinician in the
diagnosis and identification of pathological gait impairments.

Langue : ANGLAIS

Tiré à part : OUI