  This article proposes a comprehensive investigation of the automatic
classification of functional gait disorders based solely on ground reaction
force (GRF) measurements. The aim of the study is twofold: (1) to investigate
the suitability of stateof-the-art GRF parameterization techniques
(representations) for the discrimination of functional gait disorders; and (2)
to provide a first performance baseline for the automated classification of
functional gait disorders for a large-scale dataset. The utilized database
comprises GRF measurements from 279 patients with gait disorders (GDs) and data
from 161 healthy controls (N). Patients were manually classified into four
classes with different functional impairments associated with the "hip",
"knee", "ankle", and "calcaneus". Different parameterizations are investigated:
GRF parameters, global principal component analysis (PCA)-based representations
and a combined representation applying PCA on GRF parameters. The
discriminative power of each parameterization for different classes is
investigated by linear discriminant analysis (LDA). Based on this analysis, two
classification experiments are pursued: (1) distinction between healthy and
impaired gait (N vs. GD) and (2) multi-class classification between healthy
gait and all four GD classes. Experiments show promising results and reveal
among others that several factors, such as imbalanced class cardinalities and
varying numbers of measurement sessions per patient have a strong impact on the
classification accuracy and therefore need to be taken into account. The
results represent a promising first step towards the automated classification
of gait disorders and a first performance baseline for future developments in
this direction.
