TitleAutomatic Classification of Functional Gait Disorders
Publication TypeJournal Article
Year of Publication2018
AuthorsSlijepcevic, D., M. Zeppelzauer, A-M. Raberger, C. Schwab, M. Schüller, A. Baca, and C. Breiteneder
JournalIEEE Journal of Biomedical and Health Informatics
Volume22
Issue5
Pages1653 - 1661
Date Published09/2018
ISSN2168-2194
KeywordsClassification, Feature Extraction, Gait Analysis, Gait Classification, Human Gait, Machine learning, SVM
AbstractThis paper proposes a comprehensive investigation of the automatic classification of functional gait disorders (GDs) based solely on ground reaction force (GRF) measurements. The aim of this study is twofold: first, to investigate the suitability of the state-of-the-art GRF parameterization techniques (representations) for the discrimination of functional GDs; and second, to provide a first performance baseline for the automated classification of functional GDs for a large-scale dataset. The utilized database comprises GRF measurements from 279 patients with 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. Based on this analysis, two classification experiments are pursued: distinction between healthy and impaired gait (N versus GD) and multiclass 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 toward the automated classification of GDs and a first performance baseline for future developments in this direction.
DOI10.1109/JBHI.2017.2785682
Refereed DesignationRefereed