title: Walking pattern analysis and SVM classification based on simulated gaits.
abstract: Three classes of walking patterns, normal, caution and danger, were simulated by tying elastic bands to joints of lower body. In order to distinguish one class from another, four local motions suggested by doctors were investigated stepwise, and differences between levels were evaluated using t-tests. The human adaptability in the tests was also evaluated. We improved average classification accuracy to 84.50% using multiclass support vector machine classifier and concluded that human adaptability is a factor that can cause obvious bias in contiguous data collections.
authors.affiliation: Graduate School of Computer Science and Engineering, University of Aizu, Aizu-Wakamatsu City, Fukushima 965-8580, Japan.
authors.forename: Yuxiang
authors.initials: Y
authors.lastname: Mao
authors.forename: Masaru
authors.initials: M
authors.lastname: Saito
authors.forename: Takehiro
authors.initials: T
authors.lastname: Kanno
authors.forename: Daming
authors.initials: D
authors.lastname: Wei
authors.forename: Hiroyasu
authors.initials: H
authors.lastname: Muroi
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005684:Gait / Q000502:physiology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007866:Leg / Q000502:physiology*; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016138:Walking / Q000502:physiology*; D051598:Whole Body Imaging / Q000379:methods*