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Author Karg, Michelle ♦ Jenke, Robert ♦ Seiberl, Wolfgang ♦ Kühnlenz, Kolja ♦ Schwirtz, Ansgar ♦ Buss, Martin
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Abstract This study investigates recognition of affect in human walking as daily motion, in order to provide a means for affect recognition at distance. For this purpose, a data base of affective gait patterns from non-professional actors has been recorded with optical motion tracking. Principal Component Analysis (PCA), Kernel PCA (KPCA) and Linear Discriminant Analysis (LDA) are applied to kinematic parameters and compared for feature extraction. LDA in combination with Naive Bayes leads to an accuracy of 91 % for person-dependent recognition of four discrete affective states based on observation of barely a single stride. Extra-success comparing to inter-individual recognition is twice as much. Furthermore, affective states which differ in arousal or dominance are better recognizable in walking. Though primary task of gait is locomotion, cues about a walker’s affective state are recognizable with techniques from machine learning. 1.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study