Recognition of Affect Based on Gait Patterns

• Published in: IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 40; no. 4; pp. 1050 - 1061
• Main Authors: Karg, Michelle, Kuhnlenz, Kolja, Buss, Martin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2010
• Subjects: Affect - physiology; Affective computing; Algorithms; Artificial Intelligence; Biomedical monitoring; Biometry - methods; Classification; Cybernetics; Discriminant analysis; Dominance; Face detection; Feature extraction; Gait; Gait - physiology; gait recognition; human motion analysis; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Kernel; Legged locomotion; Linear discriminant analysis; Monitoring; Motion analysis; pattern classification; Pattern recognition; Pattern Recognition, Automated - methods; Photography - methods; Principal component analysis; Recognition; Reproducibility of Results; Sensitivity and Specificity; Tasks; Video Recording - methods
• ISSN: 1083-4419; 1941-0492
• DOI: 10.1109/TSMCB.2010.2044040

To provide a means for recognition of affect from a distance, this paper analyzes the capability of gait to reveal a person's affective state. We address interindividual versus person-dependent recognition, recognition based on discrete affective states versus recognition based on affective dimensions, and efficient feature extraction with respect to affect. Principal component analysis (PCA), kernel PCA, linear discriminant analysis, and general discriminant analysis are compared to either reduce temporal information in gait or extract relevant features for classification. Although expression of affect in gait is covered by the primary task of locomotion, person-dependent recognition of motion capture data reaches 95% accuracy based on the observation of a single stride. In particular, different levels of arousal and dominance are suitable for being recognized in gait. It is concluded that gait can be used as an additional modality for the recognition of affect. Application scenarios include monitoring in high-security areas, human-robot interaction, and cognitive home environments.