Recognition of Affect Based on Gait Patterns

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 dim...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2010-08, Vol.40 (4), p.1050-1061
Main Authors: Karg, Michelle, Kuhnlenz, Kolja, Buss, Martin
Format: Article
Language:English
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
Studies
Tasks
Video Recording - methods
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Summary: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.
ISSN:1083-4419
2168-2267
1941-0492
2168-2275
DOI:10.1109/TSMCB.2010.2044040