Recognition of affect based on gait patterns

Michelle Karg, Kolja Kühnlenz, Martin Buss

Research output: Contribution to journalArticlepeer-review

120 Scopus citations

Abstract

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, humanrobot interaction, and cognitive home environments.

Original languageEnglish
Article number5439949
Pages (from-to)1050-1061
Number of pages12
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume40
Issue number4
DOIs
StatePublished - Aug 2010

Keywords

  • Affective computing
  • feature extraction
  • gait recognition
  • human motion analysis
  • pattern classification

Fingerprint

Dive into the research topics of 'Recognition of affect based on gait patterns'. Together they form a unique fingerprint.

Cite this