Michelle Karg

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 persons 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.

A comparison of PCA, KPCA and LDA for feature extraction to recognize affect in gait kinematics

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 walkers affective state are recognizable with techniques from machine learning.

Towards mapping emotive gait patterns from human to robot

Integration of emotions enhances naturalness of human-robot interaction (HRI). This requires that the robot is equipped with hardware to express emotions. Believability and recognition of expressions is increased if the same emotional state is expressed in all modalities which the robot is capable of. Within this aspect, our work analyzes if a walking robot can express emotions in the way it walks and if these expressions are recognizable. The emotive gait patterns are derived from human characteristics for emotive gait. The parameters step length, height and time for a single step vary depending on the emotion. Mapping these changes to the kinematics of the robot and exaggeration of the walking styles leads to distinguishable expressions for the dimensions pleasure, arousal and dominance. Experimental results on a hexapod show that differences in arousal are best expressed and thus recognized. Comparing an animation of the hexapod with the real robot indicates that the robot is perceived as slightly more pleasant and active. This study shows that by changing its walking style the hexapod expresses emotions, in particular differences in arousal, which can be used to increase expressiveness in HRI.

Physiology and HRI: Recognition of over- and underchallenge

Contrary to common emotion recognition techniques by face or speech analysis, physiological data are involuntary and continuously available. Thus, they allow for emotion detection even in situations without spoken words or in case of non-extreme emotions, which are more likely to occur in human-robot interaction (HRI). In this paper, we describe the results of an experiment investigating non-extreme emotional states relevant for HRI scenarios (over- and underchallenge). Those states occurred naturally during the course of a LEGO construction task by manipulating working speed. Data collected from 28 subjects were analyzed and the results of different types of discriminant analysis and nearest neighbour methods were compared. Based on two physiological modalities (HR, SCR), correct classification rates of up to 76% for seven features and 74% for only two features were achieved. Overchallenge could be discriminated very well from the other two conditions (96.4 - 85.7%), whereas underchallenge is often confused with the intermediate condition with normal working speed.

A dynamic model and system-theoretic analysis of affect based on a Piecewise Linear system

This work proposes a Piecewise Linear (PL) system to model transitions of affect. Parameters of the model are identified based on a psychological experiment. The PL system describes affective reactions of humans to an external affective stimulus depending on the previous affective state. Results of the statistical analysis support that the previous affective state influences significantly the current affective state. Evaluation of the model shows that it is suitable as mathematical representation for the development of affect over time under the influence of an external stimulus. A following system-theoretic analysis of the model reveals that the PL system shows complex dynamic characteristics. It suggests that internal affective fluctuations exist.