Automated pattern analysis in gesture research : similarity measuring in 3D motion capture models of communicative action

The question of how to model similarity between gestures plays an important role in current studies in the domain of human communication. Most research into recurrent patterns in co-verbal gestures – manual communicative movements emerging spontaneously during conversation – is driven by qualitative analyses relying on observational comparisons between gestures. Due to the fact that these kinds of gestures are not bound to well-formedness conditions, however, we propose a quantitative approach consisting of a distance-based similarity model for gestures recorded and represented in motion capture data streams. To this end, we model gestures by flexible feature representations, namely gesture signatures, which are then compared via signature-based distance functions such as the Earth Mover's Distance and the Signature Quadratic Form Distance. Experiments on real conversational motion capture data evidence the appropriateness of the proposed approaches in terms of their accuracy and efficiency. Our contribution to gesture similarity research and gesture data analysis allows for new quantitative methods of identifying patterns of gestural movements in human face-to-face interaction, i.e., in complex multimodal data sets

Automated pattern analysis in gesture research : similarity measuring in 3D motion capture models of communicative action

The question of how to model similarity between gestures plays an important role in current studies in the domain of human communication. Most research into recurrent patterns in co-verbal gestures – manual communicative movements emerging spontaneously during conversation – is driven by qualitative analyses relying on observational comparisons between gestures. Due to the fact that these kinds of gestures are not bound to well-formedness conditions, however, we propose a quantitative approach consisting of a distance-based similarity model for gestures recorded and represented in motion capture data streams. To this end, we model gestures by flexible feature representations, namely gesture signatures, which are then compared via signature-based distance functions such as the Earth Mover's Distance and the Signature Quadratic Form Distance. Experiments on real conversational motion capture data evidence the appropriateness of the proposed approaches in terms of their accuracy and efficiency. Our contribution to gesture similarity research and gesture data analysis allows for new quantitative methods of identifying patterns of gestural movements in human face-to-face interaction, i.e., in complex multimodal data sets

European spaces and the Roma: Denaturalizing the naturalized in online reader comments

With the entry of several Eastern European nations into the European Union (EU), a “third” space has developed in the discourse for nations perceived as not fully integrated “inside” the EU system. This article investigates the construction of this “third space” in the resultant “moral panic” about undesired immigration from other EU countries and its potential drain on the social services of the United Kingdom and links it to Euroskeptic discourse in British media. The article uses construal operations from cognitive linguistics combined with critical discourse studies as a way of denaturalizing the discourse in online comments that focus on the Bulgarian/Romanian immigration issue which we then connect to anti-Roma discourse. Results reveal a view of the United Kingdom as contaminated by Roma and underscore the need for novel metaphors to be countered before they become entrenched and used as tools for political propaganda

Automated pattern analysis in gesture research : similarity measuring in 3D motion capture models of communicative action

The question of how to model similarity between gestures plays an important role in current studies in the domain of human communication. Most research into recurrent patterns in co-verbal gestures – manual communicative movements emerging spontaneously during conversation – is driven by qualitative analyses relying on observational comparisons between gestures. Due to the fact that these kinds of gestures are not bound to well-formedness conditions, however, we propose a quantitative approach consisting of a distance-based similarity model for gestures recorded and represented in motion capture data streams. To this end, we model gestures by flexible feature representations, namely gesture signatures, which are then compared via signature-based distance functions such as the Earth Mover's Distance and the Signature Quadratic Form Distance. Experiments on real conversational motion capture data evidence the appropriateness of the proposed approaches in terms of their accuracy and efficiency. Our contribution to gesture similarity research and gesture data analysis allows for new quantitative methods of identifying patterns of gestural movements in human face-to-face interaction, i.e., in complex multimodal data sets