Estimating self-assessed personality from body movements and proximity in crowded mingling scenarios

ArtículoThis paper focuses on the automatic classi cation of self- assessed personality traits from the HEXACO inventory du- ring crowded mingle scenarios. We exploit acceleration and proximity data from a wearable device hung around the neck. Unlike most state-of-the-art studies, addressing per- sonality estimation during mingle scenarios provides a cha- llenging social context as people interact dynamically and freely in a face-to-face setting. While many former studies use audio to extract speech-related features, we present a novel method of extracting an individual's speaking status from a single body worn triaxial accelerometer which scales easily to large populations. Moreover, by fusing both speech and movement energy related cues from just acceleration, our experimental results show improvements on the estima- tion of Humility over features extracted from a single behav- ioral modality. We validated our method on 71 participants where we obtained an accuracy of 69% for Honesty, Consci- entiousness and Openness to Experience. To our knowledge, this is the largest validation of personality estimation carried out in such a social context with simple wearable sensors

Towards automatic estimation of conversation floors within F-formations

The detection of free-standing conversing groups has received significant attention in recent years. In the absence of a formal definition, most studies operationalize the notion of a conversation group either through a spatial or a temporal lens. Spatially, the most commonly used representation is the F-formation, defined by social scientists as the configuration in which people arrange themselves to sustain an interaction. However, the use of this representation is often accompanied with the simplifying assumption that a single conversation occurs within an F-formation. Temporally, various categories have been used to organize conversational units; these include, among others, turn, topic, and floor. Some of these concepts are hard to define objectively by themselves. The present work constitutes an initial exploration into unifying these perspectives by primarily posing the question: can we use the observation of simultaneous speaker turns to infer whether multiple conversation floors exist within an F-formation? We motivate a metric for the existence of distinct conversation floors based on simultaneous speaker turns, and provide an analysis using this metric to characterize conversations across F-formations of varying cardinality. We contribute two key findings: firstly, at the average speaking turn duration of about two seconds for humans, there is evidence for the existence of multiple floors within an F-formation; and secondly, an increase in the cardinality of an F-formation correlates with a decrease in duration of simultaneous speaking turns.Comment: 8th International Conference on Affective Computing & Intelligent Interaction EMERGent Workshop, 7 pages, 4 Figures, 2 Table