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

Abstract

This 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.