Estimating Romantic, Social, and Sexual Attraction by Quantifying Bodily Coordination using Wearable Sensors
J.D. Vargas Quiros (TU Delft - Pattern Recognition and Bioinformatics)
Hayley Hung (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
In this paper we introduce a novel method of estimating romantic, social and sexual attraction between two people by quantifying their bodily coordination using wearable sensors in a speed-date setting. We developed simple synchrony and convergence features, inspired from the literature and specifically adapted to be extracted from accelerometer data. To our knowledge, this is the first time that motion convergence is used for estimating attraction. Our features could predict one-way social attraction with a 73% Area under the ROC curve (AUC), out-performing previous work in a similar setting. We also showed that prediction performance increased when the male and female data are separated. We could also predict mutual romantic attraction with an AUC of 80%. Finally, we found that social attraction could be predicted better from movement correlation features whereas for romantic and sexual interest mimicry features were better indicators. Additionally, we found that 'mimicking of female to male' and 'convergence of female's movement to male's' were indicators of sexual and romantic mutual attraction in our data.