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.

@en

When a group is traveling together it is challenging to recommendan itinerary consisting of several points of interest (POIs). Thepreferences of individual group members often diverge, but it isimportant to keep everyone in the group satisfied during the entiretrip. We propose a method to consider the preferences of all thepeople in the group. Building on this method, we design expla-nations for groups of people, to help them reach a consensus forplaces to visit. However, one open question is how to best formu-late explanations for such sequences. In this paper, we introduceTourExplain, an automated crowdsourcing pipeline to generate andevaluate explanations for groups with the aim of improving ourinitial proposed explanations by relying on the wisdom of crowds.@en