The MatchNMingle dataset: A novel multi-sensor resource for the analysis of social interactions and group dynamics in-the-wild during free-standing conversations and speed dates

Laura Cabrera-Quiros, Andrew Demetriou, Ekin Gedik, Leander van der Meij, Hayley Hung

Research output: Contribution to journalArticleScientificpeer-review

24 Citations (Scopus)
76 Downloads (Pure)

Abstract

We present MatchNMingle, a novel multimodal/multisensor dataset for the analysis of free-standing conversational groups and speed-dates in-the-wild. MatchNMingle leverages the use of wearable devices and overhead cameras to record social interactions of 92 people during real-life speed-dates, followed by a cocktail party. To our knowledge, MatchNMingle has the largest number of participants, longest recording time and largest set of manual annotations for social actions available in this context in a real-life scenario. It consists of 2 hours of data from wearable acceleration, binary proximity, video, audio, personality surveys, frontal pictures and speed-date responses. Participants' positions and group formations were manually annotated; as were social actions (eg. speaking, hand gesture) for 30 minutes at 20fps making it the first dataset to incorporate the annotation of such cues in this context. We present an empirical analysis of the performance of crowdsourcing workers against trained annotators in simple and complex annotation tasks, founding that although efficient for simple tasks, using crowdsourcing workers for more complex tasks like social action annotation led to additional overhead and poor inter-annotator agreement compared to trained annotators (differences up to 0.4 in Fleiss' Kappa coefficients). We also provide example experiments of how MatchNMingle can be used.

Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalIEEE Transactions on Affective Computing
VolumePP
Issue number99
DOIs
Publication statusPublished - 2018

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Acceleration
  • Cameras
  • cameras
  • Computers
  • Crowdsourcing
  • f-formation
  • Manuals
  • mingle
  • Multimodal dataset
  • personality traits
  • Sensors
  • Speed-dates
  • Task analysis
  • wearable acceleration

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