Detecting conversing groups with a single worn accelerometer

Hayley Hung, Gwenn Englebienne, Laura Cabrera-Quirós

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

18 Citations (Scopus)

Abstract

In this paper we propose the novel task of detecting groups of conversing people using only a single body-worn accelerometer per person. Our approach estimates each individual's social actions and uses the co-ordination of these social actions between pairs to identify group membership. The aim of such an approach is to be deployed in dense crowded environments. Our work differs significantly from previous approaches, which have tended to rely on audio and/or proximity sensing, often in much less crowded scenarios, for estimating whether people are talking together or who is speaking. Ultimately, we are interested in detecting who is speaking, who is conversing with whom, and from that, to infer socially relevant information about the interaction such as whether people are enjoying themselves, or the quality of their relationship in these extremely dense crowded scenarios. Striving towards this long-term goal, this paper presents a systematic study to understand how to detect groups of people who are conversing together in this setting, where we achieve a 64% classification accuracy using a fully automated system.

Original languageEnglish
Title of host publicationICMI 2014 - Proceedings of the 2014 International Conference on Multimodal Interaction
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages84-91
Number of pages8
ISBN (Electronic)978-1-4503-2885-2
DOIs
Publication statusPublished - 12 Nov 2014
Externally publishedYes
Event16th ACM International Conference on Multimodal Interaction (ICMI 2014) - Istanbul, Turkey
Duration: 12 Nov 201416 Nov 2014

Conference

Conference16th ACM International Conference on Multimodal Interaction (ICMI 2014)
CountryTurkey
CityIstanbul
Period12/11/1416/11/14

Keywords

  • Data mining
  • Human behavior
  • Human factors
  • Wearable sensors

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