Complex conversational scene analysis using wearable sensors

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientific

3 Citations (Scopus)
7 Downloads (Pure)


When aspiring to achieve 'in the wild' behavior analysis, we come across a number of conceptual and practical issues. In this chapter, we focus primarily on describing the data collection process for the automated analysis of human social behavior. Specifically, we address the task of analyzing social interaction during conversations. Most research in this area has focused largely on seated scenarios such as a small group having a meeting. In this chapter, we address the challenges that are faced when analyzing complex conversational scenes; crowded social settings where mingling occurs such as networking events, cocktail parties or conferences.We discuss and provide definitions of what 'in the wild' means for the context of wearable sensors. We provide a case study detailing different concerns that can emerge as a result of 'in the wild' social behavior analysis. More concretely, we address this in terms of how the concept of ecological validity coming from experimental psychology links with the concept of 'in the wild', practical and conceptual issues related to data collection, and finally how this influences social behavior analysis.Importantly in the presentation of the behavior analysis, we address key questions when an entire dataset is recorded from continuous natural behavior 'in the wild': When do we have enough data? Do we need a different machine learning approach for different amounts of data? Are social behaviors (e.g. speaking) more difficult to characterize than activities (e.g. walking/stepping) when the setting is so uncontrolled? We try to answer this question by considering the extent to which the nature of this problem becomes more personalized or person-independent as the size of the dataset increases.

Original languageEnglish
Subtitle of host publicationAdvances and Challenges
EditorsXavier Alameda-Pineda, Elisa Ricci, Nicu Sebe
Place of PublicationLondon
PublisherAcademic Press
Number of pages21
ISBN (Electronic)9780128146026
ISBN (Print)978-0-12-814601-9
Publication statusPublished - 2019

Publication series

NameComputer Vision and Pattern Recognition Series

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project
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.


  • Data collection
  • Ecological validity
  • Personalized social behavior modeling
  • Social behavior analysis


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