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The unobtrusive group interaction (UGI) corpus

Authors:

Indrani Bhattacharya,

Christoph Riedl,

Brooke Foucault Welles,

Richard J. RadkeAuthors Info & Claims

MMSys '19: Proceedings of the 10th ACM Multimedia Systems Conference

Pages 249 - 254

https://doi.org/10.1145/3304109.3325816

Published: 18 June 2019 Publication History

Abstract

Studying group dynamics requires fine-grained spatial and temporal understanding of human behavior. Social psychologists studying human interaction patterns in face-to-face group meetings often find themselves struggling with huge volumes of data that require many hours of tedious manual coding. There are only a few publicly available multi-modal datasets of face-to-face group meetings that enable the development of automated methods to study verbal and non-verbal human behavior. In this paper, we present a new, publicly available multi-modal dataset for group dynamics study that differs from previous datasets in its use of ceiling-mounted, unobtrusive depth sensors. These can be used for fine-grained analysis of head and body pose and gestures, without any concerns about participants' privacy or inhibited behavior. The dataset is complemented by synchronized and time-stamped meeting transcripts that allow analysis of spoken content. The dataset comprises 22 group meetings in which participants perform a standard collaborative group task designed to measure leadership and productivity. Participants' post-task questionnaires, including demographic information, are also provided as part of the dataset. We show the utility of the dataset in analyzing perceived leadership, contribution, and performance, by presenting results of multi-modal analysis using our sensor-fusion algorithms designed to automatically understand audio-visual interactions.

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Cited By

Beyan CVinciarelli ABue A(2023)Co-Located Human–Human Interaction Analysis Using Nonverbal Cues: A SurveyACM Computing Surveys10.1145/362651656:5(1-41)Online publication date: 25-Nov-2023
https://dl.acm.org/doi/10.1145/3626516
Lin YLiu YLee C(2023)An Interaction-process-guided Framework for Small-group Performance PredictionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/355876819:2(1-25)Online publication date: 6-Feb-2023
https://doi.org/10.1145/3558768
Webb NGiuliani MLemaignan S(2023)SoGrIn: a Non-Verbal Dataset of Social Group-Level Interactions2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)10.1109/RO-MAN57019.2023.10309351(2632-2637)Online publication date: 28-Aug-2023
https://doi.org/10.1109/RO-MAN57019.2023.10309351
Show More Cited By

Index Terms

The unobtrusive group interaction (UGI) corpus
1. Applied computing
  1. Law, social and behavioral sciences
    1. Psychology
2. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing systems and tools

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cover image ACM Conferences

MMSys '19: Proceedings of the 10th ACM Multimedia Systems Conference

June 2019

374 pages

ISBN:9781450362979

DOI:10.1145/3304109

General Chair:
Michael Zink
University of Massachusetts Amherst
,
Program Chairs:
Laura Toni
University College London
,
Ali C. Begen
Özyeğin University/Networked Media

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 18 June 2019

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MMSys '19: 10th ACM Multimedia Systems Conference

June 18 - 21, 2019

Massachusetts, Amherst

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MMSys '19 Paper Acceptance Rate 40 of 82 submissions, 49%;

Overall Acceptance Rate 176 of 530 submissions, 33%

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Cited By

Beyan CVinciarelli ABue A(2023)Co-Located Human–Human Interaction Analysis Using Nonverbal Cues: A SurveyACM Computing Surveys10.1145/362651656:5(1-41)Online publication date: 25-Nov-2023
https://dl.acm.org/doi/10.1145/3626516
Lin YLiu YLee C(2023)An Interaction-process-guided Framework for Small-group Performance PredictionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/355876819:2(1-25)Online publication date: 6-Feb-2023
https://doi.org/10.1145/3558768
Webb NGiuliani MLemaignan S(2023)SoGrIn: a Non-Verbal Dataset of Social Group-Level Interactions2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)10.1109/RO-MAN57019.2023.10309351(2632-2637)Online publication date: 28-Aug-2023
https://doi.org/10.1109/RO-MAN57019.2023.10309351
Kubasova UMurray GTruong KHeylen DCzerwinski MBerthouze NChetouani MNakano M(2020)Group Performance Prediction with Limited ContextCompanion Publication of the 2020 International Conference on Multimodal Interaction10.1145/3395035.3425964(191-195)Online publication date: 25-Oct-2020
https://dl.acm.org/doi/10.1145/3395035.3425964
Lin YLee C(2020)Predicting Performance Outcome with a Conversational Graph Convolutional Network for Small Group InteractionsICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP40776.2020.9053308(8044-8048)Online publication date: May-2020
https://doi.org/10.1109/ICASSP40776.2020.9053308

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