skip to main content
10.1145/2493432.2493513acmconferencesArticle/Chapter ViewAbstractPublication PagesubicompConference Proceedingsconference-collections
short-paper

Classifying social actions with a single accelerometer

Published:08 September 2013Publication History

ABSTRACT

In this paper, we estimate different types of social actions from a single body-worn accelerometer in a crowded social setting. Accelerometers have many advantages in such settings: they are impervious to environmental noise, unobtrusive, cheap, low-powered, and their readings are specific to a single person. Our experiments show that they are surprisingly informative of different types of social actions. The social actions we address in this paper are whether a person is speaking, laughing, gesturing, drinking, or stepping. To our knowledge, this is the first work to carry out experiments on estimating social actions from conversational behavior using only a wearable accelerometer. The ability to estimate such actions using just the acceleration opens up the potential for analyzing more about social aspects of people's interactions without explicitly recording what they are saying.

References

  1. L. Bao and S. Intille. Activity recognition from user-annotated acceleration data. Pervasive Computing, pages 1--17, 2004.Google ScholarGoogle ScholarCross RefCross Ref
  2. C. Cattuto, W. Van den Broeck, A. Barrat, V. Colizza, J. Pinton, and A. Vespignani. Dynamics of Person-to-Person Interactions from Distributed RFID Sensor Networks. PLOS ONE, 5(7):e11596, 07 2010.Google ScholarGoogle ScholarCross RefCross Ref
  3. T. L. Chartrand and J. A. Bargh. The chameleon effect: the perception-behavior link and social interaction. Journal of Personality and Social Psychology, 76(6):893--910, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  4. C. Doukas, I. Maglogiannis, P. Tragas, D. Liapis, and G. Yovanof. Patient fall detection using support vector machines. Artificial Intelligence and Innovations 2007: from Theory to Applications, pages 147--156, 2007.Google ScholarGoogle Scholar
  5. H. Hung and D. Gatica-Perez. Estimating cohesion in small groups using audio-visual nonverbal behavior. Multimedia, IEEE Transactions on, 12(6):563--575, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. D. Jayagopi, H. Hung, C. Yeo, and D. Gatica-Perez. Modeling dominance in group conversations from non-verbal activity cues. IEEE Transactions on Audio, Speech and Language Processing, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. A. Kendon. Movement coordination in social interaction: Some examples described. Acta Psychologica, 32:101--125, 1970.Google ScholarGoogle ScholarCross RefCross Ref
  8. A. Kendon. Conducting Interaction: Patterns of Behavior in Focused Encounters. Cambridge University Press, 1990.Google ScholarGoogle Scholar
  9. J. R. Kwapisz, G. M. Weiss, and S. A. Moore. Activity recognition using cell phone accelerometers. ACM SIGKDD Explorations Newsletter, 12(2):74--82, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. B. Lepri, J. Staiano, G. Rigato, K. Kalimeri, A. Finnerty, F. Pianesi, N. Sebe, and A. Pentland. The SocioMetric Badges Corpus: A Multilevel Behavioral Dataset for Social Behavior in Complex Organizations. In SocialCom/PASSAT, pages 623--628. IEEE, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. M. S. Mast. Dominance as Expressed and Inferred Through Speaking Time. Human Communication Research, (3):420--450, July 2002.Google ScholarGoogle Scholar
  12. D. McNeill. Language and Gesture. Cambridge University Press New York, 2000.Google ScholarGoogle ScholarCross RefCross Ref
  13. D. O. Olguin, B. N. Waber, T. Kim, A. Mohan, K. Ara, and A. Pentland. Sensible Organizations: Technology and Methodology for Automatically Measuring Organizational Behavior. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 39(1), 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. K. O'Quin and J. Aronoff. Humor as a Technique of Social Influence. Social Psychology Quarterly, 44(4):349--357, Dec. 1981.Google ScholarGoogle ScholarCross RefCross Ref
  15. S. Reddy, M. Mun, J. Burke, D. Estrin, M. Hansen, and M. Srivastava. Using mobile phones to determine transportation modes. ACM Transactions on Sensor Networks (TOSN), 6(2):13, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. E. J. Romero and K. W. Cruthirds. The Use of Humor in the Workplace. Academy of Management Perspectives, 20(2):58--69, 2006.Google ScholarGoogle ScholarCross RefCross Ref
  17. D. Wyatt, T. Choudhury, J. Bilmes, and J. A. Kitts. Inferring colocation and conversation networks from privacy-sensitive audio with implications for computational social science. ACM Trans. Intell. Syst. Technol., 2(1):7:1--7:41, Jan. 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. T. Zhang, J. Wang, P. Liu, and J. Hou. Fall detection by embedding an accelerometer in cellphone and using KFD algorithm. International Journal of Computer Science and Network Security, 6(10):277--284, 2006.Google ScholarGoogle Scholar

Index Terms

  1. Classifying social actions with a single accelerometer

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        UbiComp '13: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
        September 2013
        846 pages
        ISBN:9781450317702
        DOI:10.1145/2493432

        Copyright © 2013 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].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 8 September 2013

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • short-paper

        Acceptance Rates

        UbiComp '13 Paper Acceptance Rate92of394submissions,23%Overall Acceptance Rate764of2,912submissions,26%

        Upcoming Conference

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader