Skip to main content
SpringerLink
Log in

An Exploratory Study on Group Potency Classification from Non-verbal Social Behaviours

  • Conference paper
  • First Online:
Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges (ICPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13643))

Included in the following conference series:

Abstract

Technological research is increasingly focusing on intelligent computer systems that can elicit collaboration in groups made of a mix of humans and machines. These systems have to devise appropriate strategies of intervention in the joint action, a capability that requires them to be able of sensing group processes such as emergent states. Among those, group potency – i.e., the confidence a group has that it can be effective – has a particular relevance. An intervention targeted at increasing potency can indeed increment the overall performance of the group. As an initial step in this direction, this work addresses automated classification of potency from multimodal group behaviour. Interactions by 16 different groups displaying low or high potency were extracted from 3 already existing datasets: AMI, TA2, and GAME-ON. Logistic Regression, Support Vector Machines, and Random Forests were used for classification. Results show that all the classifiers can predict potency, and that a classifier trained with samples from 2 of the datasets can predict the label of a sample from the third dataset. ...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Audacity® software is copyright © 1999–2021 Audacity Team. The name Audacity® is a registered trademark.

References

  1. Ambady, N., Rosenthal, R.: Thin slices of expressive behavior as predictors of interpersonal consequences: a meta-analysis. Psychol. Bull. 111(2), 256 (1992)

    Article  Google Scholar 

  2. Bishop, C.M., Nasrabadi, N.M.: Pattern Recognition and Machine Learning, vol. 4, pp. 291–357. Springer, New York (2006)

    Google Scholar 

  3. Bredin, H., et al.: Pyannote. audio: neural building blocks for speaker diarization. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 7124–7128. IEEE (2020)

    Google Scholar 

  4. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

    Article  MATH  Google Scholar 

  5. Cao, Z., Hidalgo Martinez, G., Simon, T., Wei, S., Sheikh, Y.A.: Openpose: realtime multi-person 2D pose estimation using part affinity fields. IEEE Trans. Pattern Anal. Mach. Intell. 43, 172–186 (2019)

    Article  Google Scholar 

  6. Castro-Hernández, A., Swigger, K., Cemile Serce, F., Lopez, V.: Classification of Group Potency Levels of Software Development Student Teams. Polibits (51), 55–62 (2015). 10.17562/PB-51-8, publisher: Instituto Politécnico Nacional, Centro de Innovación y Desarrollo Tecnológico en Cómputo

    Google Scholar 

  7. Ceccaldi, E., Lehmann-Willenbrock, N., Volta, E., Chetouani, M., Volpe, G., Varni, G.: How unitizing affects annotation of cohesion. In: 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII), pp. 1–7. IEEE (2019)

    Google Scholar 

  8. Chan, D.: Functional relations among constructs in the same content domain at different levels of analysis: a typology of composition models. J. Appl. Psychol. 83(2), 234 (1998)

    Article  Google Scholar 

  9. Duffner, S., Motlicek, P., Korchagin, D.: The TA2 database - a multi-modal database from home entertainment. Int. J. Comput. Electrical Eng. 670–673 (2012). https://doi.org/10.7763/IJCEE.2012.V4.581

  10. D’Amato, V., Volta, E., Oneto, L., Volpe, G., Camurri, A., Anguita, D.: Understanding violin players’ skill level based on motion capture: a data-driven perspective. Cogn. Comput. 12(6), 1356–1369 (2020)

    Article  Google Scholar 

  11. Eyben, F., Wöllmer, M., Schuller, B.: Opensmile: the Munich versatile and fast open-source audio feature extractor. In: Proceedings of the 18th ACM international conference on Multimedia, pp. 1459–1462 (2010)

    Google Scholar 

  12. Friedman, J., Hastie, T., Tibshirani, R.: Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33(1), 1 (2010)

    Article  Google Scholar 

  13. Gamero, N., Zornoza, A., Peiró, J.M., Picazo, C.: Roles of participation and feedback in group potency. Psychol. Rep. 105(1), 293–313 (2009). https://doi.org/10.2466/PR0.105.1.293-313

    Article  Google Scholar 

  14. Gatica-Perez, D.: Automatic nonverbal analysis of social interaction in small groups: a review. Image Vis. Comput. 27(12), 1775–1787 (2009). https://doi.org/10.1016/j.imavis.2009.01.004

    Article  Google Scholar 

  15. Gibson, C.B., Randel, A.E., Earley, P.C.: Understanding group efficacy: an empirical test of multiple assessment methods. Group Organ. Manag. 25(1), 67–97 (2000). https://doi.org/10.1177/1059601100251005, publisher: SAGE Publications Inc

  16. Gully, S.M., Incalcaterra, K.A., Joshi, A., Beaubien, J.M.: A meta-analysis of team-efficacy, potency, and performance: interdependence and level of analysis as moderators of observed relationships. J. Appl. Psychol. 87(5), 819–832 (2002). https://doi.org/10.1037/0021-9010.87.5.819

    Article  Google Scholar 

  17. Guzzo, R.A., Yost, P.R., Campbell, R.J., Shea, G.P.: Potency in groups: articulating a construct. Br. J. Soc. Psychol. 32(1), 87–106 (1993). https://doi.org/10.1111/j.2044-8309.1993.tb00987.x

    Article  Google Scholar 

  18. Hung, H., Gatica-Perez, D.: Estimating cohesion in small groups using audio-visual nonverbal behavior. IEEE Trans. Multimedia 12(6), 563–575 (2010). https://doi.org/10.1109/TMM.2010.2055233

    Article  Google Scholar 

  19. Hupont, I., Chetouani, M.: Region-based facial representation for real-time action units intensity detection across datasets. Pattern Anal. Appl. 22(2), 477–489 (2019)

    Article  MathSciNet  Google Scholar 

  20. Jadhav, N., Sugandhi, R.: Survey on human behavior recognition using affective computing. In: 2018 IEEE Global Conference on Wireless Computing and Networking (GCWCN), pp. 98–103, November 2018. https://doi.org/10.1109/GCWCN.2018.8668632

  21. de Jong, A., de Ruyter, K., Wetzels, M.: Antecedents and consequences of group potency: a study of self-managing service teams. Manage. Sci. 51(11), 1610–1625 (2005). https://doi.org/10.1287/mnsc.1050.0425

    Article  Google Scholar 

  22. Kleinbaum, D.G., Dietz, K., Gail, M., Klein, M., Klein, M.: Logistic Regression. Springer, New York (2002)

    Google Scholar 

  23. Kozlowski, S., Klein, K.: A multilevel approach to theory and research in organizations: contextual, temporal, and emergent processes. Multi-level theory, research, and methods in organizations: Foundations, extensions, and new directions, October 2012

    Google Scholar 

  24. Kozlowski, S.W., Ilgen, D.R.: Enhancing the effectiveness of work groups and teams. Psychol. Sci. Public Interest 7(3), 77–124 (2006). https://doi.org/10.1111/j.1529-1006.2006.00030.x, publisher: SAGE Publications Inc

  25. Lawson, R.G., Jurs, P.C.: New index for clustering tendency and its application to chemical problems. J. Chem. Inf. Comput. Sci. 30(1), 36–41 (1990)

    Article  Google Scholar 

  26. Lee, C., Farh, J.L., Chen, Z.J.: Promoting group potency in project teams: the importance of group identification. J. Organ. Behav. 32(8), 1147–1162 (2011). https://doi.org/10.1002/job.741

    Article  Google Scholar 

  27. Lee, C., Tinsley, C.H., Bobko, P.: An investigation of the antecedents and consequences of group-level confidence1. J. Appl. Soc. Psychol. 32(8), 1628–1652 (2002). https://doi.org/10.1111/j.1559-1816.2002.tb02766.x

    Article  Google Scholar 

  28. Lester, S.W., Meglino, B.M., Korsgaard, M.A.: The antecedents and consequences of group potency: a longitudinal investigation of newly formed work groups. Acad. Manag. J. 45(2), 352–368 (2002)

    Article  Google Scholar 

  29. Levine, J.M., Hogg, M.A. (eds.): Encyclopedia of Group Processes & Intergroup Relations. SAGE Publications, Thousand Oaks, Calif (2010). oCLC: ocn251215605

    Google Scholar 

  30. Liu, H., Shah, S., Jiang, W.: On-line outlier detection and data cleaning. Comput. Chem. Eng. 28(9), 1635–1647 (2004)

    Article  Google Scholar 

  31. Maimon, O.Z., Rokach, L.: Data Mining with Decision Trees: Theory and Applications, vol. 81. World scientific (2014)

    Google Scholar 

  32. Malone, T.W.: How human-computer’ superminds’ are redefining the future of work. MIT Sloan Manag. Rev. 59(4), 34–41 (2018)

    Google Scholar 

  33. Maman, L., et al.: GAME-ON: a multimodal dataset for cohesion and group analysis. IEEE Access 8, 124185–124203 (2020). https://doi.org/10.1109/ACCESS.2020.3005719, conference Name: IEEE Access

  34. Maman, L., Likforman-Sulem, L., Chetouani, M., Varni, G.: Exploiting the interplay between social and task dimensions of cohesion to predict its dynamics leveraging social sciences. In: Proceedings of the 2021 International Conference on Multimodal Interaction. ICMI 2021, New York, NY, USA, pp. 16–24. Association for Computing Machinery, October 2021. https://doi.org/10.1145/3462244.3479940

  35. Marks, M.A.: A Temporally Based Framework and Taxonomy of Team Processes, p. 22 (2001)

    Google Scholar 

  36. Mathieu, J., Maynard, M.T., Rapp, T., Gilson, L.: Team effectiveness 1997–2007: a review of recent advancements and a glimpse into the future. J. Manag. 34(3), 410–476 (2008). https://doi.org/10.1177/0149206308316061

    Article  Google Scholar 

  37. Mathieu, J.E., Heffner, T.S., Goodwin, G.F., Salas, E., Cannon-Bowers, J.A.: The influence of shared mental models on team process and performance. J. Appl. Psychol. 85(2), 273 (2000)

    Article  Google Scholar 

  38. MATLAB: version 7.10.0 (R2010a). The MathWorks Inc., Natick, Massachusetts (2010)

    Google Scholar 

  39. Mccowan, I., et al.: The AMI meeting corpus. In: Noldus, L.P.J.J., Grieco, F., Loijens, L.W.S., Zimmerman, P.H. (eds.) Proceedings Measuring Behavior 2005, 5th International Conference on Methods and Techniques in Behavioral Research. Noldus Information Technology, Wageningen (2005)

    Google Scholar 

  40. Müller, P., Huang, M.X., Bulling, A.: Detecting low rapport during natural interactions in small groups from non-verbal behaviour. In: 23rd International Conference on Intelligent User Interfaces, pp. 153–164, Tokyo Japan. ACM, March 2018. https://doi.org/10.1145/3172944.3172969

  41. Piana, S., Staglianò, A., Camurri, A., Odone, F.: A set of full-body movement features for emotion recognition to help children affected by autism spectrum condition. In: IDGEI International Workshop (2013)

    Google Scholar 

  42. Rapp, T., Maynard, T., Domingo, M., Klock, E.: Team emergent states: what has emerged in the literature over 20 years. Small Group Res. 52(1), 68–102 (2021)

    Article  Google Scholar 

  43. Satterstrom, P., Polzer, J.T., Kwan, L.B., Hauser, O.P., Wiruchnipawan, W., Burke, M.: Thin slices of workgroups. Organ. Behav. Hum. Decis. Process. 151, 104–117 (2019). https://doi.org/10.1016/j.obhdp.2018.12.007

    Article  Google Scholar 

  44. Schafer, R.W.: What is a savitzky-golay filter?[lecture notes]. IEEE Signal Process. Mag. 28(4), 111–117 (2011)

    Article  Google Scholar 

  45. Schuller, B., Steidl, S., Batliner, A.: The interspeech 2009 emotion challenge (2009)

    Google Scholar 

  46. Seeber, I., et al.: Machines as teammates: a collaboration research agenda (2018)

    Google Scholar 

  47. Tartaglione, E., Biancardi, B., Mancini, M., Varni, G.: A hitchhiker’s guide towards transactive memory system modeling in small group interactions. In: Companion Publication of the 2021 International Conference on Multimodal Interaction, pp. 254–262 (2021)

    Google Scholar 

  48. Tomar, S.: Converting video formats with ffmpeg. Linux J. 2006(146), 10 (2006)

    Google Scholar 

  49. Tröster, C., Mehra, A., van Knippenberg, D.: Structuring for team success: the interactive effects of network structure and cultural diversity on team potency and performance. Organ. Behav. Hum. Decis. Process. 124(2), 245–255 (2014). https://doi.org/10.1016/j.obhdp.2014.04.003

    Article  Google Scholar 

  50. Tsay, C.J.: The vision heuristic: judging music ensembles by sight alone. Organ. Behav. Hum. Decis. Process. 124(1), 24–33 (2014). https://doi.org/10.1016/j.obhdp.2013.10.003

    Article  Google Scholar 

  51. Volmer, J.: Catching leaders’ mood: contagion effects in teams. Adm. Sci. 2(3), 203–220 (2012). https://doi.org/10.3390/admsci2030203, number: 3 Publisher: Molecular Diversity Preservation International

  52. Woodley, H.J.R., McLarnon, M.J.W., O’Neill, T.A.: The emergence of group potency and its implications for team effectiveness. Front. Psychol. 10, 992 (2019). https://doi.org/10.3389/fpsyg.2019.00992

    Article  Google Scholar 

Download references

Acknowledgment

Portions of the research in this paper used the TA2 Dataset made available by the Idiap Research Institute, Martigny, Switzerland. The work of Giovanna Varni has been partially supported by the French National Research Agency (ANR) in the framework of its JCJC program (GRACE, project ANR-18-CE33-0003-01, funded under the Artificial Intelligence Plan).

Author information

Authors and Affiliations

  1. Casa Paganini, InfoMus, DIBRIS, University of Genoa, Genoa, Italy

    Nicola Corbellini, Eleonora Ceccaldi & Gualtiero Volpe

  2. LTCI, Télécom Paris, Institut polytechnique de Paris, S91120, Palaiseau, France

    Giovanna Varni

Authors
  1. Nicola Corbellini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eleonora Ceccaldi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giovanna Varni
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gualtiero Volpe
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nicola Corbellini .

Editor information

Editors and Affiliations

  1. York University, Toronto, ON, Canada

    Jean-Jacques Rousseau

  2. Ontario Tech University, Oshawa, ON, Canada

    Bill Kapralos

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Corbellini, N., Ceccaldi, E., Varni, G., Volpe, G. (2023). An Exploratory Study on Group Potency Classification from Non-verbal Social Behaviours. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13643. Springer, Cham. https://doi.org/10.1007/978-3-031-37660-3_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-37660-3_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37659-7

  • Online ISBN: 978-3-031-37660-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation