Skip to main content

A Meeting Log Structuring System Using Wearable Sensors

  • 815 Accesses

Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT,volume 22)

Abstract

We propose a system that structures a meeting log by detecting and tagging the participants’ actions in the meeting using acceleration sensors. The proposed system detects head movement such as nodding of each participant or motion during utterances by using acceleration sensors attached to the heads of all participants in a meeting. In addition, we developed a Meeting Review Tree, which is an application that recognizes a meeting participants’ utterances and three kinds of actions using acceleration and angular velocity sensors and tags them to recorded movies. In the proposed system, the structure of the meeting is hierarchized into three layers and tagged contexts as follows: The first layer represents the transition of the reporter during the meeting, the second layer represents changes in information of speakers in the report, and the third layer represents motions such as nodding. As a result of the evaluation experiment, the recognition accuracy of the stratified first layer was 57.0% and that of the second layer was 61.0%.

Keywords

  • Logic Meeting
  • Head Motion Detection
  • Correct Answer Data
  • Utterance Label
  • Utterance Recognition

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-98530-5_75
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   229.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-98530-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   299.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.

References

  1. VoiceGraphy. http://jpn.nec.com/voicegraphy/

  2. Advanced Media Inc. http://www.advanced-media.co.jp/products/service/amivoice-speechwriter

  3. Morency, L.P., de Kok, I., Gratch, J.: Context-based recognition during human interactions: automatic feature selection and encoding dictionary. In: Proceedings of the 10th International Conference on Multimodal Interfaces (ICMI 2008), pp. 181–188, October 2008

    Google Scholar 

  4. Wohler, N., Grosekathofer, U., Dierker, A., Hanheide, M., Kopp, S., Hermann, T.: A calibration-free head gesture recognition system with online capability. In: Proceedings of the 20th International Conference on Pattern Recognition (ICPR 2010), pp. 3814–3817, August 2010

    Google Scholar 

  5. Kawahara, T.: Multi-modal sensing and analysis of poster conversations toward smart posterboard. In: Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 1–9 (2012)

    Google Scholar 

  6. Sumi, Y., Yano, M., Nishida, T. Analysis environment of conversational structure with nonverbal multimodal data. In: Proceedings of the International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction (ICMI-MLMI 2010), p. 44 (2010)

    Google Scholar 

  7. Kumano, S., Otsuka, K., Mikami, D., Junji, Y.: Recognizing communicative facial expressions for discovering interpersonal emotions in group meetings. In: Proceedings of the 11th International Conference on Multimodal Interfaces (ICMI-MLMI 2009), pp. 99–106, September 2009

    Google Scholar 

  8. Otsuka, K., Yamato, J., Takemae, Y., Murase, H.: quantifying interpersonal influence in face-to-face conversations based on visual attention patterns. In: Proceedings of the 4th ACM Conference on Human Factors in Computing Systems (CHI 2006), pp. 1175–1180, April 2006

    Google Scholar 

  9. Wireless Technologies Inc. http://www.wireless-t.jp/

  10. MR300. http://www.kingjim.co.jp/sp/mr360/

Download references

Acknowledgements

This research was supported in part by a grant in aid for Precursory Research for Embryonic Science and Technology (PRESTO) from the Japan Science and by a grant in aid for Scientific Research (18H01059) from the Ministry of Education, Culture, Sports, Science and Technology.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tsutomu Terada .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Ohnishi, A., Murao, K., Terada, T., Tsukamoto, M. (2019). A Meeting Log Structuring System Using Wearable Sensors. In: Barolli, L., Kryvinska, N., Enokido, T., Takizawa, M. (eds) Advances in Network-Based Information Systems. NBiS 2018. Lecture Notes on Data Engineering and Communications Technologies, vol 22. Springer, Cham. https://doi.org/10.1007/978-3-319-98530-5_75

Download citation