WhoLoDancE
Published December 31, 2017 | Version 0.2
Project deliverable Open

WhoLoDancE: Deliverable 3.4 - Report on multimodal signal modelling

Creators

  • 1. Università degli Studi di Genova

Description

This deliverable serves to summarize the input devices, data formats and methodologies adopted in the process of developing algorithms, software modules and applications for movement principle and qualities analysis.

Section 1 introduces the report and lists its objectives whereas Section 2 gives an overview of the data capture systems that are used in the context of the project; in particular, a description of professional motion capture systems, used during the production of the WhoLoDancE repository, and low-end capture devices, that are used in the low-cost applications, is given.

Section 3 introduces the methodology followed in the design and development of movement analysis algorithm and software modules, in particular, a conceptual framework is described where the qualities of movement are organized in a hierarchical way, going from physical signals to abstract, complex concepts.

Files

D3.4 - Report on multimodal signal modelling_0.2.pdf

Files (1.6 MB)

Additional details

Funding

WhoLoDancE – Whole-Body Interaction Learning for Dance Education 688865
European Commission

References

  • Camurri, A., Krumhansl, C. L., Mazzarino, B., & Volpe, G. (2004). An exploratory study of anticipating human movement in dance. the Proceedings of this same Conference.
  • Glowinski, D., Dael, N., Camurri, A., Volpe, G., Mortillaro, M., & Scherer, K. (2011). Toward a minimal representation of affective gestures. IEEE Transactions on Affective Computing, 2, 106-118.
  • Glowinski, D., Gnecco, G., Piana, S., & Camurri, A. (2013). Expressive non-verbal interaction in string quartet. Affective Computing and Intelligent Interaction (ACII), 2013 Humaine Association Conference on, (pp. 233-238).
  • Kleinsmith, A., & Bianchi-Berthouze, N. (2013). Affective body expression perception and recognition: A survey. IEEE Transactions on Affective Computing, 4, 15-33.
  • Piana, S., Staglianò, A., Odone, F., & Camurri, A. (2016). Adaptive body gesture representation for automatic emotion recognition. ACM Transactions on Interactive Intelligent Systems (TiiS), 6, 6.
  • Richman, J. S., & Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of Physiology-Heart and Circulatory Physiology, 278, H2039--H2049.