More Web Proxy on the site http://driver.im/

research-article

Movement sequence analysis using hidden Markov models: a case study in Tai Chi performance

Authors:

Jules Françoise,

Agnès Roby-Brami,

Natasha Riboud,

Frédéric BevilacquaAuthors Info & Claims

MOCO '15: Proceedings of the 2nd International Workshop on Movement and Computing

Pages 29 - 36

https://doi.org/10.1145/2790994.2791006

Published: 14 August 2015 Publication History

Abstract

Movement sequences are essential to dance and expressive movement practice; yet, they remain underexplored in movement and computing research, where the focus on short gestures prevails. We propose a method for movement sequence analysis based on motion trajectory synthesis with Hidden Markov Models. The method uses Hidden Markov Regression for jointly synthesizing motion feature trajectories and their associated variances, that serves as basis for investigating performers' consistency across executions of a movement sequence. We illustrate the method with a use-case in Tai Chi performance, and we further extend the approach to cross-modal analysis of vocalized movements.

References

[1]

Bevilacqua, F., Baschet, F., and Lemouton, S. The augmented string quartet: Experiments and gesture following. Journal of New Music Research 41, 1 (2012), 103--119.

[2]

Bevilacqua, F., Zamborlin, B., Sypniewski, A., Schnell, N., Guédy, F., and Rasamimanana, N. Continuous realtime gesture following and recognition. Gesture in Embodied Communication and Human-Computer Interaction (2010), 73--84.

[3]

Brand, M., and Hertzmann, A. Style machines. Proceedings of the 27th annual conference on Computer graphics and interactive techniques - SIGGRAPH '00 (2000), 183--192.

Digital Library

[4]

Calinon, S., D'halluin, F., Sauser, E., Caldwell, D., and Billard, A. Learning and reproduction of gestures by imitation: An approach based on Hidden Markov Model and Gaussian Mixture Regression. Robotics & Automation Magazine, IEEE 17, 2 (2010), 44--54.

[5]

Camurri, A., Hashimoto, S., and Ricchetti, M. Eyesweb: Toward gesture and affect recognition in interactive dance and music systems. Computer Music Journal 24, 1 (2000), 57--69.

Digital Library

[6]

Camurri, A., Mazzarino, B., and Ricchetti, M. Multimodal analysis of expressive gesture in music and dance performances. Gesture-Based Communication in Human-Computer Interaction (2004), 20--39.

[7]

Caramiaux, B., Wanderley, M. M., and Bevilacqua, F. Segmenting and Parsing Instrumentalist's Gestures. Journal of New Music Research 41, 1 (2012), 1--27.

[8]

Eickeler, S., Kosmala, A., and Rigoll, G. Hidden markov model based continuous online gesture recognition. Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on 2 (1998), 1206--1208.

Digital Library

[9]

Fdili Alaoui, S., Caramiaux, B., Serrano, M., and Bevilacqua, F. Movement Qualities as Interaction Modality. In ACM Designing Interactive Systems (DIS 2012) (Newcastle, UK, 2012).

Digital Library

[10]

Ferguson, S., Schubert, E., and Stevens, C. Dynamic dance warping: Using dynamic time warping to compare dance movement performed under different conditions. In Proceedings of the International Workshop on Movement and Computing, MOCO'14, ACM (Paris, France, 2014), 94--99.

Digital Library

[11]

Fine, S., Singer, Y., and Tishby, N. The hierarchical hidden Markov model: Analysis and applications. Machine learning 32, 1 (1998), 41--62.

Digital Library

[12]

Françoise, J. Realtime Segmentation and Recognition of Gestures using Hierarchical Markov Models. Master's thesis, Université Pierre et Marie Curie, Ircam, 2011.

[13]

Françoise, J., Fdili Alaoui, S., Schiphorst, T., and Bevilacqua, F. Vocalizing Dance Movement for Interactive Sonification of Laban Effort Factors. In Proceedings of the 2014 Conference on Designing Interactive Systems, DIS '14, ACM (Vancouver, Canada, 2014), 1079--1082.

Digital Library

[14]

Françoise, J., Schnell, N., and Bevilacqua, F. A Multimodal Probabilistic Model for Gesture-based Control of Sound Synthesis. In Proceedings of the 21st ACM international conference on Multimedia (MM'13) (Barcelona, Spain, 2013), 705--708.

Digital Library

[15]

Gillian, N., Knapp, B., and O'Modhrain, S. Recognition Of Multivariate Temporal Musical Gestures Using N-Dimensional Dynamic Time Warping. In Proceedings of the 2011 International Conference on New Interfaces for Musical Expression (NIME'11), Oslo, Norway (Oslo, Norway, 2011), 337--342.

[16]

Karg, M., Seiber, W., Hoey, J., and Kulic, D. Human Movement Analysis: Extension of the F-Statistic to Time Series Using HMM. In IEEE International Conference on Systems, Man, and Cybernetics, IEEE (Oct. 2013), 3870--3875.

Digital Library

[17]

Karg, M., Venture, G., Hoey, J., and Kulić, D. Human movement analysis as a measure for fatigue: a hidden Markov-based approach. Neural Systems and Rehabilitation Engineering, IEEE Transactions on 22, 3 (May 2014), 470--81.

[18]

McNeill, D. Hand and mind: What gestures reveal about thought. University of Chicago Press, 1996.

[19]

Pohl, H., and Hadjakos, A. Dance Pattern Recognition using Dynamic Time Warping. In Proceedings of the Sound and Music Computing Conference (2010).

[20]

Rabiner, L. R. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE 77, 2 (1989), 257--286.

[21]

Rasamimanana, N., Kaiser, F., and Bevilacqua, F. Perspectives on gesture-sound relationships informed from acoustic instrument studies. Organised Sound 14, 02 (2009), 208--216.

Digital Library

[22]

Tilmanne, J. Data-driven Stylistic Humanlike Walk Synthesis. Phd dissertation, University of Mons, 2013.

[23]

Tilmanne, J., Moinet, A., and Dutoit, T. Stylistic gait synthesis based on hidden Markov models. EURASIP Journal on Advances in Signal Processing 2012, 1 (2012), 72.

[24]

Yang, J., and Bolt, J. Tai Chi Sword Classical Yang Style 2nd ed. YMAA Publication Center, Inc., 2015.

Cited By

Trabelsi IFrancoise JBellik Y(2022)Sensor-based Activity Recognition using Deep Learning: A Comparative StudyProceedings of the 8th International Conference on Movement and Computing10.1145/3537972.3537996(1-8)Online publication date: 22-Jun-2022
https://dl.acm.org/doi/10.1145/3537972.3537996
Martin J(2020)Designing Human-Object Performances using Theatre Practices and Machine LearningProceedings of the 7th International Conference on Movement and Computing10.1145/3401956.3404248(1-4)Online publication date: 15-Jul-2020
https://dl.acm.org/doi/10.1145/3401956.3404248
Stepputat KKienreich WDick C(2019)Digital Methods in Intangible Cultural Heritage ResearchJournal on Computing and Cultural Heritage 10.1145/327995112:2(1-22)Online publication date: 7-May-2019
https://dl.acm.org/doi/10.1145/3279951
Show More Cited By

Index Terms

Movement sequence analysis using hidden Markov models: a case study in Tai Chi performance

Recommendations

Coding with partially hidden Markov models
DCC '95: Proceedings of the Conference on Data Compression

Partially hidden Markov models (PHMM) are introduced. They are a variation of the hidden Markov models (HMM) combining the power of explicit conditioning on past observations and the power of using hidden states. (P)HMM may be combined with arithmetic ...
Structural hidden Markov models: An application to handwritten numeral recognition

We introduce in this paper a generalization of the widely used hidden Markov models (HMM's), which we name "structural hidden Markov models" (SHMM). Our approach is motivated by the need of modeling complex structures which are encountered in many ...
The evaluation problem in discrete semi-hidden Markov models

This paper is devoted to discrete semi-hidden Markov models (SHMM), which are related to the well-known hidden Markov models (HMM). In particular, the HMM associated to an SHMM is defined, and the forward algorithm for solving the evaluation problem in ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

MOCO '15: Proceedings of the 2nd International Workshop on Movement and Computing

August 2015

175 pages

ISBN:9781450334570

DOI:10.1145/2790994

Conference Chairs:
Sarah Fdili Alaoui
SIAT, SFU, Vancouver, Canada
,
Philippe Pasquier
SIAT, SFU, Vancouver, Canada
,
Thecla Schiphorst
SIAT, SFU, Vancouver, Canada
,
Jules Françoise
Ircam, Paris, France
,
Frédéric Bevilacqua
Ircam, Paris, France

Copyright © 2015 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: 14 August 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Agence Nationale de la Recherche

Conference

MOCO '15

MOCO '15: Intersecting Art, Meaning, Cognition, Technology

August 14 - 15, 2015

British Columbia, Vancouver, Canada

Acceptance Rates

MOCO '15 Paper Acceptance Rate 26 of 56 submissions, 46%;

Overall Acceptance Rate 85 of 185 submissions, 46%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
247
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)2

Reflects downloads up to 25 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Trabelsi IFrancoise JBellik Y(2022)Sensor-based Activity Recognition using Deep Learning: A Comparative StudyProceedings of the 8th International Conference on Movement and Computing10.1145/3537972.3537996(1-8)Online publication date: 22-Jun-2022
https://dl.acm.org/doi/10.1145/3537972.3537996
Martin J(2020)Designing Human-Object Performances using Theatre Practices and Machine LearningProceedings of the 7th International Conference on Movement and Computing10.1145/3401956.3404248(1-4)Online publication date: 15-Jul-2020
https://dl.acm.org/doi/10.1145/3401956.3404248
Stepputat KKienreich WDick C(2019)Digital Methods in Intangible Cultural Heritage ResearchJournal on Computing and Cultural Heritage 10.1145/327995112:2(1-22)Online publication date: 7-May-2019
https://dl.acm.org/doi/10.1145/3279951
Hachaj TOgiela M(2018)Classification of Karate Kicks with Hidden Markov Models Classifier and Angle-Based Features2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)10.1109/CISP-BMEI.2018.8633251(1-5)Online publication date: Oct-2018
https://doi.org/10.1109/CISP-BMEI.2018.8633251
Shen YWang HHo EYang LShum H(2017)Posture-based and action-based graphs for boxing skill visualizationComputers and Graphics10.1016/j.cag.2017.09.00769:C(104-115)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1016/j.cag.2017.09.007
Bevilacqua FCaramiaux BFrançoise JManitsaris S(2016)Perspectives on Real-time Computation of Movement CoarticulationProceedings of the 3rd International Symposium on Movement and Computing10.1145/2948910.2948956(1-5)Online publication date: 5-Jul-2016
https://dl.acm.org/doi/10.1145/2948910.2948956
Tits MTilmanne Jd'Alessandro NManitsaris S(2016)A Novel Tool for Motion Capture Database Factor Statistical ExplorationProceedings of the 3rd International Symposium on Movement and Computing10.1145/2948910.2948923(1-8)Online publication date: 5-Jul-2016
https://dl.acm.org/doi/10.1145/2948910.2948923

View Options

Login options

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

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten