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.

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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

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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].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 August 2015

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Agence Nationale de la Recherche

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MOCO '15

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

August 14 - 15, 2015

British Columbia, Vancouver, Canada

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

Overall Acceptance Rate 85 of 185 submissions, 46%

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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

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