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Teaching a digital performing agent: artificial neural network and hidden Markov Model for recognising and performing dance movement

McCormick,J, Vincs,K, Nahavandi,S, Creighton,D and Hutchison,S 2014, Teaching a digital performing agent: artificial neural network and hidden Markov Model for recognising and performing dance movement, in ACM International Conference Proceeding Series, Association for Computing Machinery, New York, N.Y., pp. 70-75, doi: 10.1145/2617995.2618008.

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Title Teaching a digital performing agent: artificial neural network and hidden Markov Model for recognising and performing dance movement
Author(s) McCormick,JORCID iD for McCormick,J orcid.org/0000-0002-5347-0378
Vincs,K
Nahavandi,S
Creighton,D
Hutchison,S
Conference name Movement and Computing. Conference (1st : 2014 : Paris, France)
Conference location Paris, France
Conference dates 2014/6/16 - 2014/6/17
Title of proceedings ACM International Conference Proceeding Series
Editor(s) [Unknown]
Publication date 2014
Series ACM International Conference Proceeding Series
Conference series Movement and Computing Conference
Start page 70
End page 75
Total pages 6
Publisher Association for Computing Machinery
Place of publication New York, N.Y.
Keyword(s) Dance technology
Electronic art
Interactive installation
Machine learning for movement
Movement expression in artificial agents
Movement generation
Performing software agent
Summary For a Digital Performing Agent to be able to perform live with a human dancer, it would be useful for the agent to be able to contextualize the movement the dancer is performing and to have a suitable movement vocabulary with which to contribute to the performance. In this paper we will discuss our research into the use of Artificial Neural Networks (ANN) as a means of allowing a software agent to learn a shared vocabulary of movement from a dancer. The agent is able to use the learnt movements to form an internal representation of what the dancer is performing, allowing it to follow the dancer, generate movement sequences based on the dancer's current movement and dance independently of the dancer using a shared movement vocabulary. By combining the ANN with a Hidden Markov Model (HMM) the agent is able to recognize short full body movement phrases and respond when the dancer performs these phrases. We consider the relationship between the dancer and agent as a means of supporting the agent's learning and performance, rather than developing the agent's capability in a self-contained fashion.
ISBN 9781450328142
Language eng
DOI 10.1145/2617995.2618008
Field of Research 190403 Dance
080101 Adaptive Agents and Intelligent Robotics
Socio Economic Objective 950105 The Performing Arts (incl. Theatre and Dance)
HERDC Research category E1 Full written paper - refereed
Grant ID DP120101695
Copyright notice ©2014, ACM
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072958

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