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

conference contribution
posted on 2014-01-01, 00:00 authored by John Mccormick, Kim Vincs, Saeid Nahavandi, Douglas CreightonDouglas Creighton, Steph Hutchison
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.



Movement and Computing. Conference (1st : 2014 : Paris, France)


ACM International Conference Proceeding Series


70 - 75


Association for Computing Machinery


Paris, France

Place of publication

New York, N.Y.

Start date


End date






Publication classification

E1 Full written paper - refereed; E Conference publication

Copyright notice

2014, ACM



Title of proceedings

ACM International Conference Proceeding Series