Abstract
This paper describes a novel algorithm for autonomous and incremental learning of motion pattern primitives by observation of human motion. Human motion patterns are abstracted into a Hidden Markov Model representation, which can be used for both subsequent motion recognition and generation, analogous to the mirror neuron hypothesis in primates. As new motion patterns are observed, they are incrementally grouped together using hierarchical agglomerative clustering based on their relative distance in the HMM space. The clustering algorithm forms a tree structure, with specialized motions at the tree leaves, and generalized motions closer to the root. The generated tree structure will depend on the type of training data provided, so that the most specialized motions will be those for which the most training has been received. Tests with motion capture data for a variety of motion primitives demonstrate the efficacy of the algorithm.
Original language | English |
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Title of host publication | 16th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 1016-1021 |
Number of pages | 6 |
ISBN (Print) | 1424416345, 9781424416349 |
DOIs | |
Publication status | Published - 1 Dec 2007 |
Externally published | Yes |
Event | IEEE/RSJ International Symposium on Robot and Human Interactive Communication 2007 - Jeju, Korea, South Duration: 26 Aug 2007 → 29 Aug 2007 Conference number: 16th https://ieeexplore.ieee.org/xpl/conhome/4415041/proceeding (Proceedings) |
Publication series
Name | Proceedings - IEEE International Workshop on Robot and Human Interactive Communication |
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Conference
Conference | IEEE/RSJ International Symposium on Robot and Human Interactive Communication 2007 |
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Abbreviated title | RO-MAN 2007 |
Country/Territory | Korea, South |
City | Jeju |
Period | 26/08/07 → 29/08/07 |
Internet address |