TY - GEN
T1 - Incremental on-line hierarchical clustering of whole body motion patterns
AU - Kulić, Dana
AU - Takano, Wataru
AU - Nakamura, Yoshihiko
PY - 2007/12/1
Y1 - 2007/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=48749097061&partnerID=8YFLogxK
U2 - 10.1109/ROMAN.2007.4415231
DO - 10.1109/ROMAN.2007.4415231
M3 - Conference Paper
AN - SCOPUS:48749097061
SN - 1424416345
SN - 9781424416349
T3 - Proceedings - IEEE International Workshop on Robot and Human Interactive Communication
SP - 1016
EP - 1021
BT - 16th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN
PB - IEEE, Institute of Electrical and Electronics Engineers
T2 - 16th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN
Y2 - 26 August 2007 through 29 August 2007
ER -