  Automatic recognition of an online series of unsegmented actions requires a
method for segmentation that determines when an action starts and when it ends.
In this paper, a novel approach for recognizing unsegmented actions in online
test experiments is proposed. The method uses self-organizing neural networks
to build a three-layer cognitive architecture. The unique features of an action
sequence are represented as a series of elicited key activations by the
first-layer self-organizing map. An average length of a key activation vector
is calculated for all action sequences in a training set and adjusted in
learning trials to generate input patterns to the second-layer self-organizing
map. The pattern vectors are clustered in the second layer, and the clusters
are then labeled by an action identity in the third layer neural network. The
experiment results show that although the performance drops slightly in online
experiments compared to the offline tests, the ability of the proposed
architecture to deal with the unsegmented action sequences as well as the
online performance makes the system more plausible and practical in real-case
scenarios.
