  We propose a novel way of solving the issue of classification of
out-of-vocabulary gestures using Artificial Neural Networks (ANNs) trained in
the Generative Adversarial Network (GAN) framework. A generative model augments
the data set in an online fashion with new samples and stochastic target
vectors, while a discriminative model determines the class of the samples. The
approach was evaluated on the UC2017 SG and UC2018 DualMyo data sets. The
generative models performance was measured with a distance metric between
generated and real samples. The discriminative models were evaluated by their
accuracy on trained and novel classes. In terms of sample generation quality,
the GAN is significantly better than a random distribution (noise) in mean
distance, for all classes. In the classification tests, the baseline neural
network was not capable of identifying untrained gestures. When the proposed
methodology was implemented, we found that there is a trade-off between the
detection of trained and untrained gestures, with some trained samples being
mistaken as novelty. Nevertheless, a novelty detection accuracy of 95.4% or
90.2% (depending on the data set) was achieved with just 5% loss of accuracy on
trained classes.
