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This article proposes a method for mathematical modeling of human movementsrelated to patient exercise episodes performed during physical therapy sessionsby using artificial neural networks. The generative adversarial networkstructure is adopted, whereby a discriminative and a generative model aretrained concurrently in an adversarial manner. Different network architecturesare examined, with the discriminative and generative models structured as deepsubnetworks of hidden layers comprised of convolutional or recurrentcomputational units. The models are validated on a data set of human movementsrecorded with an optical motion tracker. The results demonstrate an ability ofthe networks for classification of new instances of motions, and for generationof motion examples that resemble the recorded motion sequences.

Generative adversarial networks for generation and classification of physical rehabilitation movement episodes