Abstract—This article proposes a method for mathematical
modeling of human movements related to patient exercise
episodes performed during physical therapy sessions by using
artificial neural networks. The generative adversarial network
structure is adopted, whereby a discriminative and a generative
model are trained concurrently in an adversarial manner.
Different network architectures are examined, with the
discriminative and generative models structured as deep
subnetworks of hidden layers comprised of convolutional or
recurrent computational units. The models are validated on a
data set of human movements recorded with an optical motion
tracker. The results demonstrate an ability of the networks for
classification of new instances of motions, and for generation of
motion examples that resemble the recorded motion sequences.
Index Terms—Generative adversarial networks, physical rehabilitation, artificial neural networks.
L. Li is with the Department of Computer Science, University of Idaho, Idaho Falls, ID 83402, USA (email@example.com).
A. Vakanski is with the Industrial Technology, University of Idaho, Idaho Falls, ID 83402, USA (e-mail: firstname.lastname@example.org).
Cite: Longze Li and Aleksandar Vakanski, "Generative Adversarial Networks for Generation and Classification of Physical Rehabilitation Movement Episodes," International Journal of Machine Learning and Computing vol. 8, no. 5, pp. 428-436, 2018.