Generative Adversarial Networks for Generation and Classification of Physical Rehabilitation Movement Episodes.
Generative Adversarial Networks for Generation and Classification of Physical Rehabilitation Movement Episodes.
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DOI：
10.18178/ijmlc.2018.8.5.724
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发表时间：
2018-10-01
期刊：
影响因子：
--
通讯作者：
Vakanski, Aleksandar
中科院分区：
文献类型：
--
作者：
Li, Longze;Vakanski, Aleksandar
本文提出了一种使用人工神经网络对物理治疗过程中与患者运动相关的人体运动进行数学建模的方法。采用生成对抗网络结构，以对抗方式同时训练判别模型和生成模型。研究了不同的网络架构，将判别模型和生成模型构造为由卷积或循环计算单元组成的隐藏层的深层子网络。这些模型在光学运动跟踪器记录的人体运动数据集上进行了验证。结果证明了网络对新运动实例进行分类以及生成类似于记录的运动序列的运动示例的能力。
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.