Kohki Nakane, Rentaro Ono, Shota Yamamoto, M. Takada, Fumiya Kinoshita, A. Sugiura, Y. Matsuura, Kazuhiro Fujikake, H. Takada
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引用次数: 1
Abstract
We have applied this artificial intelligence (AI) system to numerical simulations of the stabilogram whose randomness is remarkably greater than that of the other bio-signal in accordance with the nonlinear analysis. We have succeeded in findings of the mathematical models of the body sway in the elderly with use of the Generative Adversarial Networks (GANs). Trying to visualize internal state of the discriminator layer in our GAN, we can discuss how the AI captures the feature of patterns in the stabilograms recorded during the 3D sickness. Especially in the stabilograms measured during the 3D sickness, cusp patterns could be extracted as a high contribution to the output of the discriminator.
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