Frontiers in Bioengineering and Biotechnology (May 2020)

Linear and Non-linear Dimensionality-Reduction Techniques on Full Hand Kinematics

  • Alexandra A. Portnova-Fahreeva,
  • Alexandra A. Portnova-Fahreeva,
  • Fabio Rizzoglio,
  • Fabio Rizzoglio,
  • Fabio Rizzoglio,
  • Ilana Nisky,
  • Maura Casadio,
  • Maura Casadio,
  • Ferdinando A. Mussa-Ivaldi,
  • Ferdinando A. Mussa-Ivaldi,
  • Ferdinando A. Mussa-Ivaldi,
  • Eric Rombokas,
  • Eric Rombokas

DOI
https://doi.org/10.3389/fbioe.2020.00429
Journal volume & issue
Vol. 8

Abstract

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The purpose of this study was to find a parsimonious representation of hand kinematics data that could facilitate prosthetic hand control. Principal Component Analysis (PCA) and a non-linear Autoencoder Network (nAEN) were compared in their effectiveness at capturing the essential characteristics of a wide spectrum of hand gestures and actions. Performance of the two methods was compared on (a) the ability to accurately reconstruct hand kinematic data from a latent manifold of reduced dimension, (b) variance distribution across latent dimensions, and (c) the separability of hand movements in compressed and reconstructed representations derived using a linear classifier. The nAEN exhibited higher performance than PCA in its ability to more accurately reconstruct hand kinematic data from a latent manifold of reduced dimension. Whereas, for two dimensions in the latent manifold, PCA was able to account for 78% of input data variance, nAEN accounted for 94%. In addition, the nAEN latent manifold was spanned by coordinates with more uniform share of signal variance compared to PCA. Lastly, the nAEN was able to produce a manifold of more separable movements than PCA, as different tasks, when reconstructed, were more distinguishable by a linear classifier, SoftMax regression. It is concluded that non-linear dimensionality reduction may offer a more effective platform than linear methods to control prosthetic hands.

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