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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number429
JournalFrontiers in Bioengineering and Biotechnology
Volume8
DOIs
StatePublished - May 5 2020

Keywords

  • dimensionality reduction
  • kinematics
  • neural networks
  • principal component analysis
  • prosthetics
  • unsupervised learning

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Histology
  • Biomedical Engineering

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