Linear and Non-linear Dimensionality-Reduction Techniques on Full Hand Kinematics - Portnova-Fahreeva, Alexandra A.; Rizzoglio, Fabio; Nisky, Ilana; Casadio, Maura; Mussa-Ivaldi, Ferdinando A.; Rombokas, Eric (Frontiers in Bioengineering and Biotechnology, 2020)
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 nonlinear 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 nonlinear dimensionality reduction may offer a more effective platform than linear methods to control prosthetic hands.