Title
Moving Past Principal Component Analysis: Nonlinear Dimensionality Reduction Towards Better Hand Pose Synthesis
Abstract
Despite their complex kinematic structure with many degrees of freedom, human hands have been shown to have synergistic behavior, with coordinated joint movements being able to explain a large amount of the variance in hand posture measurements. This phenomenon has traditionally been analyzed through Principal Component Analysis (PCA), and has led to important applications in medical robotics, such as the design and control of upper limb prostheses and measurement of hand posture with a reduced number of sensors. However, the use of more complex, nonlinear dimensionality reduction techniques for hand joint measurements has been under-explored in the literature. In this paper, we aim to fill this gap by comparing Principal Component Analysis, Kernel Principal Component Analysis (KPCA), and autoencoders on the same data set, evaluating the performance in terms of Mean Square Error of reconstructed hand poses with respect to the original data set. Results show a better performance for the nonlinear techniques, lowering Mean Square Error up to 25% for the KPCA and 50% for the autoencoders when compared to PCA. Visualization of the reconstructed poses shows a better ability from the autoencoder to reconstruct hand shapes when compared to the two other methods.
Year
DOI
Venue
2022
10.1109/ISMR48347.2022.9807580
2022 International Symposium on Medical Robotics (ISMR)
Keywords
DocType
ISSN
nonlinear dimensionality reduction towards better hand pose synthesis,synergistic behavior,coordinated joint movements,hand posture measurements,PCA,upper limb prostheses,complex dimensionality reduction techniques,nonlinear dimensionality reduction techniques,hand joint measurements,kernel principal component analysis,autoencoder,mean square error,reconstructed hand poses,nonlinear techniques,hand shapes
Conference
2831-3690
ISBN
Citations 
PageRank 
978-1-6654-6929-6
0
0.34
References 
Authors
20
3
Name
Order
Citations
PageRank
Edoardo Battaglia100.34
Michael Kasman200.34
Ann Majewicz Fey322.09