Candidates for synergies: linear discriminants versus principal components.

Ramana Vinjamuri,Vrajeshri Patel,Nathan Crone,Michael Powell,Zhi-Hong Mao

doi:10.1155/2014/373957

Abstract

Movement primitives or synergies have been extracted from human hand movements using several matrix factorization, dimensionality reduction, and classification methods. Principal component analysis (PCA) is widely used to obtain the first few significant eigenvectors of covariance that explain most of the variance of the data. Linear discriminant analysis (LDA) is also used as a supervised learning method to classify the hand postures corresponding to the objects grasped. Synergies obtained using PCA are principal component vectors aligned with dominant variances. On the other hand, synergies obtained using LDA are linear discriminant vectors that separate the groups of variances. In this paper, time varying kinematic synergies in the human hand grasping movements were extracted using these two diametrically opposite methods and were evaluated in reconstructing natural and American sign language (ASL) postural movements. We used an unsupervised LDA (ULDA) to extract linear discriminants. The results suggest that PCA outperformed LDA. The uniqueness, advantages, and disadvantages of each of these methods in representing high-dimensional hand movements in reduced dimensions were discussed.

Highlights

The central nervous system (CNS) is responsible for generating a potentially infinite set of hand postures used in everyday tasks such as grasping objects
The top six significant synergies derived from Principal component analysis (PCA) and unsupervised LDA (ULDA) were used in reconstruction of natural movements and American sign language (ASL) postural movements
Synergies derived from ULDA show an initial movement delay which is consistent in proximal interphalangeal (PIP) joints for synergies 1–5

Summary

Introduction

The central nervous system (CNS) is responsible for generating a potentially infinite set of hand postures used in everyday tasks such as grasping objects. Ninety percent of natural grasp movements produced by the human hand, which has greater than 25 DoF [4], have been reconstructed using five or six synergies [5, 6] For these reasons, synergies have potential applications in dexterous control of prosthetic hands and have been recently applied to brain machine interfaces used in neural prosthesis [3, 7, 8]. Synergies have potential applications in dexterous control of prosthetic hands and have been recently applied to brain machine interfaces used in neural prosthesis [3, 7, 8] Deriving these synergies to effectively represent and reconstruct human hand movements poses a challenge.

Methods

Results

Conclusion