Candidates for Synergies: Linear Discriminants versus Principal Components

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...

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Bibliographic Details
Published in:Computational Intelligence and Neuroscience 2014-01, Vol.2014 (2014), p.208-217
Main Authors: Vinjamuri, Ramana, Patel, Vrajeshri, Powell, Michael, Mao, Zhi-Hong, Crone, Nathan
Format: Article
Language:English
Subjects:
Biomechanical Phenomena
Biomedical engineering
Cybernetics - instrumentation
Cybernetics - methods
Discriminant Analysis
Fingers & toes
Hand
Hand Strength
Human mechanics
Humans
Identification and classification
Kinematics
Methods
Movement - physiology
Nonlinear Dynamics
Physiological aspects
Posture
Principal Component Analysis
Principal components analysis
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Summary: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.
ISSN:1687-5265
1687-5273
DOI:10.1155/2014/373957