Linear and Nonlinear Kinematic Synergies in the Grasping Hand

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dc.contributor.authorPatel, Vrajeshri
dc.contributor.authorBurns, Martin
dc.contributor.authorMao, Zhi-Hong
dc.contributor.authorCrone, Nathan E
dc.contributor.authorVinjamuri, Ramana
dc.date.accessioned2021-05-17T18:06:25Z
dc.date.available2021-05-17T18:06:25Z
dc.date.issued2015-08-30
dc.description.abstractKinematic synergies in human hand movements have shown promising applications in dexterous control of robotic and prosthetic hands. We and others have previously derived kinematic synergies from human hand grasping movements using a widely used linear dimensionality reduction method, Principal Component Analysis (PCA). As the human biomechanical system is inherently nonlinear, using nonlinear dimensionality reduction methods to derive kinematic synergies might be expected to improve the representation of human hand movements in reduced dimensions. In this paper, we derived linear and nonlinear kinematic synergies from linear (PCA), globally nonlinear (Isomap, Stochastic Proximity Embedding (SPE), Sammon Mapping (SaM), and Stochastic Neighbor Embedding (SNE)) and locally nonlinear (Local Linear Embedding (LLE), LaplacianEigenmaps (LaE), and Local Tangent Space Alignment (LTSA)) dimensionality reduction methods. Synergies derived from linear PCA and nonlinear SaMwere able to capture multiple functional postures and physiological patterns. Results from natural hand grasping movements indicated that PCA performed better than all nonlinear dimensionality reduction methods used in the paper. Results from ASL postural movements indicated that PCA, SaM, and SPE better generalized over ASL postural movements when compared to other methods. Overall, our results show that PCA derived synergies offer qualitative and quantitative advantages over nonlinear methods as a limited number of kinematic synergies begin to be implemented in human prosthetics.en_US
dc.description.sponsorshipThis work was supported by the National Institute on Disability and Rehabilitation Research (NIDRR) under Grant H133F100001 and National Science Foundation (NSF) under Grant CMMI-0727256.en_US
dc.description.urihttps://www.hilarispublisher.com/open-access/linear-and-nonlinear-kinematic-synergies-in-the-grasping-hand-2155-9538-1000163.pdfen_US
dc.format.extent8 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2ilth-ptbj
dc.identifier.citationPatel V, Burns M, Mao ZH, Crone NE, Vinjamuri R (2015) Linear and Nonlinear Kinematic Synergies in the Grasping Hand. J Bioengineer & Biomedical Sci 5: 163. doi:10.4172/2155- 9538.1000163en_US
dc.identifier.urihttps://doi.org/10.4172/2155- 9538.1000163
dc.identifier.urihttp://hdl.handle.net/11603/21558
dc.language.isoen_USen_US
dc.publisherHilarisen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.titleLinear and Nonlinear Kinematic Synergies in the Grasping Handen_US
dc.typeTexten_US

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