Fuzzy entropy based nonnegative matrix factorization for muscle synergy extraction

Jelfs, Beth and Li, Ling and Tin, Chung and Chan, Rosa H.M. (2016) Fuzzy entropy based nonnegative matrix factorization for muscle synergy extraction. In: UNSPECIFIED, 20-25 March 2016, Shanghai. (doi:https://doi.org/10.1109/ICASSP.2016.7471773) (Full text available)

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http://dx.doi.org/10.1109/ICASSP.2016.7471773

Abstract

The concept of muscle synergies has proven to be an effective method for representing patterns of muscle activation. The number of degrees of freedom to be controlled are reduced while also providing a flexible platform for producing detailed movements using synergies as building blocks. It has previously been shown that small components of movement are crucial to producing precise and coordinated movement. Methods which focus on the variance of the data make it possible to overlook these small components in the synergy extraction process. However, algorithms which address the inherent complexity in the neuromuscular system are lacking. To that end we propose a new nonnegative matrix factorization algorithm which employs a cross fuzzy entropy similarity measure, thus, extracting muscle synergies which preserve the complexity of the recorded muscular data. The performance of the proposed algorithm is illustrated on representative EMG data.

Item Type: Conference or workshop item (Other)
Uncontrolled keywords: Matrix Factorization, NMF, Fuzzy Entropy, Muscle Synergies, EMG
Subjects: Q Science
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Caroline Li
Date Deposited: 20 May 2016 11:34 UTC
Last Modified: 06 Oct 2017 15:09 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/55634 (The current URI for this page, for reference purposes)
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