Fuzzy entropy based nonnegative matrix factorization for muscle synergy extraction

Beth Jelfs, Ling Li, Chung Tin, Rosa U.M. Chan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherIEEE
Pages739-743
Number of pages5
ISBN (Electronic)9781479999880
DOIs
Publication statusPublished - 18 May 2016
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

  • EMG
  • Fuzzy Entropy
  • Matrix Factorization
  • Muscle Synergies
  • NMF

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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