Fuzzy Entropy Based Nonnegative Matrix Factorization for Muscle Synergy Extraction

Beth Jelfs1, Chung Tin1, Ling Li2, Rosa H. M. Chan1

  • 1City University of Hong Kong
  • 2University of Kent

Details

Category

Technical Session: Poster

Theme

Bio Imaging and Signal Processing

Sessions

13:30 - 15:30 | Tue 22 Mar | Poster Area J | BISP-P1

Processing of Electro-Physiological Signals

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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.

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