@inproceedings{f1f120fc203d4328be8f5ed549d83954,
title = "Fuzzy entropy based nonnegative matrix factorization for muscle synergy extraction",
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.",
keywords = "EMG, Fuzzy Entropy, Matrix Factorization, Muscle Synergies, NMF",
author = "Beth Jelfs and Ling Li and Chung Tin and CHAN, {Rosa H. M.}",
year = "2016",
month = mar,
doi = "10.1109/ICASSP.2016.7471773",
language = "English",
series = "International Conference on Acoustics Speech and Signal Processing ICASSP",
publisher = "IEEE",
pages = "739--743",
booktitle = "Proceedings of 2016 IEEE International Conference on Acoustics, Speech, and Signal Processing",
address = "United States",
note = "41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 ; Conference date: 20-03-2016 Through 25-03-2016",
}