Mixed matrix factorization: a novel algorithm for the extraction of kinematic-muscular synergies-Reference-Cited by-同舟云学术

Mixed matrix factorization: a novel algorithm for the extraction of kinematic-muscular synergies

Published:2022-02-01 Issue:2 Volume:127 Page:529-547
ISSN:0022-3077
Container-title:Journal of Neurophysiology
language:en
Short-container-title:Journal of Neurophysiology

Author:

Scano Alessandro¹^ORCID,Mira Robert Mihai¹,d’Avella Andrea²³^ORCID

Affiliation:

1. UOS STIIMA Lecco—Human-Centered, Smart & Safe, Living Environment, Italian National Research Council (CNR), Lecco, Italy

2. Laboratory of Neuromotor Physiology, IRCCS Fondazione Santa Lucia, Rome, Italy

3. Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, Messina, Italy

Abstract

The mixed matrix factorization (MMF) is a novel method for extracting kinematic-muscular synergies. The previous state of the art algorithm (NMFpn) factorizes separately positive and rectified negative waveforms; the MMF instead employs a gradient descent method to factorize from mixed kinematic unconstrained data and muscular non-negative data. MMF achieves perfect reconstruction on noiseless data, improving the NMFpn. MMF shows promising applicability on real data.

Publisher

American Physiological Society

Subject

Physiology,General Neuroscience

Link

https://journals.physiology.org/doi/pdf/10.1152/jn.00379.2021

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