Paper Info
Title
Consistency of Muscle Synergies Extracted via Higher-Order Tensor Decomposition Towards Myoelectric Control
Abstract
In recent years, muscle synergies have been proposed for proportional myoelectric control. Synergies were extracted using matrix factorisation techniques (mainly non-negative matrix factorisation, NMF), which requires identification of synergies to tasks or movements. In addition, NMF methods were viable only with a task dimension of 2 degrees of freedoms (DoFs). Here, the potential use of a higher-order tensor model for myoelectric control is explored. We assess the ability of a constrained Tucker tensor decomposition (consTD) method to estimate consistent synergies when the task dimensionality is increased up to 3-DoFs. Synergies extracted from 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> -order tensor of 1 and 3 DoFs were compared. Results showed that muscle synergies extracted via consTD were consistent with the increase of task-dimension. Hence, these results support the consideration of proportional 3-DoF myoelectric control based on tensor decompositions.
Year
DOI
Venue
2019
10.1109/NER.2019.8717076
2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)
Keywords
Field
DocType
muscle synergies extracted,higher-order tensor decomposition,proportional myoelectric control,matrix factorisation techniques,nonnegative matrix factorisation,NMF methods,task dimension,higher-order tensor model,constrained Tucker tensor decomposition method,consistent synergies,task dimensionality,rd -order tensor,task-dimension,3-DoF myoelectric control,tensor decompositions
Computer vision,Computer science,Higher order tensor,Artificial intelligence
Conference
ISSN
ISBN
Citations 
1948-3546
978-1-5386-7922-7
0
PageRank 
References 
Authors
0.34
4
3
Name
Order
Citations
PageRank
Ahmed Ebied111.71
Eli Kinney-Lang212.05
Escudero Javier317427.45