Consistency of Muscle Synergies Extracted via Higher-Order Tensor Decomposition Towards Myoelectric Control

TL;DR

This paper investigates the use of higher-order tensor decomposition, specifically constrained Tucker decomposition, to extract consistent muscle synergies for proportional myoelectric control involving up to 3 degrees of freedom, overcoming limitations of traditional matrix factorization methods.

Contribution

It introduces a tensor-based approach for extracting muscle synergies that remain consistent across increased task dimensionality, extending beyond the 2-DoF limitation of NMF methods.

Findings

Tensor decomposition yields consistent synergies with increased task dimension

Synergies from 3-DoF tasks are comparable to those from 1-DoF tasks

Supports the feasibility of 3-DoF myoelectric control using tensor methods

Abstract

In recent years, muscle synergies have been pro-posed 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 to estimate consistent synergies when the task dimensionality is increased up to 3-DoFs. Synergies extracted from 3rd-order tensor of 1 and 3 DoFs were compared. Results showed that muscle synergies extracted via constrained Tucker decomposition were consistent with the increase of task-dimension. Hence, these results support the consideration of proportional 3-DoF myoelectric control based on tensor decompositions.