On the use of higher-order tensors to model muscle synergies

TL;DR

This paper introduces a higher-order tensor model for muscle synergies that incorporates spectral and repetition information, demonstrating its potential to enhance understanding of motor control over traditional matrix factorisation methods.

Contribution

The paper presents a novel 4th-order tensor muscle synergy model using Tucker3 decomposition, extending current models by including spectral and movement repetition data.

Findings

Tensor model captures richer muscle synergy patterns.

Tucker3 decomposition reveals movement-specific synergy differences.

Potential for improved motor control applications.

Abstract

The muscle synergy concept provides the best framework to understand motor control and it has been recently utilised in many applications such as prosthesis control. The current muscle synergy model relies on decomposing multi-channel surface Electromyography (EMG) signals into a synergy matrix (spatial mode) and its weighting function (temporal mode). This is done using several matrix factorisation techniques, with Non-negative matrix factorisation (NMF) being the most prominent method. Here, we introduce a 4th-order tensor muscle synergy model that extends the current state of the art by taking spectral information and repetitions (movements) into account. This adds more depth to the model and provides more synergistic information. In particular, we illustrate a proof-of-concept study where the Tucker3 tensor decomposition model was applied to a subset of wrist movements from the Ninapro database. The results showed the potential of Tucker3 tensor factorisation in finding patterns of muscle synergies with information about the movements and highlights the differences between the current and proposed model.