Higher order tensor decomposition for proportional myoelectric control based on muscle synergies

Ahmed Ebied, Eli Kinney-Lang, Javier Escudero

Research output: Contribution to journalArticlepeer-review


Muscle synergies have recently been utilised in myoelectric control systems. Thus far, all proposed synergy-based systems rely on matrix factorisation methods. However, this is limited in terms of task5 dimensionality. Here, the potential application of higher order tensor decomposition as a framework for proportional myoelectric control is demonstrated. A novel constrained Tucker decomposition (consTD) technique of syn9 ergy extraction is proposed for synergy-based myoelectric control model and compared with state-of-the-art matrix factorisation models. The extracted synergies were used to estimate control signals for the wrist’s Degree of Freedom (DoF) through direct projection. The consTD model was able to estimate the control signals for each DoF by utilising all data in one 3rd-order tensor. This is contrast with matrix factorisation models where data are segmented for each DoF and then the synergies often have to be realigned. Moreover, the consTD method offers more information by providing additional shared synergies, unlike matrix factori20 sation methods. The extracted control signals were fed to a ridge regression to estimate the wrist’s kinematics based on real glove data. The Coefficient of Determination (R2) for the reconstructed wrist position showed that the proposed consTD was higher than matrix factorisation methods. In sum, this study provides the first proof of concept for the use of higher-order tensor decomposition in proportional myoelectric control and it highlights the potential of tensors to provide an objective and direct approach to identify synergies.
Original languageEnglish
Article number102523
Number of pages13
JournalBiomedical Signal Processing and Control
Early online date4 Mar 2021
Publication statusPublished - May 2021


  • myoelectric control
  • Muscle synergy
  • Matrix factorisation
  • Sparse non-negative matrix factorisation
  • Tucker decomposition
  • Tensor decomposition


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