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

• Published in: Biomedical signal processing and control 2021-05, Vol.67, p.102523, Article 102523
• Main Authors: Ebied, Ahmed, Kinney-Lang, Eli, Escudero, Javier
• Format: Article
• Language: English
• Subjects: Matrix factorisation; Muscle synergy; Myoelectric control; Sparse non-negative matrix factorisation; Tensor decomposition; Tucker decomposition
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2021.102523

•Higher-order tensor decomposition can be utilised in synergy-based myoelectric control.•The novel consTD method is more effective than matrix factorisation in synergy extraction from EMG repetitive training dataset.•ConsTD method is comparable with NMF and SNMF in synergy-based myoelectric control. 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 task-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 synergy 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 factorisation 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.