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Front. Bioeng. Biotechnol. | doi: 10.3389/fbioe.2018.00013

Simultaneous Force Regression and Movement Classification of Fingers via Surface EMG within a Unified Bayesian Framework

  • 1Department of Automatic Control and Systems Engineering, University of Sheffield, United Kingdom
  • 2Department of Mechanical Engineering, University of Sheffield, United Kingdom
  • 3Insigneo Institute for in silico Medicine, University of Sheffield, United Kingdom

This contribution presents a novel methodology for myolectric based control using surface electromyographic (sEMG) signals recorded during finger movements. A multivariate Bayesian mixture of experts (MoE) model is introduced which provides a powerful method for modelling force regression at the fingertips, whilst also performing finger movement classification as a by-product of the modelling algorithm. Bayesian inference of the model allows uncertainties to be naturally incorporated into the model structure. This method is tested using data from the publicly released NinaPro database which consists of sEMG recording for 6 degree-of-freedom force activations for 40 intact subjects. The results demonstrate that the MoE model achieves similar performance compared to the benchmark set by the authors of NinaPro for finger force regression. Additionally, inherent to the Bayesian framework is the inclusion of uncertainty in the model parameters, naturally providing confidence bounds on the force regression predictions. Furthermore, the integrated clustering step allows a detailed investigation into classification of the finger movements, without incurring any extra computational effort. Subsequently, a systematic approach to assessing the importance of the number of electrodes needed for accurate control is performed via sensitivity analysis techniques. A slight degradation in regression performance is observed for a reduced number of electrodes, while classification performance is unaffected.

Keywords: sEMG signals, finger force regression, Finger Movement Classification, variational Bayes, multivariate mixture of experts, Prosthetic hand

Received: 07 Aug 2017; Accepted: 23 Jan 2018.

Edited by:

Marie-Christine HO BA THO, University of Technology of Compiègne, France

Reviewed by:

Jason Luck, Duke University, United States
Ahmed Samet, Institut National des Sciences Appliquées de Strasbourg, France  

Copyright: © 2018 Baldacchino, Jacobs, Anderson, Worden and Rowson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Tara Baldacchino, University of Sheffield, Department of Automatic Control and Systems Engineering, Mappin Street, Sheffield, S1 3JD, United Kingdom, t.baldacchino@sheffield.ac.uk