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

Frontiers in Bioengineering and Biotechnology

Tara Baldacchino¹  William R. Jacobs¹  Sean R. Anderson¹  Keith Worden²  Jennifer Rowson³ 

[1] Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom;Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, United Kingdom;Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, United Kingdom;

关键词: sEMG signals;    finger force regression;    finger movement classification;    variational Bayes;    multivariate mixture of experts;    prosthetic hand;   

DOI  :  10.3389/fbioe.2018.00013

来源: DOAJ

【 摘 要 】

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 modeling force regression at the fingertips, while also performing finger movement classification as a by-product of the modeling 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 recordings 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.

【 授权许可】

Unknown