Sparse Estimation for the Assessment of Muscular Activity based on sEMG Measurements

Abstract

Surface Electromyography (sEMG) is an established technology in measuring the electrical activity produced by skeletal muscles. For the purpose of medical diagnosis and automatic control of medical support systems, deriving a measure of muscular activity from sEMG signals is desired. This task is not trivial, since the electrical activity of the individual muscle fibers can only be measured as a superposition, after being filtered differently by the surrounding tissue. This paper presents a novel approach for the estimation of muscular activity based on estimating the composite spike train (CST), which represents the superposition of the individual spike trains of all motor units. To this end, a low-order linear state-space model is estimated from artificial CST input and sEMG output signals, generated by means of a numerical simulation of the underlying physiological processes. This model, obtained using standard system identification methods, is then utilized to estimate the CST input corresponding to real sEMG measurements. For the input estimation, a probabilistic factor graph-based algorithm is employed to perform sparse deconvolution. By enforcing sparsity, the influence of the omnipresent background noise in sEMG measurements on the estimated input is suppressed, and an input signal can be derived, which not only resembles real CST signals but also closely follows the time course of the generated muscle force. An evaluation based on measured sEMG signals of the respiratory muscles shows the practical applicability of the presented approach.