AUTHOR=Mechtenberg Malte , Schneider Axel 

TITLE=A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model

JOURNAL=Frontiers in Neurorobotics

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2023.1179224

DOI=10.3389/fnbot.2023.1179224

ISSN=1662-5218

ABSTRACT=Motion predictions for limbs can be performed using so called Hill-based muscle models. For
this type of models, a surface electromyogram (sEMG) of the muscle serves as an input signal for
the activation of the muscle model. However, the Hill model needs additional information about
the mechanical system state of the muscle (current length, velocity, etc.) for a reliable prediction
of the muscle force generation and, hence, the prediction of the joint motion. One feature that
contains potential information about the state of the muscle is the position of the center of the
innervation zone. This feature can be additionally extracted from the surface EMG. To find the
center, a wavelet-based algorithm is proposed that localizes motor unit potentials in the individual
channels of a single-column sEMG array and then identifies innervation point candidates. In a
final step, these innervation point candidates are clustered in a density-based manner. The center
of the largest cluster is the estimated innervation zone center. The density of the center can be
used to assess the accuracy of the estimate. The algorithm has been tested in a simulation. For
this purpose, an sEMG simulator was developed and implemented that is able to compute large
motor units (thousands of muscle fibers) quickly (within seconds on a standard PC).