A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model - Mechtenberg, Malte; Schneider, Axel (Frontiers in Neurorobotics, 2023)
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).