A New Algorithm for Innervation Zone Estimation Using Surface Electromyography: A Simulation Study Based on a Simulator for Continuous sEMGs

In this work, a novel algorithm for the estimation of the innervation zone location within a muscle head is presented. The algorithm is able to identify innervation zone clusters within continuous surface electromyography (sEMG) recordings based on linear electrode arrays. The presented algorithm is tested in a simulation environment, which is capable of simulating EMG signals based on a common drive signal (activation). The simulator was used to generate sEMGs of six virtual muscle based on six different configurations for the respective muscle fibre distributions. The virtual muscles were each activated with a trapezoidal signal (common drive). The new algorithm was able to identify the location of the innervation zone centers with a mean absolute error of 3.8% of the inter electrode distance. In the best case, the absolute error was below 1% of the inter electrode distance.