Curve Fitting Based Minimum Norm Estimation (CFB-MNE) for motor unit spatial localization using high density surface electromyogram signals

Abstract

The purpose of the study is the real time spatial localization of single motor units using simulated Motor Unit Action Potential dictionary from a cylindrical muscle volume conductor model by means of a state-of-the-art curve fitting method. This Curve Fitting Based Minimum Norm Estimation (CFB-MNE) was made possible by using minimum norm estimation methods to solve an underdetermined inverse problem knowing produced HD-sEMG signals. Specific data extracted from the inverse problem of a pre-determined number of simulated motor units were used to create a 3D curve, which can be used as a fitting curve to anticipate the unknown location of motor units. Results show that the proposed algorithms succeeded in accurately localizing motor units with varying noise levels in a fast duration (less than 100 ms). From all the simulations, a mean root mean square error of maximum 1 mm was recorded for the localization of the depth (less than 1 mm) and a negligible error for the angular position (less than 1°). The proposed work is an innovative algorithm that aids in non-invasively localizing motor units within a muscle in real time. Further efforts are planned to manage MUAP superposition and type. Final applications are related to prosthetic control, rehabilitation guidance or aging monitoring.