Investigation of the HD-sEMG probability density function shapes with varying muscle force using data fusion and shape descriptors.

This work presents an evaluation of the High Density surface Electromyogram (HD-sEMG) Probability Density Function (PDF) shape variation according to contraction level. On that account, using PDF shape descriptors: High Order Statistics (HOS) and Shape Distances (SD), we try to address the absence of a consensus for the sEMG non-Gaussianity evolution with force variation. This is motivated by the fact that PDF shape information are relevant in physiological assessment of the muscle architecture and function, such as contraction level classification, in complement to classical amplitude parameters. Accordingly, both experimental and simulation studies are presented in this work. For data fusion, the watershed image processing technique was used. This technique allowed us to find the dominant PDF shape variation profiles from the 64 signals. The experimental protocol consisted of three isometric isotonic contractions of 30, 50 and 70% of the Maximum Voluntary Contraction (MVC). This protocol was performed by six subjects and recorded using an 8 × 8 HD-sEMG grid. For the simulation study, the muscle modeling was done using a fast computing cylindrical HD-sEMG generation model. This model was personalized by morphological parameters obtained by sonography. Moreover, a set of the model parameter configurations were compared as a focused sensitivity analysis of the PDF shape variation. Further, monopolar, bipolar and Laplacian electrode configurations were investigated in both experimental and simulation studies. Results indicated that sEMG PDF shape variations according to force increase are mainly dependent on the Motor Unit (MU) spatial recruitment strategy, the MU type distribution within the muscle, and the used electrode arrangement. Consequently, these statistics can give us an insight into non measurable parameters and specifications of the studied muscle primarily the MU type distribution.