title: Analysis of the sEMG/force relationship using HD-sEMG technique and data fusion: A simulation study.
abstract: The relationship between the surface Electromyogram (sEMG) signal and the force of an individual muscle is still ambiguous due to the complexity of experimental evaluation. However, understanding this relationship should be useful for the assessment of neuromuscular system in healthy and pathological contexts. In this study, we present a global investigation of the factors governing the shape of this relationship. Accordingly, we conducted a focused sensitivity analysis of the sEMG/force relationship form with respect to neural, functional and physiological parameters variation. For this purpose, we used a fast generation cylindrical model for the simulation of an 8×8 High Density-sEMG (HD-sEMG) grid and a twitch based force model for the muscle force generation. The HD-sEMG signals as well as the corresponding force signals were simulated in isometric non-fatiguing conditions and were based on the Biceps Brachii (BB) muscle properties. A total of 10 isometric constant contractions of 5s were simulated for each configuration of parameters. The Root Mean Squared (RMS) value was computed in order to quantify the sEMG amplitude. Then, an image segmentation method was used for data fusion of the 8×8 RMS maps. In addition, a comparative study between recent modeling propositions and the model proposed in this study is presented. The evaluation was made by computing the Normalized Root Mean Squared Error (NRMSE) of their fitting to the simulated relationship functions. Our results indicated that the relationship between the RMS (mV) and muscle force (N) can be modeled using a 3rd degree polynomial equation. Moreover, it appears that the obtained coefficients are patient-specific and dependent on physiological, anatomical and neural parameters.
authors.affiliation: Sorbonne Universites, Universite de Technologie de Compiegne, UMR CNRS 7338 Biomecanique et Bioingenieurie (BMBI), Centre de recherche Royallieu, CS 60203 Compiegne cedex, France. Electronic address: mariam.al-harrach@utc.fr.
authors.forename: Mariam
authors.initials: M
authors.lastname: Al Harrach
authors.affiliation: Sorbonne Universites, Universite de Technologie de Compiegne, UMR CNRS 7338 Biomecanique et Bioingenieurie (BMBI), Centre de recherche Royallieu, CS 60203 Compiegne cedex, France.
authors.forename: Vincent
authors.initials: V
authors.lastname: Carriou
authors.affiliation: Sorbonne Universites, Universite de Technologie de Compiegne, UMR CNRS 7338 Biomecanique et Bioingenieurie (BMBI), Centre de recherche Royallieu, CS 60203 Compiegne cedex, France.
authors.forename: Sofiane
authors.initials: S
authors.lastname: Boudaoud
authors.affiliation: Sorbonne Universites, Universite de Technologie de Compiegne, UMR CNRS 7338 Biomecanique et Bioingenieurie (BMBI), Centre de recherche Royallieu, CS 60203 Compiegne cedex, France.
authors.forename: Jeremy
authors.initials: J
authors.lastname: Laforet
authors.affiliation: Sorbonne Universites, Universite de Technologie de Compiegne, UMR CNRS 7338 Biomecanique et Bioingenieurie (BMBI), Centre de recherche Royallieu, CS 60203 Compiegne cedex, France.
authors.forename: Frederic
authors.initials: F
authors.lastname: Marin
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004553:Electric Conductivity; D004576:Electromyography / Q000379:methods*; D006801:Humans; D007537:Isometric Contraction / Q000502:physiology*; D008959:Models, Neurological; D053580:Muscle Strength / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D011999:Recruitment, Neurophysiological / Q000502:physiology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013314:Stress, Mechanical
keywords: Data fusion; High Density surface Electromyogram; Image segmentation; Muscle force; Relationship modeling; sEMG/force relationship