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Séparation des signaux de deux extenseurs des doigts à partir d'électromyogrammes de surface haute densité et modélisation biomécanique du mécanisme extenseur

 

Directeur de thèse :     Franck QUAINE

Co-directeur de thèse :     Christine SERVIERE

École doctorale : Ingénierie pour la santé, la cognition et l''environnement (edisce)

Spécialité :

Structure de rattachement : UJF

Établissement d'origine : Autre

Financement(s) : Contrat doctoral ; contrat à durée déterminée ; ATER

 

Date d'entrée en thèse : 01/10/2014

Date de soutenance : 25/06/2018

 

Composition du jury :
M. Philippe RAVIER MCU, HDR, Université d''Orléans, Rapporteur
M. Laurent VIGOUROUX MCU, HDR, Université Aix-Marseille, Rapporteur
M. Franck QUAINE MCU, HDR, Université de Grenoble Alpes, Directeur de thèse
Mme. Christine SERVIERE Chargée de recherche, CNRS, Codirecteur de thèse
M. Christian JUTTEN Professeur, Université de Grenoble Alpes, Examinateur, Président du jury
M. Roberto MERLETTI Professeur, École polytechnique de Turin, Examinateur
M. Pierre PORTERO Professeur, Université Paris-Est Créteil, Examinateur
M. Francisco VALERO-CUEVAS Professeur, Université de Californie du Sud, Examinateur
M. François MOUTET Professeur, Université de Grenoble Alpes, CHUG, Invité

 

Résumé : The surface electromyographic signals (SEMG) are the electric signals, composed of electric potentials. These potentials are produced by the recruited motor units of an active muscle and captured by the surface electrodes. The SEMG signals are widely used in medicine, prosthesis control, and biomechanical studies as an indicator of muscle activity.
However, SEMG measurements are usually subjects of crosstalk or interference from nearby muscles. It appears when two or more muscles situated close to each other are active during a SEMG recording. An example of such muscles are the extensors of index and little finger, extensor indicis and extensor digiti minimi, situated close to each other and creating a significant amount of mutual crosstalk when simultaneously active. The crosstalk causes precision decrease of SEMG-based estimation of muscle activations. Hence, the crosstalk-reducing problem must be preliminary solved before muscle activation evaluation.
Once the activations of individual muscles are estimated from the mixture, they may be used as an input of a finger biomechanical model to calculate a fingertip force. These models usually contain an extensor mechanism of the finger, which is a structure, transmitting the force from the extensor muscles to the finger joints. This structure is often taken into account as a set of coefficients. However, there is a lack of study about how these coefficients vary with posture, applied force, and subject variability.
The purpose of this work is to improve the finger force estimation from the crosstalk-contaminated signals for isometric tasks by extracting the activations of individual muscles and improving the finger biomechanical model.
Firstly, the SEMG signals were recorded with high-density surface electromyographic (HD-EMG) electrode matrix. The extraction of individual muscle activation was based on classifying the detected potentials according their propagation direction and depth of originating motor unit.
Secondly, a precise biomechanical model of the finger extensor mechanism was created, containing the principal tendons and ligaments, which are represented by a set of elastic bands and convex surfaces. The algorithm of the model parametrization was proposed as well. The proposed methods of muscle activation estimation along with the created extensor mechanism model may be used for calculating the fingertip force and internal tissues deformations for normal or pathological fingers. These methods can be applied to biomechanical studies as well as find a use in hand rehabilitation or prosthesis control.


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