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A new method for muscle fatigue assessment: Online model identification techniques
Authors:Maria Papaiordanidou PhD  Mitsuhiro Hayashibe PhD  Alain Varray PhD  Charles Fattal MD  PhD  David Guiraud PhD
Affiliation:1. UMR7287, CNRS, Aix‐Marseille University, , 13288 Marseille, France;2. Movement to Health, University Montpellier 1, , Montpellier, France;3. DEMAR team, , Montpellier, France;4. CMN Propara, , Montpellier, France
Abstract:Introduction: The purpose of this study was to propose a method that allows extraction of the current muscle state under electrically induced fatigue. Methods: The triceps surae muscle of 5 subjects paralyzed by spinal cord injury was fatigued by intermittent electrical stimulation (5 × 5 trains at 30 Hz ). Classical fatigue indices representing muscle contractile properties [peak twitch (Pt) and half‐relaxation time (HRT)] were assessed before and after each 5‐train series and were used to identify 2 relevant parameters (Fm, Ur) of a previously developed mathematical model using the Sigma‐Point Kalman Filter. Results: Pt declined significantly during the protocol, whereas HRT remained unchanged. Identification of the model parameters with experimental data yielded a model‐based fatigue assessment that gave a more stable evaluation of fatigue than classical parameters. Conclusions: This work reinforces clinical research by providing a tool that clinicians can use to monitor fatigue development during stimulation. Muscle Nerve 50: 556–563, 2014
Keywords:contractile apparatus  identification method  muscle model  paraplegia  Sigma‐Point Kalman Filter
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