Muscle & nerve · 2014
A new method for muscle fatigue assessment: Online model identification techniques.
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Papaiordanidou Maria, Hayashibe Mitsuhiro, Varray Alain, Fattal Charles, Guiraud David
Аннотация
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.
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.