Experimental Validation of a New Dynamic Muscle Fatigue Model

Deep Seth (1) , Damien Chablat (1) , Sophie Sakka (1) , Fouad Bennis (1)

1 IRCCyN - Institut de Recherche en Communications et en Cybernétique de Nantes

Muscle fatigue is considered as one of the major risk factor causing Musculo-Skeletal Disorder (MSD). To avoid MSD the study of muscle fatigue is very important. For the study of muscle fatigue a new model is developed by modifying the Ruina Ma's dynamic muscle fatigue model and introducing the muscle co-contraction factor 'n' in this model.The aim of this paper is to experimentally validate a dynamic muscle fatigue model using Electromyography (EMG) and Maximum Voluntary Contraction (MVC) data. The data of ten subjects are used to analyze the muscle activities and muscle fatigue during the extension-flexion (push-pull) motion of the arm on a constant absolute value of the external load. The findings for co-contraction factor shows that the fatigue increases when co-contraction area decreases. The dynamic muscle fatigue model is validated using the MVC data, fatigue rate and co-contraction factor of the subjects.

Mots clés: Muscle fatigue; maximum voluntary contraction (MVC); muscle fatigue model; co-contraction; fatigue rate; electromyography (EMG)

Domaines: Robotique [cs.RO]

Deep Seth, Damien Chablat, Sophie Sakka, Fouad Bennis. Experimental Validation of a New Dynamic Muscle Fatigue Model. 18th International Conference on Human-Computer Interaction, Jul 2016, Toronto, Canada. ⟨hal-01309155⟩