Automation in industries reduced the human effort, but still there are many manual tasks in industries which lead to musculo-skeletal disorder (MSD). Muscle fatigue is one of the reasons leading to MSD. The objective of this article is to experimentally validate a new dynamic muscle fatigue model taking cocontraction factor into consideration using electromyography (EMG) and Maximum voluntary contraction (MVC) data. A new model (Seth's model) is developed by introducing a co-contraction factor 'n' in R. Ma's dynamic muscle fatigue model. The experimental data of ten subjects are used to analyze the muscle activities and muscle fatigue during extension-flexion 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 index decreases. The dynamic muscle fatigue model is validated using the MVC data, fatigue rate and co-contraction factor of the subjects. It has been found that with the increase in muscle fatigue, co-contraction index decreases and 90% of the subjects followed the exponential function predicted by fatigue model. The model is compared with other models on the basis of dynamic maximum endurance time (DMET). The co-contraction has significant effect on the muscle fatigue model and DMET. With the introduction of co-contraction factor DMET decreases by 25:9% as compare to R. Ma's Model.

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Muscle fatigue is one of the reason leads to Musculo-Skeletal Disorder(MSD). Automation in today's industries makes human effort very less, but still there are many industries in which human have to do complex and repetitive tasks manually. The society/companies have to pay attention on this issue due to the new laws on penibility or repetitive tasks. The objective of this paper is to experimentally validate a new dynamic muscle fatigue model using electromyography (EMG) and Maximum voluntary contraction (MVC). A new model is developed by introducing a co-contraction factor 'n' in the Ruina Ma's dynamic muscle fatigue model. The experimental data of ten subjects are used to analyze the muscle activities and muscle fatigue during extension-flexion 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.

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 cocontraction area decreases. The dynamic muscle fatigue model is validated using the MVC data, fatigue rate and co-contraction factor of the subjects.

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