Validation of a New Dynamic Muscle Fatigue Model and DMET Analysis

• Published in: The international journal of virtual reality Vol. 16; no. 1; pp. 22 - 32
• Main Authors: Seth, Deep, Chablat, Damien, Bennis, Fouad, Sakka, Sophie, Jubeau, Marc, Nordez, Antoine
• Format: Journal Article
• Language: English
• Published: 01.01.2016
• ISSN: 1081-1451
• DOI: 10.20870/IJVR.2016.16.1.2879

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.