Identifying the Dynamics of Leg Muscle Activation During Human Gait Using Neural Oscillator and Fuzzy Compensator - Ravari, Reihaneh; Kobravi, Hamid Reza (International Clinical Neuroscience Journal, 2018)
Introduction: The goal of this study is to design a model in order to predict the muscle activation pattern because the muscle activation patterns contain valuable information about the muscle dynamics and movement patterns. Therefore, the goal of the presentation of this neural model is to identify the desired muscle activation patterns by Hopf chaotic oscillator during walking. Since the knee muscles activation has a significant effect on the movement pattern during walking, the main concentration of this study is to identify the knee muscles activation dynamics using a modeling technique. Material and Method: The EMG recording obtained from 5 healthy subjects that electrodes positioned on the Tibialis- Anterior and Rectus- Femoris muscles on every two feet. In the proposed model, along with the chaotic oscillator, a fuzzy compensator was designed to face the unmolded dynamics. In fact, on the condition, the observed difference between the desired and actual activation patterns violate some specific quantitative ranges, the fuzzy compensator based on predefined rules modify the activity pattern produced by the Hopf oscillator. Results: To evaluate the results, some quantitative measures used. According to the achieved results, the proposed model could generate the trajectories, dynamics of which are similar to the muscle activation dynamics of the studied muscles. In this model, the generated activity pattern by the proposed model cannot follow the desired activity of the Tibialis- Anterior muscle as well as Rectus- Femoris muscle. Conclusion: The similarity between the generated activity pattern by the model and the activation dynamics of Rectus- Femoris muscle was more considerable. In other words, based on the recorded human data, the activation pattern of the Rectus- Femoris is more similar to a rhythmic pattern.