Abstract
Background: 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. Methods: The electromyography (EMG) recording obtained from 5 healthy subjects that electrodes positioned on the tibialis-anterior (TA) and rectus femoris muscles on every 2 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: Some quantitative measures used to evaluate the results. 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 TA 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 in comparison with the similarity observed between activation pattern of Tibialis- Anterior and the pattern generated by the model. In other words, based on the recorded human data, the activation pattern of the Rectus- Femoris is more similar to a rhythmic pattern.
Highlights
The electromyography (EMG) signal measures the elicited electrical currents generated during the muscle contraction representing the neuromuscular activities
Functional electrical stimulation is one of the well-known solutions applied for gait correction in patients with drop-foot
One of the main restrictions of functional electrical stimulation (FES), which limit the prevalence of FES clinical applications, is the online optimization of muscle stimulation profile
Summary
The electromyography (EMG) signal measures the elicited electrical currents generated during the muscle contraction representing the neuromuscular activities. 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. The goal of the presentation of this neural model is to identify the desired muscle activation patterns by Hopf chaotic oscillator during walking. 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 TA muscle as well as rectus femoris muscle.
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