Knee Joint Modeling Based on Muscle Interactions Using a Central Pattern Generator to Predict Disease Progression and Rehabilitation Techniques in Incomplete Spinal Cord Injury

Abstract

Purpose: Purpose: Musculoskeletal systems have a complex nature, and it is very difficult to control issues in these systems due to various characteristics such as speed and accuracy. Thus, investigating these musculoskeletal systems requires simple and analyzable methods. Also, due to sudden changes during the movement process, the speed and accuracy of the calculations should be proportional to the operating speed of the system. Predicting the system norms and fulfilling them for the system are the next challenges for relevant studies. Materials and Methods: Accordingly, this study aimed to investigate the knee joint function, the joint condition in an incomplete Spinal Cord Injury (SCI), as well as its rehabilitation conditions by designing a simple mathematical model. This model was designed based on the interactions between Hamstring Muscles (HAM) and the vasti muscle group. Considering changes in the Central Pattern Generator (CPG) as a variable input, we analyzed the model output in fixed point, periodic and chaotic modes. Results: The results of the present study showed that the knee joint model output was a chaotic and fixed point for the healthy and incomplete SCI modes, respectively. Increasing the values of afferents was enhanced in the central pattern generating model to rehabilitate the model. According to the modeling results, by applying coefficients of 1.98, 2.21, and 3.1 to the values of afferents Ia, II and Ib, the incomplete spinal injury model changed permanently from the fixed point to the periodic position, indicating movement with rehabilitation in the knee joint. Conclusion: Based on the results obtained from the knee joint mathematical model in comparison with the reference articles in relation to the expected results, it can be stated that this model has an acceptable output while being simple in calculations and has the ability to predict different norms. It can also be hoped that improved and more detailed results will be achieved in the study of musculoskeletal systems with the development of this model.