Volume 11, Issue 1 (January & February 2020)                   BCN 2020, 11(1): 1-14 | Back to browse issues page


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Rezaee Z, Kobravi H R. Human Gait Control Using Functional Electrical Stimulation Based on Controlling the Shank Dynamics. BCN. 2020; 11 (1) :1-14
URL: http://bcn.iums.ac.ir/article-1-1180-en.html
1- Research Center of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
Abstract:  
Introduction: Efficient gait control using Functional Electrical Stimulation (FES) is an open research problem. In this research, a new intermittent controller has been designed to control the human shank movement dynamics during gait.
Methods: In this approach, first, the three-dimensional phase space was constructed using the human shank movement data recorded from the healthy subjects. Then, three iterated sine-circle maps were extracted in the mentioned phase space. The three identified one-dimensional maps contained the essential information about the shank movement dynamics during a gait cycle. Next, an intermittent fuzzy controller was designed to control the shank angle. According to the adopted intermittent control strategy, the fuzzy controller is activated whenever the shank angle is far enough from the specific. The specific points are described using the identified iterated maps in the constructed phase space. In this manner, the designed controller is activated during a short-time fraction of the gait cycle time.
Results: The designed intermittent controller was evaluated through some simulation studies on a two-joint musculoskeletal model. The obtained results suggested that the pattern of the obtained hip and knee joint trajectories, the outputs of the musculoskeletal model, were acceptably similar to the joints’ trajectories pattern of healthy subjects. 
Conclusion: The intriguing similarity was observed between the dynamics of the recorded human data and those of the controlled musculoskeletal model. It supports the acceptable performance of the proposed control strategy.
Type of Study: Original | Subject: Computational Neuroscience
Received: 2018/05/8 | Accepted: 2019/01/21 | Published: 2020/01/1

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