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1- Biomedical Engineering Group, Department of Electrical Engineering, Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Mashhad, Iran.
Abstract:
Purpose of the study: Clean, noise-free data is an ideal, but often unattainable, circumstance in biological control systems. Filters are usually employed to remove noise, but this process also leads to the loss or alteration of information. A considerable challenge is managing the uncertain knowledge using a proper and realistic mathematical representation and staying consistent with the actual biological patterns and behaviors. The purpose of this study is to explore the potential of fuzzy logic as a computational paradigm to manage uncertainties in the nonlinear dynamics of human walking, a field that has paid little attention to this aspect despite its considerable nonlinear and uncertain behavior due to adaptability, muscle fatigue, environmental noise, and external disturbances.
Method: We employed a fuzzy logic-based controller, integrated with Functional Electrical Stimulation (FES) and the concept of a gait basin of attraction, to enhance gait performance. Our controller focused on accommodating imprecision in shank angle deviation and angular velocity, rather than relying on predetermined trajectories.
Results: Our findings indicate that more fuzzy rules and partitions improve the similarity of the gait dynamics to those of a healthy human. Moreover, higher membership function overlaps lead to more robust gait control.
Conclusion: The study demonstrates that fuzzy logic can effectively manage uncertainties in the nonlinear dynamics of human walking, improving gait performance and robustness. This approach offers a promising direction for goal-oriented rehabilitation strategies by mimicking the human mind's ability to handle challenging and unknown environments.
Method: We employed a fuzzy logic-based controller, integrated with Functional Electrical Stimulation (FES) and the concept of a gait basin of attraction, to enhance gait performance. Our controller focused on accommodating imprecision in shank angle deviation and angular velocity, rather than relying on predetermined trajectories.
Results: Our findings indicate that more fuzzy rules and partitions improve the similarity of the gait dynamics to those of a healthy human. Moreover, higher membership function overlaps lead to more robust gait control.
Conclusion: The study demonstrates that fuzzy logic can effectively manage uncertainties in the nonlinear dynamics of human walking, improving gait performance and robustness. This approach offers a promising direction for goal-oriented rehabilitation strategies by mimicking the human mind's ability to handle challenging and unknown environments.
Keywords: Rehabilitation, Gait performance, Fuzzy control, Knowledge management, Imprecision tolerance, Functional Electrical Stimulation
Type of Study: Original |
Subject:
Computational Neuroscience
Received: 2024/07/29 | Accepted: 2024/09/9
Received: 2024/07/29 | Accepted: 2024/09/9
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