uncertainty management in the dynamics of biological systems: a key to goal-oriented rehabilitation

Introduction: clean, noise-free data are ideal but often unattainable 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 biological patterns and behaviors. this study explores the potential of fuzzy logic as a computational paradigm to manage uncertainties in the nonlinear dynamics of human walking. this field has paid little attention to this aspect despite its considerable nonlinear and uncertain behavior due to adaptability, muscle fatigue, environmental noise, and external disturbances.methods: we employed a fuzzy logic-based controller integrated with functional electrical stimulation (fes) and a gait basin of attraction concept 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.