Post-Stroke Motor Recovery: Modern Approaches Based on Neuroplasticity and Sensory Feedback

Stroke remains one of the leading causes of long-term disability worldwide, severely impairing motor and cognitive functions. Post-stroke recovery involves complex neuroplastic mechanisms that cannot be adequately described by classical linear models. In this context, fractal theory and nonlinear dynamics offer an innovative framework for understanding and optimizing rehabilitation processes. This study aims to evaluate the applicability of fractal theory in post-stroke rehabilitation by identifying relationships between fractal movement complexity and functional recovery, and by proposing a personalized rehabilitation model based on fractal indicators. Using METLAB-based nonlinear analysis, fractal dimension (FD) and Lyapunov exponents (λ) were applied to simulated and representative time series of movement and neuronal activity. Fractal parameters were interpreted in relation to movement rigidity, stability, and adaptability. Reduced FD values (1.08) and large negative Lyapunov exponents (λ ≈ −1.12) were observed in post-stroke motor patterns, indicating rigidity and reduced adaptability. These findings support the need for variable, feedback-based, and robot-assisted rehabilitation strategies. Fractal theory provides an objective framework for evaluating and optimizing post-stroke recovery and enables personalized rehabilitation strategies based on movement complexity and stability.