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Adaptive weighted fuzzy ILC for personalized FES trajectory reconstruction using an anatomical prior
Shiqin Liu1, Jiao Wu2, Weihua He1
1Ningxia University School of Information Engineering, School of Information Engineering, Ningxia University, Yinchuan, Ningxia 750021, China, Key Laboratory of Artificial Intelligence and Information Security for Channeling Computing Resources from the East to the West of Ningxia, Yinchuan, Ningxia, China, Yinchuan, Ningxia, 750021, China.
Objective:
The clinical translation of closed-loop functional electrical stimulation (FES) systems is hindered by three interrelated bottlenecks: the trade-off between algorithmic complexity and embedded real-time feasibility, inefficient personalization, and a lack of empirical validation of human-machine cooperative performance in stroke patients.
Approach:
We propose a physiology-informed adaptive weighted fuzzy iterative learning control (AW-FILC) algorithm, implemented on a custom STM32F407-based embedded FES platform. The core innovation is a dual-layer adaptive mechanism that incorporates subcutaneous fat thickness T_s, which is a key biophysical determinant of current attenuation measurable, as a feedforward prior. This prior is fused with real-time kinematic error feedback to dynamically modulate the learning gain and penalty factor.
Main Results:
In simulations across two heterogeneous musculoskeletal models, AW-FILC achieves convergence within 5-7 iterations, with steady-state tracking accuracy of 10⁻⁵ for normalized torque and <0.13°for joint angles, outperforming PID-ILC and Type-1/Type-2 fuzzy ILC. In healthy participants (n=6), AW-FILC combined with voluntary effort achieved a trajectory tracking Pearson correlation of 0.87±0.04, substantially exceeding the pure-FES ceiling (0.80) and approaching the physiological gold standard of natural bilateral coordination (0.984±0.014). In stroke patients (n=8), AW-FILC elevated tracking correlation from 0.24 (pure open-loop FES) to 0.30±0.02. This modest numerical gain represents a qualitative shift from pathological toward normalized movement patterns. The embedded implementation achieves per-iteration execution < 2 ms on STM32F407, and the system respects rather than quantifies patient voluntary effort, aligning with activity-dependent neuroplasticity principles.
Significance:
This work provides a computationally efficient, physiologically adaptive control core for embedded FES devices and empirically validates that AW-FILC-driven active rehabilitation bridges the gap between pathological and physiological coordination.
