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论文arXiv 2610.2159013 天前

融合心理与身体参与度的上肢康复流理论多模态控制框架 ​

针对AAN难辨肌肉无力与心理不投入，融合EEG与sEMG参与度进行自适应阻抗控制，并在健康受试者上验证。

AI 整理
  • 硬件零部件
  • 本体 SDK 与控制

原文链接：A Flow-Theoretic Multimodal Control Framework Fusing Mental and Physical Engagement for Upper-Limb Rehabilitation（arXiv 2610.21590 · 发布日期：2026-10-15）

A Flow-Theoretic Multimodal Control Framework Fusing Mental and Physical Engagement for Upper-Limb Rehabilitation

针对AAN难辨肌肉无力与心理不投入，融合EEG与sEMG参与度进行自适应阻抗控制，并在健康受试者上验证。

核心要点 ​

  • 现有仅依赖身体指标的AAN难区分肌肉无力与心理不投入。
  • 框架融合EEG心理参与度和sEMG身体参与度，用模糊决策生成连续控制策略。
  • 可在意图执行不匹配时提供前馈辅助，在心理分心时提高引导刚度。
  • 较sEMG-only基线，能耗降71.9%，并保持精确轨迹跟踪。

验证范围与局限：仅在健康参与者的上肢外骨骼实验中，与sEMG-only AAN基线对比验证。

英文摘要 (Abstract) ​

While Assist-as-Needed (AAN) control aims to maximize user participation in robot-assisted training, existing methods relying solely on physical metrics cannot distinguish muscular inability from mental disengagement, leading to suboptimal interventions. To address this ambiguity, a Flow-theoretic multimodal AAN framework is proposed, fusing EEG-based mental engagement and sEMG-based physical engagement into an adaptive impedance control strategy. A four-quadrant fuzzy decision engine translates these dual physiological metrics into continuous control policies. This enables the upper-limb exoskeleton to inject feedforward assistance during intent-execution mismatch (e.g., motor output attenuation despite mental focus) and escalate guiding stiffness to penalize mental distraction. The framework was validated with healthy participants against a state-of-practice sEMG-only AAN baseline. Kinematically and mechanically, the proposed framework reduced robotic energy expenditure by 71.9% while maintaining precise trajectory tracking, confirming it elicited greater voluntary human effort. Physiologically, the system increased the time spent in the neuroplasticity zone (Flow State Ratio) from 4.6% to 42.8%. These results validate that decoupling and jointly regulating mental and physical states enhances the selectivity of robotic assistance, mitigating user passivity and sustaining the engagement crucial for motor learning.

作者：Jun Leng, Pengcheng Li, Hanze Wang, Shuxiang Guo, Junlin Jiang

来源

本页事实对应的公开页面，以原文为准。

  1. arXiv 2610.21590：A Flow-Theoretic Multimodal Control Framework Fusing Mental and Physical Engagement for Upper-Limb Rehabilitation访问 2026-10-21