Muscle Synergy-Based Iterative Learning Control for Upper Limb Functional Electrical Stimulation in Stroke Rehabilitation.

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Yanhong Liu, Yaowei Li, Zan Zhang, Benyan Huo, Long Cheng, Anqin Dong, Gen Li
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Abstract

Functional Electrical Stimulation (FES) is widely used in the postoperative rehabilitation of stroke patients. Multi-channel FES enables alternating stimulation of multiple muscle groups, effectively delaying muscle fatigue and facilitating precise control of complex upper limb movements. However, high-dimensional control of multiple muscles introduces additional challenges, particularly in coordinating antagonistic muscles and achieving efficient control. This study proposes a novel FES control framework that integrates muscle synergy theory, Long Short-Term Memory (LSTM) networks, and Iterative Learning Control (ILC). In this framework, the LSTM network predicts synergy activation coefficients from joint kinematics (angle and angular velocity), while the ILC algorithm iteratively updates electrical stimulation intensities based on the tracking error from previous iterations. This combination reduces the dimensionality of muscle control and improves the balance of muscle group activation, aligning better with natural motor control strategies. Experiments conducted on eight healthy subjects demonstrated that the proposed synergy-based ILC method significantly reduced joint angle tracking errors (measured by RMSE) over 10 stimulation iterations, compared to reference trajectories derived from voluntary motion. Specifically, in the combined elbow-wrist drinking task, the wrist RMSE decreased from 13.10° to 4.19°, and the elbow RMSE decreased from 45.07° to 5.53°. The coefficient of determination (R2), reflecting the goodness of fit between predicted and reference trajectories, exceeded 0.96, indicating high tracking accuracy and stability. Preliminary experiments on three stroke patients further support the adaptability and clinical potential of the proposed method.

基于肌肉协同的上肢功能性电刺激脑卒中康复迭代学习控制。
功能电刺激(FES)广泛应用于脑卒中患者术后康复。多通道FES可以交替刺激多个肌群,有效延缓肌肉疲劳,促进上肢复杂动作的精确控制。然而,多肌肉的高维控制带来了额外的挑战,特别是在协调拮抗肌肉和实现有效控制方面。本研究提出了一个新的FES控制框架,该框架整合了肌肉协同理论、长短期记忆(LSTM)网络和迭代学习控制(ILC)。在该框架中,LSTM网络根据关节运动学(角度和角速度)预测协同激活系数,而ILC算法根据之前迭代的跟踪误差迭代更新电刺激强度。这种组合降低了肌肉控制的维度,改善了肌肉群激活的平衡,更好地与自然运动控制策略一致。在8名健康受试者身上进行的实验表明,与来自自主运动的参考轨迹相比,基于协同的ILC方法在10次刺激迭代中显著降低了关节角度跟踪误差(以RMSE测量)。具体来说,在肘关节联合饮酒任务中,手腕的RMSE从13.10°下降到4.19°,肘关节的RMSE从45.07°下降到5.53°。反映预测轨迹与参考轨迹拟合优度的决定系数(R2)超过0.96,表明跟踪精度和稳定性较高。对三例脑卒中患者的初步实验进一步支持了该方法的适应性和临床潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
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