Stroke is a leading cause of long-term disability worldwide and often impairs walking ability. To improve recovery of walking function post-stroke, researchers have investigated the use of treatments such as fast functional electrical stimulation (FastFES). During FastFES treatments, individuals post-stroke walk on a treadmill at their fastest comfortable speed while electrical stimulation is delivered to two muscles of the paretic ankle, ideally to improve paretic leg propulsion and toe clearance. However, muscle selection and stimulation timing are currently standardized based on clinical intuition and a one-size-fits-all approach, which may explain in part why some patients respond to FastFES training while others do not. This study explores how personalized neuromusculoskeletal models could potentially be used to enable individual-specific selection of target muscles and stimulation timing to address unique functional limitations of individual patients post-stroke. Treadmill gait data, including EMG, surface marker positions, and ground reactions, were collected from an individual post-stroke who was a non-responder to FastFES treatment. The patient's gait data were used to personalize key aspects of a full-body neuromusculoskeletal walking model, including lower-body joint functional axes, lower-body muscle force generating properties, deformable foot-ground contact properties, and paretic and non-paretic leg neural control properties. The personalized model was utilized within a direct collocation optimal control framework to reproduce the patient's unstimulated treadmill gait data (verification problem) and to generate three stimulated walking predictions that sought to minimize inter-limb propulsive force asymmetry (prediction problems). The three predictions used: (1) Standard muscle selection (gastrocnemius and tibialis anterior) with standard stimulation timing, (2) Standard muscle selection with optimized stimulation timing, and (3) Optimized muscle selection (soleus and semimembranosus) with optimized stimulation timing. Relative to unstimulated walking, the optimal control problems predicted a 41% reduction in propulsive force asymmetry for scenario (1), a 45% reduction for scenario (2), and a 64% reduction for scenario (3), suggesting that non-standard muscle selection may be superior for this patient. Despite these predicted improvements, kinematic symmetry was not noticeably improved for any of the walking predictions. These results suggest that personalized neuromusculoskeletal models may be able to predict personalized FastFES training prescriptions that could improve propulsive force symmetry, though inclusion of kinematic requirements would be necessary to improve kinematic symmetry as well.

中文翻译：

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FastFES 治疗的计算设计，以改善中风后步态期间的推进力对称性：可行性研究。

中风是世界范围内长期残疾的主要原因，并经常损害步行能力。为了改善中风后步行功能的恢复，研究人员研究了快速功能性电刺激 (FastFES) 等治疗方法的使用。在 FastFES 治疗期间，中风后个人以最快的舒适速度在跑步机上行走，同时电刺激被传递到麻痹脚踝的两块肌肉，最理想的是改善麻痹腿的推进力和脚趾间隙。然而，肌肉选择和刺激时间目前是基于临床直觉和一刀切的方法标准化的，这可能部分解释了为什么有些患者对 FastFES 训练有反应，而另一些则没有。本研究探讨了个性化神经肌肉骨骼模型如何潜在地用于实现个体特定的目标肌肉选择和刺激时间，以解决中风后个体患者的独特功能限制。跑步机步态数据，包括 EMG、表面标记位置和地面反应，是从对 FastFES 治疗无反应的个体中风后收集的。患者的步态数据用于个性化全身神经肌肉骨骼步行模型的关键方面，包括下半身关节功能轴、下半身肌肉力生成特性、可变形足-地面接触特性以及麻痹和非麻痹腿神经控制属性。在直接搭配优化控制框架内利用个性化模型来重现患者 s 未受刺激的跑步机步态数据（验证问题）并生成三个受刺激的步行预测，这些预测旨在最小化四肢间推进力的不对称性（预测问题）。使用的三个预测：（1）标准肌肉选择（腓肠肌和胫骨前肌）与标准刺激时间，（2）标准肌肉选择与优化刺激时间，以及（3）优化肌肉选择（比目鱼肌和半膜肌）与优化刺激时间。相对于无刺激步行，最优控制问题预测情景（1）的推进力不对称减少了 41%，情景（2）减少了 45%，情景（3）减少了 64%，表明非标准肌肉选择可能更适合该患者。尽管有这些预期的改进，对于任何步行预测，运动学对称性都没有明显改善。这些结果表明个性化的神经肌肉骨骼模型可能能够预测个性化的 FastFES 训练处方，从而改善推进力的对称性，尽管包括运动学要求对于改善运动学对称性也是必要的。