Meyer Andrew J, Eskinazi Ilan, Jackson Jennifer N, Rao Anil V, Patten Carolynn, Fregly Benjamin J
Department of Mechanical and Aerospace Engineering, University of Florida , Gainesville, FL , USA.
Department of Biomedical Engineering, University of Florida , Gainesville, FL , USA.
Front Bioeng Biotechnol. 2016 Oct 13;4:77. doi: 10.3389/fbioe.2016.00077. eCollection 2016.
Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject's self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot-ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject's walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject's walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject's walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations.
研究人员探索了多种神经康复方法，以恢复中风后正常的行走功能。然而，目前尚无客观方法来为任何特定患者制定和实施可能使行走功能恢复最大化的治疗方案。作为优化神经康复效果的第一步，本研究开发并评估了一种针对患者的协同控制神经肌肉骨骼模拟框架，该框架可以预测中风后个体的行走运动。我们解决的主要问题是，与由肌肉激活控制（每条腿35个）或关节扭矩控制（每条腿5个）驱动的模型相比，用肌肉协同控制（每条腿5个）驱动特定于个体的神经肌肉骨骼模型是否有助于生成准确的行走预测。为了探究这个问题，我们利用在受试者自行选择的0.5米/秒速度下收集的仪器化跑步机行走数据，开发了一个单一高功能偏瘫受试者的特定于个体的神经肌肉骨骼模型。该模型包括下肢运动学结构、足底与地面接触行为、肌电图驱动的肌肉力量生成以及神经控制限制和剩余能力的特定于个体的表示。使用直接配置最优控制和特定于个体的模型，我们评估了三种控制方法在两种速度（0.5和0.8米/秒）下预测受试者行走运动学和动力学的能力，这两种速度下有来自该受试者的实验数据。我们还评估了协同控制是否能在一种速度（1.1米/秒）下预测出符合物理实际的步态周期，而该速度下没有实验数据。对于0.5米/秒的模型校准速度，所有三种控制方法都能很好地预测受试者的行走运动学和动力学（包括地面反作用力）。然而，对于0.8米/秒的更快的非校准速度，只有激活控制和协同控制能很好地预测受试者的行走运动学和动力学，其中协同控制对新步态周期的预测最为准确。当用于预测受试者在1.1米/秒时的行走方式时，协同控制预测的步态周期与根据步态速度和步幅长度之间的线性关系估计的步态周期相近。这些发现表明，我们的神经肌肉骨骼模拟框架或许能够弥合特定于患者的肌肉协同信息与由此产生的功能能力和限制之间的差距。
