PDF(Pubmed)
Abstract：
UNASSIGNED: Functional electrical stimulation (FES) is an established method of supporting neurological rehabilitation. However, particularly on the forearm, it still cannot elicit selective muscle activations that form the basis of complex hand movements. Current research approaches in the context of selective muscle activation often attempt to enable targeted stimulation by increasing the number of electrodes and combining them in electrode arrays. In order to determine the best stimulation positions and settings, manual or semi-automated algorithms are used. This approach is limited due to experimental limitations. The supportive use of simulation studies is well-established, but existing simulation models are not suitable for analyses of selective muscle activation due to missing or arbitrarily arranged innervation zones.
UNASSIGNED: This study introduces a new modeling method to design a person-specific digital twin that enables the prediction of muscle activations during FES on the forearm. The designed individual model consists of three parts: an anatomically based 3D volume conductor, a muscle-specific nerve fiber arrangement in various regions of interest (ROIs), and a standard nerve model. All processes were embedded in scripts or macros to enable automated changes to the model and the simulation setup.
UNASSIGNED: The experimental evaluation of simulated strength-duration diagrams showed good coincidence. The relative differences of the simulated amplitudes to the mean amplitude of the four experiments were in the same range as the inter-experimental differences, with mean values between 0.005 and 0.045. Based on these results, muscle-specific activation thresholds were determined and integrated into the simulation process. With this modification, simulated force-intensity curves showed good agreement with additionally measured curves.
UNASSIGNED: The results show that the model is suitable for simulating realistic muscle-specific activations. Since complex hand movements are physiologically composed of individual, selective muscle activations, it can be assumed that the model is also suitable for simulating these movements. Therefore, this study presents a new and very promising approach for developing new applications and products in the context of the rehabilitation of sensorimotor disorders.
UNASSIGNED: This study introduces a new modeling method to design a person-specific digital twin that enables the prediction of muscle activations during FES on the forearm. The designed individual model consists of three parts: an anatomically based 3D volume conductor, a muscle-specific nerve fiber arrangement in various regions of interest (ROIs), and a standard nerve model. All processes were embedded in scripts or macros to enable automated changes to the model and the simulation setup.
UNASSIGNED: The experimental evaluation of simulated strength-duration diagrams showed good coincidence. The relative differences of the simulated amplitudes to the mean amplitude of the four experiments were in the same range as the inter-experimental differences, with mean values between 0.005 and 0.045. Based on these results, muscle-specific activation thresholds were determined and integrated into the simulation process. With this modification, simulated force-intensity curves showed good agreement with additionally measured curves.
UNASSIGNED: The results show that the model is suitable for simulating realistic muscle-specific activations. Since complex hand movements are physiologically composed of individual, selective muscle activations, it can be assumed that the model is also suitable for simulating these movements. Therefore, this study presents a new and very promising approach for developing new applications and products in the context of the rehabilitation of sensorimotor disorders.
摘要：
■功能性电刺激（FES）是一种支持神经康复的既定方法。然而,尤其是前臂，它仍然不能引起形成复杂手部运动基础的选择性肌肉激活。在选择性肌肉激活的背景下的当前研究方法通常试图通过增加电极的数量并将它们组合在电极阵列中来实现靶向刺激。为了确定最佳的刺激位置和设置，使用手动或半自动算法。这种方法由于实验限制而受到限制。模拟研究的支持性使用已经确立，但是现有的模拟模型不适合分析由于缺失或任意排列的神经支配区而导致的选择性肌肉激活。
■这项研究引入了一种新的建模方法，以设计一个特定于人的数字双胞胎，该双胞胎能够预测前臂上FES期间的肌肉活动。设计的单个模型由三部分组成：基于解剖学的3D体积导体，不同感兴趣区域(ROI)中的肌肉特异性神经纤维排列，和标准神经模型.所有过程都嵌入在脚本或宏中，以实现对模型和仿真设置的自动更改。
■模拟强度-持续时间图的实验评估显示出良好的一致性。模拟振幅与四个实验的平均振幅的相对差异与实验间差异在相同的范围内，平均值在0.005和0.045之间。基于这些结果,确定肌肉特异性激活阈值并将其整合到模拟过程中.有了这个修改，模拟力-强度曲线与额外测量曲线吻合良好。
■结果表明，该模型适用于模拟逼真的肌肉特异性激活。由于复杂的手部动作是由个体生理组成的，选择性肌肉激活，可以假设该模型也适用于模拟这些运动。因此,这项研究提出了一种新的，非常有希望的方法，用于在感觉运动障碍的康复中开发新的应用和产品。
■这项研究引入了一种新的建模方法，以设计一个特定于人的数字双胞胎，该双胞胎能够预测前臂上FES期间的肌肉活动。设计的单个模型由三部分组成：基于解剖学的3D体积导体，不同感兴趣区域(ROI)中的肌肉特异性神经纤维排列，和标准神经模型.所有过程都嵌入在脚本或宏中，以实现对模型和仿真设置的自动更改。
■模拟强度-持续时间图的实验评估显示出良好的一致性。模拟振幅与四个实验的平均振幅的相对差异与实验间差异在相同的范围内，平均值在0.005和0.045之间。基于这些结果,确定肌肉特异性激活阈值并将其整合到模拟过程中.有了这个修改，模拟力-强度曲线与额外测量曲线吻合良好。
■结果表明，该模型适用于模拟逼真的肌肉特异性激活。由于复杂的手部动作是由个体生理组成的，选择性肌肉激活，可以假设该模型也适用于模拟这些运动。因此,这项研究提出了一种新的，非常有希望的方法，用于在感觉运动障碍的康复中开发新的应用和产品。
