Center for Biomedical Engineering Research, Department of Mechanical Engineering, University of Delaware, Newark, 19716, USA.
Comput Biol Med. 2009 Dec;39(12):1083-8. doi: 10.1016/j.compbiomed.2009.09.002. Epub 2009 Oct 8.
Individuals following stroke exhibit altered muscle activation and movement patterns. Improving the efficiency of gait can be facilitated by knowing which muscles are affected and how they contribute to the pathological pattern. In this paper we present an electromyographically (EMG) driven musculoskeletal model to estimate muscle forces and joint moments. Subject specific EMG for the primary ankle plantar and dorsiflexor muscles, and joint kinematics during walking for four subjects following stroke were used as inputs to the model to predict ankle joint moments during stance. The model's ability to predict the joint moment was evaluated by comparing the model output with the moment computed using inverse dynamics. The model did predict the ankle moment with acceptable accuracy, exhibiting an average R(2) value ranging between 0.87 and 0.92, with RMS errors between 9.7% and 14.7%. The values are in line with previous results for healthy subjects, suggesting that EMG-driven modeling in this population of patients is feasible. It is our hope that such models can provide clinical insight into developing more effective rehabilitation therapies and to assess the effects of an intervention.
中风患者的肌肉活动和运动模式会发生改变。了解哪些肌肉受到影响以及它们如何导致病理模式，可以促进步态效率的提高。在本文中，我们提出了一种基于肌电图（EMG）的肌肉骨骼模型，以估计肌肉力量和关节力矩。使用四个中风后患者的步行时的特定于个体的跖屈和背屈主要踝关节肌肉的肌电图以及关节运动学作为模型的输入，以预测站立时的踝关节关节力矩。通过将模型输出与使用逆动力学计算的力矩进行比较，评估模型预测关节力矩的能力。该模型能够以可接受的精度预测踝关节力矩，其平均 R（2）值在 0.87 到 0.92 之间，均方根误差在 9.7%到 14.7%之间。这些值与健康受试者的先前结果一致，表明在该患者人群中进行基于肌电图的建模是可行的。我们希望这些模型能够为开发更有效的康复治疗方法并评估干预措施的效果提供临床见解。
