Li L, Tong K Y, Hu X L, Hung L K, Koo T K K
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
Clin Biomech (Bristol). 2009 Jan;24(1):101-9. doi: 10.1016/j.clinbiomech.2008.08.008. Epub 2008 Nov 13.
This study was to extend previous neuromusculoskeletal modeling efforts through combining the in vivo ultrasound-measured musculotendon parameters on persons after stroke.
A subject-specific neuromusculoskeletal model of the elbow was developed to predict the individual muscle force during dynamic movement and then validated by joint trajectory. The model combined a geometrical model and a Hill-type musculotendon model, and used subject-specific musculotendon parameters as inputs. EMG signals and joint angle were recorded from healthy control subjects (n=4) and persons after stroke (n=4) during voluntary elbow flexion in a vertical plane. Ultrasonography was employed to measure the muscle optimal length and pennation angle of each prime elbow flexor (biceps brachii, brachialis, brachioradialis) and extensor (three heads of triceps brachii). Maximum isometric muscle stresses of the flexor and extensor muscle group were calibrated by minimizing the root mean square difference between the predicted and measured maximum isometric torque-angle curves. These parameters were then inputted into the neuromusculoskeletal model to predict the individual muscle force using the input of EMG signals directly without any trajectory fitting procedure involved.
The results showed that the prediction of voluntary flexion in the hemiparetic group using subject-specific parameters data was better than that using cadaveric data extracted from the literature.
The results demonstrated the feasibility of using EMG-driven neuromusculoskeletal modeling with direct ultrasound measurement for the prediction of voluntary elbow movement for both subjects without impairment and persons after stroke.
本研究旨在通过结合中风患者体内超声测量的肌腱参数,扩展先前的神经肌肉骨骼建模工作。
建立了一个特定于个体的肘部神经肌肉骨骼模型,以预测动态运动期间的个体肌肉力量,然后通过关节轨迹进行验证。该模型结合了几何模型和希尔型肌腱模型,并使用特定于个体的肌腱参数作为输入。在垂直平面内进行自愿性肘部屈曲时,记录了健康对照受试者(n = 4)和中风患者(n = 4)的肌电图信号和关节角度。采用超声测量每个主要肘部屈肌(肱二头肌、肱肌、肱桡肌)和伸肌(肱三头肌三个头)的肌肉最佳长度和羽状角。通过最小化预测和测量的最大等长扭矩 - 角度曲线之间的均方根差异,校准屈肌和伸肌组的最大等长肌肉应力。然后将这些参数输入神经肌肉骨骼模型,直接使用肌电图信号输入预测个体肌肉力量,无需任何轨迹拟合程序。
结果表明,使用特定于个体的参数数据对偏瘫组自愿屈曲的预测优于使用从文献中提取的尸体数据的预测。
结果证明了使用肌电图驱动的神经肌肉骨骼建模结合直接超声测量来预测无损伤受试者和中风患者自愿肘部运动的可行性。