Department of Mechanical Engineering, The University of Auckland, Auckland, 1010, New Zealand.
Department of Mechanical Engineering, The University of Auckland, Auckland, 1010, New Zealand.
Comput Biol Med. 2018 Dec 1;103:269-276. doi: 10.1016/j.compbiomed.2018.10.027. Epub 2018 Nov 1.
Muscles of individuals with Cerebral Palsy (CP) undergo structural changes over their lifespan including an increase in muscle stiffness, decreased strength and coordination. Being able to identify these changes non-invasively would be beneficial to improve understanding of CP and assess therapy effectiveness over time. This study aims to adapt an existing EMG-driven Hill-type muscle model for neuromuscular characterisation during isometric contractions of the elbow joint.
Participants with (n = 2) and without CP (n = 8) performed isometric force ramps with contraction levels ranging between 15 and 70% of their maximum torque. During these contractions, high-density EMG data were collected from the M. Biceps and Triceps brachii with 64 electrodes on each muscle. The EMG-driven Hill-type muscle model was used to predict torques around the elbow joint, and muscle characterisation was performed by applying a genetic algorithm that tuned individuals' parameters to reduce the RMS error between observed and predicted torque data.
Observed torques could be predicted accurately with an overall mean error of 1.24Nm ± 0.53Nm when modelling individual force ramps. The first four parameters of the model could be identified relatively reliably across different experimental protocols with a full-scale variation of below 20%.
An HD-EMG muscle modelling approach to evaluating neuromuscular properties in participants with and without CP has been presented. This pilot study confirms the feasibility of the experimental protocol and demonstrates some parameters can be identified robustly using the isometric contraction force ramps.
脑瘫（CP）患者的肌肉在其整个生命周期中会发生结构变化，包括肌肉僵硬增加、力量和协调性下降。能够非侵入性地识别这些变化将有助于提高对 CP 的理解，并随着时间的推移评估治疗效果。本研究旨在改编现有的肌电图驱动的 Hill 型肌肉模型，用于在等长收缩期间对肘关节进行神经肌肉特征分析。
CP 患者（n=2）和无 CP 患者（n=8）进行了等长力斜坡试验，收缩水平范围为最大扭矩的 15%至 70%。在这些收缩中，从肱二头肌和肱三头肌收集了 64 个电极的高密度肌电图数据。使用肌电图驱动的 Hill 型肌肉模型来预测肘关节周围的扭矩，并通过应用遗传算法来调整个体参数以减少观察到的和预测的扭矩数据之间的均方根误差来进行肌肉特征分析。
当单独建模力斜坡时，观察到的扭矩可以被准确地预测，总体平均误差为 1.24Nm±0.53Nm。模型的前四个参数可以在不同的实验方案中相对可靠地识别出来，全尺度变化低于 20%。
提出了一种使用高密度肌电图进行肌肉建模来评估 CP 患者和非 CP 患者神经肌肉特性的方法。这项初步研究证实了实验方案的可行性，并证明了使用等长收缩力斜坡可以可靠地识别一些参数。
