Department of Biomedical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA.
Med Eng Phys. 2012 Jun;34(5):617-24. doi: 10.1016/j.medengphy.2011.09.009. Epub 2011 Oct 12.
Repetitive reaching movements to a fixed target can be generally characterized by bell-shaped velocity profiles and sigmoidal trajectories with variable morphologies across multiple repetitions. A neuromuscular correspondence of these kinematic variations has thus far eluded electromyographic (EMG) analysis. We recorded EMG and elbow kinematics from fourteen healthy individuals performing repetitive, self-paced, isolated elbow flexions, with their arms supported against gravity. The global kinematic pattern of each flexion was classified as either sigmoidal (S) or non-sigmoidal (NS), based on goodness of fit with analytical curves. Ten of the fourteen subjects generated an approximately equal number of S and NS types (383 movement cycles). Trajectories of the other four subjects were not classifiable or did not vary sufficiently and were excluded from subsequent analysis. A post hoc predictor of trajectory type was derived by testing linear support vector machines trained with a strategically selected 3-feature sub-space of the early phase of enveloped biceps EMG during a leave-one-out cross-validation paradigm. Results showed that EMG features predicted kinematic morphology with sensitivity and specificity both exceeding 80%. The high predictive accuracy suggests neuromotor signals coding for subtle variations in elbow kinematics during self-paced, unloaded motions, can be deciphered from the biceps EMG.
重复性地向固定目标进行伸展运动通常具有钟形速度曲线和多重复时形态可变的 S 形轨迹特征。到目前为止,肌电图(EMG)分析还未能揭示这些运动学变化的神经肌肉对应关系。我们记录了 14 名健康个体在手臂支撑重力的情况下进行重复、自主节奏、孤立的肘部弯曲时的 EMG 和肘部运动学数据。根据与分析曲线的拟合程度,将每个弯曲的整体运动模式分为 S 型(S)或非 S 型(NS)。在 14 名受试者中,有 10 名受试者产生的 S 型和 NS 型运动的数量大致相等(383 个运动周期)。另外 4 名受试者的运动轨迹无法分类或变化不够充分,因此被排除在后续分析之外。通过在留出一个样本的交叉验证范例中,使用在包络肱二头肌 EMG 的早期阶段选择的 3 个特征子空间训练的线性支持向量机进行测试,得出了轨迹类型的事后预测器。结果表明,EMG 特征对运动形态的预测具有超过 80%的灵敏度和特异性。高预测精度表明,在自主、无负荷运动期间,编码肘部运动学细微变化的神经运动信号可以从肱二头肌 EMG 中解码。