Dynamic electromyography. I. Numerical representation using principal component analysis.
Dynamic electromyography. I. Numerical representation using principal component analysis.
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动态肌电图。
DOI：
10.1002/jor.1100080214
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发表时间：
1990
期刊：
影响因子：
--
通讯作者：
Cochran,GV
中科院分区：
文献类型：
--
作者：
Wootten,ME;Kadaba,MP;Cochran,GV
对人类步态的完整描述需要考虑线性和时间步态参数，如速度、节奏和步幅长度，以及图形波形，如肢体旋转、力、关节力矩和肌肉的阶段性活动。这导致了大量的交互参数，使得步态数据的解释非常困难。统计模式识别技术可以简化这个问题。为了使这种方法取得成功，首先有必要将交互参数的数量减少到可管理的集合。在这项研究中，我们提出了主成分分析的应用，作为一种手段，以简洁的方式表示图形波形。特别地，我们专注于用表面电极记录35名正常人在水平行走时下肢10个主要肌肉的相肌活动。创建一个32点向量，其中每个点表示部分经过整流平滑的肌电信号曲线下的归一化面积，表示为步态周期的函数。计算主成分，保留前几个加权系数作为特征来表示原始肌电图数据。我们表明，相应的基向量跨越了个体受试者之间存在最大可变性的步态周期部分。我们还表明，基向量可以用来表示最初不用于生成基向量的受试者的肌电数据。
A complete description of human gait requires consideration of linear and temporal gait parameters such as velocity, cadence, and stride length, as well as graphic waveforms such as limb rotations, forces, and moments at the joints and phasic activity of muscles. This results in a large number of interactive parameters, making interpretation of gait data extremely difficult. Statistical pattern recognition techniques can simplify this problem. For this approach to be successful, first it is necessary to reduce the number of interactive parameters to a manageable set. In this study, we present an application of principal component analysis as a means for representing graphic waveforms in a parsimonious manner. In particular, we concentrate on representing the phasic muscle activity recorded using surface electrodes from ten major muscles of the lower extremity of 35 normal subjects during level walking. A 32 point vector is created in which each point of the vector represents the normalized area under the curve of a portion of rectified and smoothed electromyographic signal, expressed as a function of gait cycle. Principal components are computed and the first few weighting coefficients are retained as features to represent the original EMG data. We show that the corresponding basis vectors span parts of the gait cycle where the most variability between individual subjects exists. We also show that the basis vectors can be used to represent the EMG data of subjects not originally used to generate the basis vectors.