IEEE Trans Neural Syst Rehabil Eng. 2018 Jan;26(1):188-196. doi: 10.1109/TNSRE.2017.2732448. Epub 2017 Jul 27.
The analysis of gait dynamics is helpful for predicting and improving the quality of life, morbidity, and mortality in neuro-degenerative patients. Feature extraction of physiological time series and classification between gait patterns of healthy control subjects and patients are usually carried out on the basis of 1-D signal analysis. The proposed approach presented in this paper departs itself from conventional methods for gait analysis by transforming time series into images, of which texture features can be extracted from methods of texture analysis. Here, the fuzzy recurrence plot algorithm is applied to transform gait time series into texture images, which can be visualized to gain insight into disease patterns. Several texture features are then extracted from fuzzy recurrence plots using the gray-level co-occurrence matrix for pattern analysis and machine classification to differentiate healthy control subjects from patients with Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis. Experimental results using only the right stride-intervals of the four groups show the effectiveness of the application of the proposed approach.
步态动力学分析有助于预测和改善神经退行性疾病患者的生活质量、发病率和死亡率。生理时间序列的特征提取和健康对照组与患者之间步态模式的分类通常基于一维信号分析来进行。本文提出的方法通过将时间序列转换为图像来脱离传统的步态分析方法，其中可以从纹理分析方法中提取纹理特征。在这里，模糊递归图算法被应用于将步态时间序列转换为纹理图像，可以对其进行可视化以深入了解疾病模式。然后，使用灰度共生矩阵从模糊递归图中提取几个纹理特征，用于模式分析和机器分类，以区分健康对照组与帕金森病、亨廷顿病和肌萎缩性侧索硬化症患者。仅使用四组中右侧步幅间隔的实验结果表明了所提出方法的应用的有效性。
