Gait rhythm fluctuations are of great importance for automatic neurodegenerative diseases (NDDs) detection. They provide a cost-effective and noninvasive monitoring tool in which their parameters are related to neuromuscular function. This study investigated a new solution based on a set of new symmetric features and sparse non-negative least squares (NNLS) coding classifier. Dynamic gait series warping (DGSW), Euclidean, Manhattan, Minkowski, Chebyshev, Canberra distances, and cosine function were used to quantify the amount of divergence between the left and right stride, swing, and stance intervals. The algorithm was evaluated using the gait signals of 16 healthy control subjects, 13 patients with amyotrophic lateral sclerosis (ALS), 15 patients with Parkinson's disease (PD) and 20 patients with Huntington's disease (HD). The proposed new approach using symmetric features and NNLS technique achieved outstanding accuracies of 98%, 97%, and 95% on the patients with PD, ALS, and HD, respectively. The findings also suggested that the new DGSW, cosine function, and Chebyshev distance, which are designed to dynamically, geometrically, or nonlinearly quantify the similarity between two time series, provide the discriminatory measures to describe how NDDs alter the gait symmetry. In comparison with other studies, combining symmetric features with a sparse NNLS coding classifier can improve the detection accuracy providing an efficient and cost-effective framework for the development of a NDDs detection system.

中文翻译：

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使用距离度量和稀疏编码进行神经退行性疾病检测：步态对称特征的新视角。

步态节奏波动对于自动神经退行性疾病（NDD）检测非常重要。它们提供了一种经济有效的无创监测工具，其参数与神经肌肉功能有关。这项研究研究了基于一组新的对称特征和稀疏非负最小二乘（NNLS）编码分类器的新解决方案。动态步态序列翘曲（DGSW），欧几里得，曼哈顿，明可夫斯基，切比雪夫，堪培拉距离和余弦函数被用来量化左右步幅，挥杆和姿势间隔之间的差异量。使用16位健康对照受试者，13例肌萎缩性侧索硬化症（ALS），15例帕金森氏病（PD）和20例亨廷顿氏病（HD）的步态信号评估算法。提出的使用对称特征和NNLS技术的新方法分别在PD，ALS和HD患者中实现了98％，97％和95％的出色准确性。研究结果还表明，新的DGSW，余弦函数和Chebyshev距离旨在动态，几何或非线性地量化两个时间序列之间的相似性，提供了描述NDD如何改变步态对称性的区分性措施。与其他研究相比，将对称特征与稀疏NNLS编码分类器结合在一起可以提高检测精度，从而为开发NDDs检测系统提供了有效且具有成本效益的框架。研究结果还表明，新的DGSW，余弦函数和Chebyshev距离旨在动态，几何或非线性地量化两个时间序列之间的相似性，提供了描述NDD如何改变步态对称性的区分性措施。与其他研究相比，将对称特征与稀疏NNLS编码分类器结合在一起可以提高检测精度，从而为开发NDDs检测系统提供了有效且具有成本效益的框架。研究结果还表明，新的DGSW，余弦函数和Chebyshev距离旨在动态，几何或非线性地量化两个时间序列之间的相似性，提供了描述NDD如何改变步态对称性的区分性措施。与其他研究相比，将对称特征与稀疏NNLS编码分类器结合在一起可以提高检测精度，从而为开发NDDs检测系统提供了有效且具有成本效益的框架。