Center for Computational Systems Biology, Fudan University, Shanghai, People's Republic of China.
PLoS Comput Biol. 2010 Dec 16;6(12):e1001033. doi: 10.1371/journal.pcbi.1001033.
Reliable characterization of locomotor dynamics of human walking is vital to understanding the neuromuscular control of human locomotion and disease diagnosis. However, the inherent oscillation and ubiquity of noise in such non-strictly periodic signals pose great challenges to current methodologies. To this end, we exploit the state-of-the-art technology in pattern recognition and, specifically, dimensionality reduction techniques, and propose to reconstruct and characterize the dynamics accurately on the cycle scale of the signal. This is achieved by deriving a low-dimensional representation of the cycles through global optimization, which effectively preserves the topology of the cycles that are embedded in a high-dimensional Euclidian space. Our approach demonstrates a clear advantage in capturing the intrinsic dynamics and probing the subtle synchronization patterns from uni/bivariate oscillatory signals over traditional methods. Application to human gait data for healthy subjects and diabetics reveals a significant difference in the dynamics of ankle movements and ankle-knee coordination, but not in knee movements. These results indicate that the impaired sensory feedback from the feet due to diabetes does not influence the knee movement in general, and that normal human walking is not critically dependent on the feedback from the peripheral nervous system.
可靠地描述人类行走的运动动力学对于理解人类运动的神经肌肉控制和疾病诊断至关重要。然而，这种非严格周期性信号中固有的振荡和噪声的普遍性给当前的方法带来了巨大的挑战。为此，我们利用模式识别的最新技术，特别是降维技术，并提出在信号的周期尺度上对动力学进行精确重构和描述。这是通过全局优化来获得周期的低维表示来实现的，该优化有效地保留了嵌入在高维欧几里得空间中的周期的拓扑结构。与传统方法相比，我们的方法在从单变量/多变量振荡信号中捕获内在动力学和探测微妙的同步模式方面具有明显的优势。将其应用于健康受试者和糖尿病患者的人体步态数据，揭示了踝关节运动和踝膝协调的动力学存在显著差异，但膝关节运动没有差异。这些结果表明，由于糖尿病导致的脚部感觉反馈受损一般不会影响膝关节运动，而且正常的人类行走并不严重依赖于周围神经系统的反馈。
