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Quaternion Entropy for Analysis of Gait Data

机译：四元数熵用于步态数据分析

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Nonlinear dynamical analysis is a powerful approach to understanding biological systems. One of the most used metrics of system complexities is the Kolmogorov entropy. Long input signals without noise are required for the calculation, which are very hard to obtain in real situations. Techniques allowing the estimation of entropy directly from time signals are statistics like approximate and sample entropy. Based on that, the new measurement for quaternion signal is introduced. This work presents an example of application of a nonlinear time series analysis by using the new quaternion, approximate entropy to analyse human gait kinematic data. The quaternion entropy was applied to analyse the quaternion signal which represents the segments orientations in time during the human gait. The research was aimed at the assessment of the influence of both walking speed and ground slope on the gait control during treadmill walking. Gait data was obtained by the optical motion capture system.
机译：非线性动力学分析是了解生物系统的有力方法。系统复杂度最常用的指标之一是Kolmogorov熵。计算需要没有噪声的长输入信号，这在实际情况下很难获得。允许直接从时间信号估计熵的技术是诸如近似熵和样本熵之类的统计数据。在此基础上，介绍了四元数信号的新测量方法。这项工作通过使用新的四元数，近似熵来分析人体步态运动学数据，提供了非线性时间序列分析的应用示例。应用四元数熵分析四元数信号，该信号表示人类步态中各个时间段的方向。该研究旨在评估跑步机步行过程中步行速度和地面坡度对步态控制的影响。步态数据是通过光学运动捕捉系统获得的。

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