Al Kouzbary Mouaz, Al Kouzbary Hamza, Liu Jingjing, Shasmin Hanie Nadia, Arifin Nooranida, Osman Noor Azuan Abu
Center for Applied Biomechanics, Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia.
The Chancellery, University of Malaya, Kuala Lumpur, Malaysia.
Comput Methods Biomech Biomed Engin. 2024 Sep 4:1-13. doi: 10.1080/10255842.2024.2399023.
The aim of the present study is to investigate the complexity and stability of human ambulation and the implications on robotic prostheses control systems. Fourteen healthy individuals participate in two experiments, the first group run at three different speeds. The second group ascended and descended stairs of a five-level building block at a self-selected speed. All participants completed the experiment with seven inertial measurement units wrapped around the lower body segments and waist. The data were analyzed to determine the fractal dimension, spectral entropy, and the Lyapunov exponent (LyE). Two methods were used to calculate the long-term LyE, first LyE calculated using the full size of data sets. And the embedding dimensions were calculated using Average Mutual Information (AMI) and the False Nearest Neighbor (FNN) algorithm was used to find the time delay. Besides, a second approach was developed to find long-term LyE where the time delay was based on the average period of the gait cycle using adaptive event-based window. The average values of spectral entropy are 0.538 and 0.575 for stairs ambulation and running, respectively. The degree of uncertainty and complexity increases with the ambulation speed. The short term LyEs for tibia orientation have the minimum range of variation when it comes to stairs ascent and descent. Using two-way analysis of variance we demonstrated the effect of the ambulation speed and type of ambulation on spectral entropy. Moreover, it was shown that the fractal dimension only changed significantly with ambulation speed.
本研究的目的是调查人类行走的复杂性和稳定性以及对机器人假肢控制系统的影响。14名健康个体参与了两项实验，第一组以三种不同速度跑步。第二组以自选速度在一个五层积木楼梯上上下行走。所有参与者在下半身各部位和腰部佩戴七个惯性测量单元完成实验。对数据进行分析以确定分形维数、频谱熵和李雅普诺夫指数（LyE）。使用两种方法计算长期LyE，第一种是使用完整数据集计算LyE。使用平均互信息（AMI）计算嵌入维数，并使用伪最近邻（FNN）算法找到时间延迟。此外，还开发了第二种方法来找到长期LyE，其中时间延迟基于使用基于自适应事件的窗口的步态周期平均周期。上下楼梯行走和跑步时频谱熵的平均值分别为0.538和0.575。不确定性和复杂性程度随着行走速度的增加而增加。在上下楼梯时，胫骨方向的短期LyE变化范围最小。使用双向方差分析，我们证明了行走速度和行走类型对频谱熵的影响。此外，结果表明分形维数仅随行走速度有显著变化。
