Kang Hyun Gu, Dingwell Jonathan B
Nonlinear Biodynamics Lab, Department of Kinesiology and Health Education, University of Texas at Austin, Austin, TX 78712, United States.
Gait Posture. 2006 Nov;24(3):386-90. doi: 10.1016/j.gaitpost.2005.11.004. Epub 2006 Jan 18.
While local dynamic stability measures have been successfully used to characterize walking stability, they require long continuous walking data, which may be difficult to obtain from a clinical population. We investigated the amount of walking data necessary to obtain reliable measures of local dynamic stability. Twenty healthy adults walked on a motorized treadmill at their self-selected speed for three trials of 5 min each. Trunk motion was used to construct a 12-dimensional state space comprised of the linear and angular positions and velocities. Mean divergence of locally perturbed trajectories was calculated as a measure of local dynamic stability using the first 1-5 min of data from each trial. Exponential divergence rates were quantified. Divergence was also parameterized using a double-exponential function. Intra-class correlation coefficients ICC(2,1) were calculated for each divergence measure for each trial length. ICC(2, 1) values increased with trial length, and reached 0.5-0.9. Good reliability was obtained for short-term measures for trial lengths of 2 and 3 min, but 5 min was not adequate to estimate the long-term coefficients based on a single trial.
虽然局部动态稳定性测量方法已成功用于表征步行稳定性,但它们需要长时间的连续步行数据,而从临床人群中可能难以获取这些数据。我们研究了获得可靠的局部动态稳定性测量所需的步行数据量。20名健康成年人在电动跑步机上以自选速度行走,每次进行3次时长为5分钟的试验。利用躯干运动构建了一个由线性和角位置及速度组成的12维状态空间。使用每次试验前1 - 5分钟的数据计算局部扰动轨迹的平均发散度,作为局部动态稳定性的一种测量方法。对指数发散率进行了量化。发散度也使用双指数函数进行参数化。针对每个试验长度的每种发散度测量方法计算组内相关系数ICC(2,1)。ICC(2,1)值随试验长度增加,达到0.5 - 0.9。对于2分钟和3分钟试验长度的短期测量方法获得了良好的可靠性,但5分钟不足以基于单次试验估计长期系数。