Giakas G, Baltzopoulos V
Division of Sport, Health and Exercise, Staffordshire University, Stoke-on-Trent, UK.
J Biomech. 1997 Aug;30(8):847-50. doi: 10.1016/s0021-9290(97)00042-0.
The purpose of this study was to compare and evaluate six automatic filtering techniques commonly used in biomechanics for filtering gait analysis kinematic signals namely: (1) power spectrum (signal-to-noise ratio) assessment; (2) generalised cross validation spline; (3) least-squares cubic splines; (4) regularisation of Fourier series; (5) regression model and (6) residual analysis. A battery of 1440 signals representing the displacements of seven markers attached upon the surface of the right lower limbs and one marker attached upon the surface of the sacrum during walking were used; their original signal and added noise characteristics were known a priori. The signals were filtered with every technique and the root mean square error between the filtered and reference signal was calculated for each derivative domain. Results indicated that among the investigated techniques there is not one that performs best in all the cases studied. Generally, the techniques of power spectrum estimation, least-squares cubic splines and generalised cross validation produced the most acceptable results.
本研究的目的是比较和评估生物力学中常用于过滤步态分析运动学信号的六种自动过滤技术，即：（1）功率谱（信噪比）评估；（2）广义交叉验证样条；（3）最小二乘三次样条；（4）傅里叶级数正则化；（5）回归模型和（6）残差分析。使用了一组1440个信号，这些信号代表行走过程中附着在右下肢表面的七个标记和附着在骶骨表面的一个标记的位移；它们的原始信号和添加噪声的特征是先验已知的。用每种技术对信号进行滤波，并针对每个导数域计算滤波后信号与参考信号之间的均方根误差。结果表明，在所研究的技术中，没有一种在所有研究案例中表现最佳。一般来说，功率谱估计、最小二乘三次样条和广义交叉验证技术产生了最可接受的结果。
