A comparison of automatic filtering techniques applied to biomechanical walking data

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.