Automatic filtering techniques in biomechanics: a comparison using walking data

The purpose of this study was to compare and evaluate six automatic filtering techniques commonly used in biomechanics. Sixty levels of noise were generated and superimposed on 24 original signals, creating 1440 signals in which 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. The techniques of power spectrum estimation, least squares cubic splines and generalised cross validation produced the most acceptable results.