Application of singular spectrum analysis to the smoothing of raw kinematic signals

Abstract 

Motion capture systems currently used in biomechanical analysis introduce systematic measurement errors that appear in the form of noise in recorded displacement signals. The noise is unacceptably amplified when differentiating displacements to obtain velocities and accelerations. To avoid this phenomenon, it is necessary to smooth the displacement signal prior to differentiation in order to eliminate the noise introduced by the experimental system. The use of singular spectrum analysis (SSA) is presented in this paper as an alternative to traditional digital filtering methods. SSA is a novel non-parametric technique based on principles of multivariate statistics. The original time series is decomposed into a number of additive time series, each of which can be easily identified as being part of the modulated signal, or as being part of the random noise. Several examples that demonstrate the superiority of this technique over other methods used in biomechanical analysis are presented in this paper.

Keywords:  Singular spectrum analysis, Signal differentiation, Smoothing