A SURROGATE METHOD FOR DISCRETE MOVEMENT DATA

Abstract

Sample entropy can be an effective tool for the investigation of human movement variability. However, before applying the method, it can be beneficial to employ an analysis to confirm that observed data is not solely the result of stochastic processes. This can be achieved using surrogate methods. Previous investigations have used surrogate methods within human gait data, yet no appropriate method has been applied to discrete human movement. This article proposes a surrogate method for discrete movement data. The technique reliably generated surrogates for discrete joint angle time series, effectively destroying fine-scale dynamics of the observed signal and maintaining macro structural characteristics (e.g., Mean, SD). Comparison of entropy estimates indicated that observed signals contained deterministic dynamics.