時系列データにおける規則性の定量化 : 運動解析へのApproximate Entropyの適用

概要

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Approximate Entropy (Ap entropy) was developed as a method to quantify regularity of time-series data. The aim of this study was to examine problems in the application of Ap entropy to human movement data and to distinguish movement patterns by quantifying the regularity of experimental human movement data. The following results were obtained: 1) For a relatively periodic time-series function with a small number of periods，difference in both the number of data points and number of periods affected the Ap entropy value. Thus，for the application of Ap entropy，the number of data points and the number of periods should be made the same. 2) Under the experimental conditions of this study，the change in the Ap entropy value was in accordance with subjective judgment of movement patterns. This indicates that Ap entropy is an effective parameter for quantitatively distinguishing movement patterns in data that differ in time-series regularity.
日本バイオメカニクス学会の論文