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Article Dans Une Revue Scientific Reports Année : 2023

Smoothing method for unit quaternion time series in a classification problem: an application to motion data

Elena Ballante
  • Fonction : Auteur
Pierre Drouin
  • Fonction : Auteur
Silvia Figini
  • Fonction : Auteur

Résumé

Abstract Smoothing orientation data is a fundamental task in different fields of research. Different methods of smoothing time series in quaternion algebras have been described in the literature, but their application is still an open point. This paper develops a smoothing approach for smoothing quaternion time series to obtain good performance in classification problems. Starting from an existing method which involves an angular velocity transformation of unit quaternion time series, a new method which employ the logarithm function to transform the quaternion time series to a real three-dimensional time series is proposed. Empirical evidences achieved on real data set and artificially noisy data sets confirm the effectiveness of the proposed method compared with the classical approach based on angular velocity transformation. The R functions developed for this paper will be provided in a Github repository.

Dates et versions

hal-04169578 , version 1 (24-07-2023)

Identifiants

Citer

Elena Ballante, Lise Bellanger, Pierre Drouin, Silvia Figini, Aymeric Stamm. Smoothing method for unit quaternion time series in a classification problem: an application to motion data. Scientific Reports, 2023, 13 (1), pp.9366. ⟨10.1038/s41598-023-36480-y⟩. ⟨hal-04169578⟩
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