A comparison of smoothing and filtering approaches using simulated kinematic data of human movements
Deutscher übersetzter Titel: | Ein Vergleich von Glättungs- und Filterungsmethoden unter Verwendung simulierter kinematischer Daten menschlicher Bewegungen |
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Autor: | Gulde, Philipp; Hermsdörfer, Joachim |
Erschienen in: | Proceedings of the 11th International Symposium on Computer Science in Sport (IACSS 2017) |
Veröffentlicht: | Cham: Springer International Publishing (Verlag), 2018, S. 97-102, Lit. |
Beteiligte Körperschaft: | International Symposium of Computer Science in Sport |
Format: | Literatur (SPOLIT) |
Publikationstyp: | Sammelwerksbeitrag |
Medienart: | Elektronische Ressource (online) Gedruckte Ressource |
Dokumententyp: | Tagungsband |
Sprache: | Englisch |
DOI: | 10.1007/978-3-319-67846-7_10 |
Schlagworte: | |
Online Zugang: | |
Erfassungsnummer: | PU201807004649 |
Quelle: | BISp |
Abstract des Autors
Gathered kinematic data usually requires post-processing in order to handle noise. There a three different approaches frequently used: local regression & moving average algorithms, and Butterworth filters. In order to examine the most appropriate post-processing approach and its optimal settings to human upper limb movements, we examined how far the approaches were able to reproduce a simulated movement signal with overlaid noise. We overlaid a simulated movement signal (movement amplitude 80 cm) with normal distributed noise (standard deviation of 0.5 cm). The resulting signal was post-processed with local regression and moving average algorithms as well as Butterworth filters with different settings (spans/orders). The deviation from the original simulated signal in four kinematic parameters (path length, maximum velocity, relative activity, and spectral arc length) was calculated and checked for a minimum. The unprocessed noisy signal showed absolute mean deviations of 54.78% ± 12.16% in the four kinematic parameters. The local regression algorithm revealed the best performance at a span of 420 ms with an absolute mean deviation of 2.00% ± 0.86%. For spans between 280–690 ms the local regression algorithm still revealed deviations below 5%. Based on our results we suggest a local regression algorithm with a span of 420 ms for smoothing noisy kinematic data in upper limb performance, e.g., activities of daily living. This suggestion applies to kinematic data of human movements.