KOPS - Das Institutionelle Repositorium der Universität Konstanz

A novel approach to quantify time series differences of gait data using attractor attributes

A novel approach to quantify time series differences of gait data using attractor attributes

Zitieren

Dateien zu dieser Ressource

Prüfsumme: MD5:1a4d67c3a0f8c2ead823555fb73aa524

VIETEN, Manfred, Aida SEHLE, Randall JENSEN, 2013. A novel approach to quantify time series differences of gait data using attractor attributes. In: PLoS ONE. 8(8), e71824. eISSN 1932-6203

@article{Vieten2013novel-24323, title={A novel approach to quantify time series differences of gait data using attractor attributes}, year={2013}, doi={10.1371/journal.pone.0071824}, number={8}, volume={8}, journal={PLoS ONE}, author={Vieten, Manfred and Sehle, Aida and Jensen, Randall}, note={Article Number: e71824} }

2013-08-23T12:54:09Z eng 2013 Jensen, Randall Vieten, Manfred Sehle, Aida 2013-08-23T12:54:09Z PLoS ONE ; 8 (2013), 8. - e71824 deposit-license Vieten, Manfred Sehle, Aida A novel approach to quantify time series differences of gait data using attractor attributes Jensen, Randall In this paper we introduce a new method to expressly use live/corporeal data in quantifying differences of time series data with an underlying limit cycle attractor; and apply it using an example of gait data. Our intention is to identify gait pattern differences between diverse situations and classify them on group and individual subject levels. First we approximated the limit cycle attractors, from which three measures were calculated: δM amounts to the difference between two attractors (a measure for the differences of two movements), δD computes the difference between the two associated deviations of the state vector away from the attractor (a measure for the change in movement variation), and δF, a combination of the previous two, is an index of the change. As an application we quantified these measures for walking on a treadmill under three different conditions: normal walking, dual task walking, and walking with additional weights at the ankle. The new method was able to successfully differentiate between the three walking conditions. Day to day repeatability, studied with repeated trials approximately one week apart, indicated excellent reliability for δM (ICCave > 0.73 with no differences across days; p > 0.05) and good reliability for δD (ICCave = 0.414 to 0.610 with no differences across days; p > 0.05). Based on the ability to detect differences in varying gait conditions and the good repeatability of the measures across days, the new method is recommended as an alternative to expensive and time consuming techniques of gait classification assessment. In particular, the new method is an easy to use diagnostic tool to quantify clinical changes in neurological patients.

Dateiabrufe seit 01.10.2014 (Informationen über die Zugriffsstatistik)

Vieten_243236.pdf 107

Das Dokument erscheint in:

KOPS Suche


Stöbern

Mein Benutzerkonto