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Sensors 2015, 15(3), 6419-6440; doi:10.3390/s150306419

Stride Segmentation during Free Walk Movements Using Multi-Dimensional Subsequence Dynamic Time Warping on Inertial Sensor Data

1
ASTRUM IT GmbH, Am Wolfsmantel 2, Erlangen D-91058, Germany
2
Digital Sports Group, Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Martensstraße 3, Erlangen D-91058, Germany
3
Department of Molecular Neurology, Universitätsklinikum Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, Erlangen D-91054, Germany
4
Geriatrics Centre Erlangen, Waldkrankenhaus St. Marien, Rathsberger Straße 57, Erlangen D-91054, Germany
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Oliver Amft
Received: 26 October 2014 / Revised: 28 February 2015 / Accepted: 4 March 2015 / Published: 17 March 2015
(This article belongs to the Special Issue Sensor Systems for Motion Capture and Interpretation)
View Full-Text   |   Download PDF [1620 KB, uploaded 17 March 2015]   |  

Abstract

Changes in gait patterns provide important information about individuals’ health. To perform sensor based gait analysis, it is crucial to develop methodologies to automatically segment single strides from continuous movement sequences. In this study we developed an algorithm based on time-invariant template matching to isolate strides from inertial sensor signals. Shoe-mounted gyroscopes and accelerometers were used to record gait data from 40 elderly controls, 15 patients with Parkinson’s disease and 15 geriatric patients. Each stride was manually labeled from a straight 40 m walk test and from a video monitored free walk sequence. A multi-dimensional subsequence Dynamic Time Warping (msDTW) approach was used to search for patterns matching a pre-defined stride template constructed from 25 elderly controls. F-measure of 98% (recall 98%, precision 98%) for 40 m walk tests and of 97% (recall 97%, precision 97%) for free walk tests were obtained for the three groups. Compared to conventional peak detection methods up to 15% F-measure improvement was shown. The msDTW proved to be robust for segmenting strides from both standardized gait tests and free walks. This approach may serve as a platform for individualized stride segmentation during activities of daily living. View Full-Text
Keywords: inertial sensors; stride segmentation; accelerometer; gyroscope; dynamic time warping; free walk; gait analysis; Parkinson’s disease; geriatric patients; movement impairments inertial sensors; stride segmentation; accelerometer; gyroscope; dynamic time warping; free walk; gait analysis; Parkinson’s disease; geriatric patients; movement impairments
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Barth, J.; Oberndorfer, C.; Pasluosta, C.; Schülein, S.; Gassner, H.; Reinfelder, S.; Kugler, P.; Schuldhaus, D.; Winkler, J.; Klucken, J.; Eskofier, B.M. Stride Segmentation during Free Walk Movements Using Multi-Dimensional Subsequence Dynamic Time Warping on Inertial Sensor Data. Sensors 2015, 15, 6419-6440.

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