Stride segmentation during free walk movements using multi-dimensional subsequence dynamic time warping on inertial sensor data

Jens Barth, Cäcilia Oberndorfer, Cristian Pasluosta, Samuel Schülein, Heiko Gassner, Samuel Reinfelder, Patrick Kugler, Dominik Schuldhaus, Jürgen Winkler, Jochen Klucken, Björn M. Eskofier*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

131 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)6419-6440
Number of pages22
JournalSensors
Volume15
Issue number3
DOIs
Publication statusPublished - 17 Mar 2015
Externally publishedYes

Keywords

  • Accelerometer
  • Dynamic time warping
  • Free walk
  • Gait analysis
  • Geriatric patients
  • Gyroscope
  • Inertial sensors
  • Movement impairments
  • Parkinson’s disease
  • Stride segmentation

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