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
Affiliations
Jens Barth
ASTRUM IT GmbH, Am Wolfsmantel 2, Erlangen D-91058, Germany
Cäcilia Oberndorfer
ASTRUM IT GmbH, Am Wolfsmantel 2, Erlangen D-91058, Germany
Cristian Pasluosta
Digital Sports Group, Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Martensstraße 3, Erlangen D-91058, Germany
Samuel Schülein
Geriatrics Centre Erlangen, Waldkrankenhaus St. Marien, Rathsberger Straße 57, Erlangen D-91054, Germany
Heiko Gassner
Department of Molecular Neurology, Universitätsklinikum Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, Erlangen D-91054, Germany
Samuel Reinfelder
Digital Sports Group, Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Martensstraße 3, Erlangen D-91058, Germany
Patrick Kugler
Digital Sports Group, Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Martensstraße 3, Erlangen D-91058, Germany
Dominik Schuldhaus
Digital Sports Group, Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Martensstraße 3, Erlangen D-91058, Germany
Jürgen Winkler
Department of Molecular Neurology, Universitätsklinikum Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, Erlangen D-91054, Germany
Jochen Klucken
Department of Molecular Neurology, Universitätsklinikum Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, Erlangen D-91054, Germany
Björn M. Eskofier
Digital Sports Group, Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Martensstraße 3, Erlangen D-91058, Germany
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.