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

Sensors

Jens Barth²  C์ilia Oberndorfer²  Cristian Pasluosta³  Samuel Schülein¹  Heiko Gassner⁴  Samuel Reinfelder³  Patrick Kugler³  Dominik Schuldhaus³  Jürgen Winkler⁴  Jochen Klucken⁴  Björn M. Eskofier³ 

[1] Geriatrics Centre Erlangen, Waldkrankenhaus St. Marien, Rathsberger Straße 57, Erlangen D-91054, Germany; E-Mail:;ASTRUM IT GmbH, Am Wolfsmantel 2, Erlangen D-91058, Germany; E-Mails:;Digital Sports Group, Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Martensstraße 3, Erlangen D-91058, Germany; E-Mails:;Department of Molecular Neurology, Universitätsklinikum Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, Erlangen D-91054, Germany; E-Mails:

关键词: inertial sensors;    stride segmentation;    accelerometer;    gyroscope;    dynamic time warping;    free walk;    gait analysis;    Parkinson’s disease;    geriatric patients;    movement impairments;   

DOI  :  10.3390/s150306419

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.