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Journal of Sensors
Volume 2017, Article ID 1260734, 8 pages
Research Article

Detection of Freezing of Gait Using Template-Matching-Based Approaches

1School of Computer and Communication Engineering, University of Science and Technology, Beijing, China
2Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, China

Correspondence should be addressed to Jie He; nc.ude.btsu@eijeh

Received 21 July 2017; Accepted 2 October 2017; Published 6 November 2017

Academic Editor: Mucheol Kim

Copyright © 2017 Cheng Xu et al. 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.


Every year, injuries associated with fall incidences cause lots of human suffering and assets loss for Parkinson’s disease (PD) patients. Thereinto, freezing of gait (FOG), which is one of the most common symptoms of PD, is quite responsible for most incidents. Although lots of researches have been done on characterized analysis and detection methods of FOG, large room for improvement still exists in the high accuracy and high efficiency examination of FOG. In view of the above requirements, this paper presents a template-matching-based improved subsequence Dynamic Time Warping (IsDTW) method, and experimental tests were carried out on typical open source datasets. Results show that, compared with traditional template-matching and statistical learning methods, proposed IsDTW not only embodies higher experimental accuracy (92%) but also has a significant runtime efficiency. By contrast, IsDTW is far more available in real-time practice applications.