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Content Provider | IEEE Xplore Digital Library |
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Author | Barth, Jens Klucken, Jochen Kugler, Patrick Kammerer, Thomas Steidl, Ralph Winkler, Jurgen Hornegger, Joachim Eskofier, Bjorn |
Copyright Year | 2011 |
Description | Author affiliation: Pattern Recognition Lab, Department of Computer Science, University of Erlangen-Nuremberg, Erlangen, Germany (Kugler, Patrick; Hornegger, Joachim; Eskofier, Bjorn) || ASTRUM IT GmbH, Erlangen, Germany (Kammerer, Thomas; Steidl, Ralph) || Department of Molecular Neurology, University Hospital of Erlangen, Erlangen, Germany (Barth, Jens; Klucken, Jochen; Winkler, Jurgen) |
Abstract | Parkinson's disease (PD) is the most frequent neurodegenerative movement disorder. Early diagnosis and effective therapy monitoring is an important prerequisite to treat patients and reduce health care costs. Objective and non-invasive assessment strategies are an urgent need in order to achieve this goal. In this study we apply a mobile, lightweight and easy applicable sensor based gait analysis system to measure gait patterns in PD and to distinguish mild and severe impairment of gait. Examinations of 16 healthy controls, 14 PD patients in an early stage, and 13 PD patients in an intermediate stage were included. Subjects performed standardized gait tests while wearing sport shoes equipped with inertial sensors (gyroscopes and accelerometers). Signals were recorded wirelessly, features were extracted, and distinct subpopulations classified using different classification algorithms. The presented system is able to classify patients and controls (for early diagnosis) with a sensitivity of 88% and a specificity of 86%. In addition it is possible to distinguish mild from severe gait impairment (for therapy monitoring) with 100% sensitivity and 100% specificity. This system may be able to objectively classify PD gait patterns providing important and complementary information for patients, caregivers and therapists. |
Starting Page | 868 |
Ending Page | 871 |
File Size | 670873 |
Page Count | 4 |
File Format | |
ISBN | 9781424441211 |
ISSN | 1557170X |
e-ISBN | 9781457715891 |
e-ISBN | 9781424441228 |
DOI | 10.1109/IEMBS.2011.6090226 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2011-08-30 |
Publisher Place | USA |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Feature extraction Monitoring Frequency measurement Accelerometers Parkinson's disease Frequency control |
Content Type | Text |
Resource Type | Article |
Subject | Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition |
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