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Clustering of Human Gait with Parkinson's Disease by Using Dynamic Time Warping

  • We present a new method for detecting gait disorders according to their stadium using cluster methods for sensor data. 21 healthy and 18 Parkinson subjects performed the Time Up and Go test. The time series were segmented into separate steps. For the analysis the horizontal acceleration measured by a mobile sensor system was considered. We used Dynamic Time Warping and Hierarchical Custering to distinguish the stadiums. A specificity of 92% was achieved.

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Metadaten
Author: Tobias Steinmetzer, Ingrid Bönninger, Barbara PriwitzerGND, Fritjof Reinhardt, Markus Christoph Reckhardt, Dorela Erk, Carlos M. Travieso-González
DOI:https://doi.org/10.1109/IWOBI.2018.8464203
Title of the source (English):IEEE International Work Conference on Bioinspired Intelligence (IWOBI)
Document Type:Scientific journal article not peer-reviewed
Language:English
Year of publication:2018
Tag:DTW; clustering; parkinson disease; time series
Volume/Year:2018
First Page:1
Last Page:6
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / Institut für Medizintechnologie
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