Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/48800
Title: Clustering of Human Gait with Parkinson's Disease by Using Dynamic Time Warping
Authors: Steinmetzer, Tobias
Bonninger, Ingrid
Priwitzer, Barbara
Reinhardt, Fritjof
Reckhardt, Markus Christoph
Erk, Dorela
Travieso, Carlos M. 
UNESCO Clasification: 3314 Tecnología médica
Keywords: DTW
Parkinson disease
clustering
time series
Issue Date: 2018
Journal: 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings
Abstract: 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.
URI: http://hdl.handle.net/10553/48800
ISBN: 9781538675069
DOI: 10.1109/IWOBI.2018.8464203
Source: 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings (8464203)
Appears in Collections:Actas de congresos
Show full item record

SCOPUSTM   
Citations

5
checked on Feb 28, 2021

Page view(s)

8
checked on Mar 1, 2021

Google ScholarTM

Check

Altmetric


Share



Export metadata



Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.