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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
Name:Priwitzer, Barbara
DOI:https://doi.org/10.1109/IWOBI.2018.8464203
ISBN:978-1-5386-7506-9
Erschienen in:2018 IEEE International Work Conference on Bioinspired Intelligence : proceedings : July 18-20, 2018, Instituto Tecnológico de Costa Rica, San Carlos, Costa Rica
Publisher:IEEE
Place of publication:Piscataway, NJ
Document Type:Conference Proceeding
Language:English
Tag:DTW; clustering; parkinson disease; time series
Pagenumber:6
Dewey Decimal Classification:006 Spezielle Computerverfahren
Open Access:Nein
Licence (German):License Logo  Lizenzbedingungen IEEE