Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/48800
Título: Clustering of Human Gait with Parkinson's Disease by Using Dynamic Time Warping
Autores/as: Steinmetzer, Tobias
Bonninger, Ingrid
Priwitzer, Barbara
Reinhardt, Fritjof
Reckhardt, Markus Christoph
Erk, Dorela
Travieso, Carlos M. 
Clasificación UNESCO: 3314 Tecnología médica
Palabras clave: DTW
Parkinson disease
clustering
time series
Fecha de publicación: 2018
Publicación seriada: 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings
Resumen: 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
Fuente: 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings (8464203)
Colección:Actas de congresos
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