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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