|Title:||Clustering of Human Gait with Parkinson's Disease by Using Dynamic Time Warping||Authors:||Steinmetzer, Tobias
Reckhardt, Markus Christoph
Travieso, Carlos M.
|UNESCO Clasification:||3314 Tecnología médica||Keywords:||DTW
|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|
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