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Clustering of Human Gait with Parkinson's Disease by Using Dynamic Time Warping

机译:动态时间规整在人的步态与帕金森病之间的聚类

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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.
机译:我们提出了一种新的方法来检测步态障碍,根据其运动场使用传感器数据的聚类方法。 21名健康受试者和18名帕金森受试者进行了Time Up and Go测试。时间序列分为几个单独的步骤。为了进行分析,考虑了由移动传感器系统测量的水平加速度。我们使用动态时间规整和层次化聚类来区分体育场。达到92%的特异性。

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