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We propose a non-parametric statistical procedure for detecting multiple change-points in multidimensional signals. The method is based on a test statistic that generalizes the well-known Kruskal-Wallis procedure to the multivariate setting. The proposed approach does not require any knowledge about the distribution of the observations and is parameter-free. It is computationally efficient thanks...
We introduce a novel statistical test for unsupervised detection of changepoints in multidimensional sequences of temporal observations. The test statistic is based on a multivariate generalization of the Mann-Whitney Wilcoxon two-sample test. The proposed test performs nonparametric changepoint localization and returns a quantifiable measure of significance in the form of a p-value. This approach...
This paper describes a method for segmentation of triaxial accelerometer signals recorded during continuous treadmill walking. More specifically, we aim at detecting changes in speed and in incline by analyzing the accelerometer signals recorded on the shin or the waist of the walker. The raw accelerometer signals are transformed either in the time-frequency domain (with a Short-Time Fourier Transform)...
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