Pathological Gait Detection Based on Multiple Regression Models Using Unobtrusive Sensing Technology

Journal of Signal Processing Systems - Analysis of human gait to detect walking abnormality has recently gained growing interest. It carries profound impact in medical diagnosis and rehabilitation...     

Robust asymptotic tests for the equality of multivariate coefficients of variation

In order to easily compare several populations on the basis of more than one feature, multivariate coefficients of variation (MCV) may be used as they allow to summarize relative dispersion in a single index. However, up to date, no test of equality of one or more MCVs has been developed in the literature. In this paper, several classical and robust Wald-type tests are proposed and studied. The asymptotic distributions of the test statistics are derived under elliptical symmetry, and the asymptotic efficiency of the robust versions is compared to the classical tests. Robustness of the proposed procedures is examined through partial and joint influence functions of the test statistic, as well as by means of power and level influence functions. A simulation study compares the performance of the classical and robust tests under uncontaminated and contaminated schemes, and the difference with the usual covariance homogeneity test is highlighted. As a by-product, these tests may also be considered in the univariate context where they yield procedures that are both robust and easy-to-use. They provide an interesting alternative to the numerous parametric tests existing in the literature, which are, in most cases, unreliable in presence of outliers. The methods are illustrated on a real data set.     

Comparison of local outlier detection techniques in spatial multivariate data

Outlier detection techniques in spatial data should allow to identify two types of outliers: global and local ones. Local outliers typically have non-spatial attributes that strongly differ from those observed on their neighbors. Detecting local outliers requires to be able to work locally, on neighborhoods, in order to take into account the spatial dependence between the statistical units under consideration, even though the outlyingness is usually measured on the non-spatial variables. Many procedures have been outlined in the literature, but their number reduces when one wants to deal with multivariate non-spatial attributes. In this paper, focus is on the multivariate context. A review of existing procedures is done. A new approach, based on a two-step improvement of an existing one, is also designed and compared with the benchmarked methods by means of examples and simulations.