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In this paper we introduce a generalization of Fisher-Rao's discriminant analysis and its application in a human-computer interaction scenario: a sensing chair. Our algorithm shows to be able to successfully estimate the underlying distributions of the pressure maps data of the sensing chair. Other linear discriminant techniques, such as LDA, had been found to be inadequate for the job; typically yielding inferior results than PCA. We compare our approach to several template-based approaches and show that the new discriminant function is comparable to the best approach classifier. This is important because generally each application tends to prefer a different algorithm. Fortunately, our new algorithm is usually the top one (or comparable to the top one). In this paper we will however restrict our study tothe classification of sitting postures.