Human movement recognition using fuzzy clustering and discriminant analysis

In this paper a novel method for human movement representation and recognition is proposed. A movement is regarded as a sequence of basic movement patterns, the so-called dynemes. Initially, the fuzzy c-mean (FCM) algorithm is used to identify the dynemes in the input space, and then principal component analysis plus linear discriminant analysis (PCA plus LDA) is employed to project the postures of a movement to the identified dynemes. In this space, the posture representations of the movement are combined to represent the movement in terms of its comprising dynemes. This representation allows for efficient Mahalanobis or cosine-based nearest centroid classification of variable length movements.