Autorizo a reprodução e divulgação total ou parcial deste trabalho, por qualquer meio convencional ou eletrônico, para fins de estudo e pesquisa, desde que citada a fonte. Gestures analysis systems have stood out for their contributions to the interaction between humans, humans and machines, and humans and environments. In this interaction, natural gesticulation is seen as part of a linguistic system that supports the communication, and all information systems aiming at the use of such an interaction in making decisions should be able to interpret it. Such an interpretation can be carried out through the gesture phases segmentation. In order to solve this task, the establishment of an efficient data representation for gestures is a critical issue. The chosen representation as well as its combination with techniques for analysis can or can not favor the solution being developed. In this work, different forms representation for gestures are applied to pattern recognition algorithms MLP and SOM to create an adequate environment to identify the more discriminative representations, which aspect the different representations describe with more efficiency, and how they can be combined in order to improve gesture phases segmentation. To construct the multidimensional representations we use spatial and temporal aspects combined with the normalization of the data and the application of the wavelet filter in the search for the most discriminating representation for the recognition of the gesture phases. Both algorithms achieved good results with the use of temporal aspects. MLP was able to classify all gesture phases using representation settings containing data about all monitored members. SOM presented good ability to form groups containing data of the same gesture phase even with the use of few characteristics in the construction of the representation, but it was not possible to identify the proposal of a new gesture phase with unsupervised learning.
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