Renata Cristina Barros Madeo

Hand movement recognition for Brazilian Sign Language: A study using distance-based neural networks

In this paper, the vision-based hand movement recognition problem is formulated for the universe of discourse of the Brazilian Sign Language. In order to analyze this specific domain we have used the artificial neural networks models based on distance, including neural-fuzzy models. The experiments explored here show the usefulness of these models to extract helpful knowledge about the classes of movements and to support the project of adaptative recognizer modules for Libras-oriented computational tools. Using artificial neural networks architectures - Self Organizing Maps and (Fuzzy) Learning Vector Quantization, it was possible to understand the data space and to build models able to recognize hand movements performed for one or more than one specific Libras users.

Classification of electromyography signals using relevance vector machines and fractal dimension

Abstract Surface electromyography (EMG) signals have been studied extensively in the last years aiming at the automatic classification of hand gestures and movements as well as the early identification of latent neuromuscular disorders. In this paper, we investigate the potentials of the conjoint use of relevance vector machines (RVM) and fractal dimension (FD) for automatically identifying EMG signals related to different classes of limb motion. The adoption of FD as the mechanism for feature extraction is justified by the fact that EMG signals usually show traces of self-similarity. In particular, four well-known FD estimation methods, namely box-counting, Higuchi’s, Katz’s and Sevcik’s methods, have been considered in this study. With respect to RVM, besides the standard formulation for binary classification, we also investigate the performance of two recently proposed variants, namely constructive mRVM and top-down mRVM, that deal specifically with multiclass problems. These classifiers operate solely over the features extracted by the FD estimation methods, and since the number of such features is relatively small, the efficiency of the classifier induction process is ensured. Results of experiments conducted on a publicly available dataset involving seven distinct types of limb motions are reported whereby we assess the performance of different configurations of the proposed RVM+FD approach. Overall, the results evidence that kernel machines equipped with the FD feature values can be useful for achieving good levels of classification performance. In particular, we have empirically observed that the features extracted by the Katz’s method is of better quality than the features generated by other methods.

Gesture recognition for fingerspelling applications: an approach based on sign language cheremes

This paper presents an approach for carrying out gesture recognition for the Brazilian Sign Language Manual Alphabet. The gestural patterns are treated as a combination of three primitives, or cheremes - hand configuration, hand orientation and hand movement. The recognizer is built in a modular architecture composed by inductive reasoning modules, which use the artificial neural network Fuzzy Learning Vector Quantization; and rule-based modules. This architecture has been tested and results are presented here. Some strengths of such approach are: robustness of recognition, portability to similar contexts, extensibility of the dataset to be recognize and reduction of the vocabulary recognition problem to the recognition of its primitives.

Tutorial sobre Fuzzy-c-Means e Fuzzy Learning Vector Quantization: Abordagens Híbridas para Tarefas de Agrupamento e Classificação

Neste tutorial e apresentada uma discussao sobre o algoritmo Fuzzy-c-Means e sobre as Redes Neurais Fuzzy, considerando a proposta de insercao de principios da Teoria de Conjuntos Fuzzynas abordagens de agrupamento e classificacao classicas: algoritmo c-Means e o modelo neural Learning Vector Quantization. A motivacao para a construcao de um modelo hibrido, dessa categoria, e conferir as abordagens classicas a capacidade de lidar adequadamente com aspectos de incerteza e imprecisao, comumente encontrados em problemas reais.

Brazilian sign language multimedia hangman game: a prototype of an educational and inclusive application

This paper presents a prototype of an educative and inclusive application: the Brazilian Sign Language Multimedia Hangman Game. This application aims to estimulate people, specially children, deaf or not, to learn a sign language and to help deaf people to improve their vocabulary in an oral language. The differential of this game is that its input consists of videos of the user performing signs from Brazilian Sign Language corresponding to Latin alphabet letters, recorded through the game graphical interface. These videos are processed by a computer vision module in order to recognize the letter to which the sign corresponds, using a recognition strategy based on primitives - hand configuration, movement and orientation, reaching 84.3% accuracy.

Hybrid architecture for gesture recognition: Integrating fuzzy-connectionist and heuristic classifiers using fuzzy syntactical strategy

This paper describes a hybrid architecture that provides automatic classification for a set of gestures. Such architecture combines fuzzy-connectionist, heuristic and syntactical pattern recognition approaches, and deals with gesture recognition based on primitives. The modeling with primitives allows the use of multiples classifiers in order to achieve high classification accuracy. The heuristic classifier and the fuzzy syntactical integrating strategy are described in this paper. The fuzzy-connectionist classifiers were discussed in previous works and they are now revisited just to present the set of parameters that solves the current proof of concept, in the scope of Brazilian Sign Language Manual Alphabet. The fuzzy syntactical strategy coupled with the modeling with primitives has improved the pattern recognition results, enabling the design of architecture for classification with high flexibility and scalability to development of applications in different signed communication contexts. The experimental results show that the proposed approach is valid and has promising application.

Studies in automated hand gesture analysis: an overview of functional types and gesture phases

Abstract This paper presents an overview of studies on automated hand gesture analysis, which is mainly concerned with recognition and segmentation issues related to functional types and gesture phases. The issues selected for discussion have been arranged in a way that takes account of problems within the Theory of Gestures that each study seeks to address. Their principal computational factors that were involved in conducting the analysis of automated hand gesture have been examined, and an analysis of open research issues has been carried out for each application dealt with in the studies.

A Review on Temporal Reasoning Using Support Vector Machines

Recently, Support Vector Machines have presented promissing results to various machine learning tasks, such as classification and regression. These good results have motivated its application to several complex problems, including temporal information analysis. In this context, some studies attempt to extract temporal features from data and submit these features in a vector representation to traditional Support Vector Machines. However, Support Vector Machines and its traditional variations do not consider temporal dependency among data. Thus, some approaches adapt Support Vector Machines internal mechanism in order to integrate some processing of temporal characteristics, attempting to make them able to interpret the temporal information inherent on data. This paper presents a review on studies covering this last approach for dealing with temporal information: incorporating temporal reasoning into Support Vector Machines and its variations.

PAPEL ESTRATÉGICO E IMPACTO DOS SISTEMAS DE INFORMAÇÃO NO MERCADO DE AÇÕES: UM ESTUDO ENVOLVENDO BRASIL E ESTADOS UNIDOS

This paper presents an overview on the strategic role of information systems in the stock market under a historical perspective, aiming to discuss their ethical, social and political impacts on society, focusing on the stock markets of Brazil and the USA. It is possible to classify systems according to their strategic goals: there are systems aiming at ensuring the organization’s survival or aiming at providing competitive advantage. Based on that classification, the strategic goals, ethical, social and political impacts of each kind of system are analyzed. We conclude that, in the case of stock markets, systems aiming at ensuring the organization’s survival have brought great benefits to society, although there were some negative ethical impacts, since these systems have caused the dismissal of a huge number of employees in the Brazilian case. Systems aiming to provide competitive advantage have brought some benefits related to increased liquidity in markets. However, these systems raise several ethical, social and political issues, which need to be better understood and dealt with by organizations that operate in the stock market.