Interval Fuzzy Rule-Based Hand Gesture Recognition Benjam´ ın R. Callejas Bedregal Depto de Inform´ atica e Matem´ atica Aplicada Universidade Federal do Rio Grande do Norte Campus Universit´ ario, 59.072-970 Natal, Brazil bedregal@dimap.ufrn.br Grac ¸aliz P. Dimuro, Antˆ onio C. Rocha Costa Programa de P ´ os-Graduac ¸˜ ao em Inform´ atica Universidade Cat´ olica de Pelotas Felix da Cunha 412, 96010-000 Pelotas, Brazil {liz,rocha}@ucpel.tche.br Abstract Abstract. This paper introduces an interval fuzzy rule- based method for the recognition of hand gestures acquired from a data glove, with an application to the recognition of hand gestures of the Brazilian Sign Language. To deal with the uncertainties in the data provided by the data glove, an approach based on interval fuzzy logic is used. The method uses the set of angles of finger joints and of separation be- tween finger for the classification of hand configurations, and classifications of segments of hand gestures for rec- ognizing gestures. The segmentation of gestures is based on the concept of monotonic gesture segment, sequences of hand configurations in which the variations of the angles of the finger joints have the same sign (non-increasing or non-decreasing), separated by reference configurations that mark the inflexion points in the sequence. Each gesture is characterized by its list of monotonic segments. The set of all lists of segments of a given set of gestures determines a set of finite automata able to recognize such gestures. 1. Introduction Sign languages are the gestural languages used by deaf people in their daily face-to-face communication. Differ- ently to the problems found in the processing of oral lan- guages used by hearing people, the visual-gestural nature of sign languages gives rise to many specific problems for their automated recognition. There is an extensive literature about methods and sys- tems for gesture recognition in general, and hand gesture recognition in particular, such as, e.g.: systems for the recognition of 3-D and 2-D gestures captured by different devices (data gloves, cameras etc.) [4] or systems for the graphical recognition of traces left on tablet devices [20]; methods based on fuzzy logic, neural networks or hybrid neuro-fuzzy methods [5]; fuzzy rule [24], finite state ma- chine [13] or Hidden Markov Models [21] based methods. In particular, considering sign language recognition, some literature can be found related to fuzzy methods, such as, e.g, fuzzy decision trees [9] and neuro-fuzzy systems [1]. In this paper, we propose an interval fuzzy rule-based method for the recognition of hand gestures acquired from a data glove, extending the work presented in [2] to con- sider the uncertainties in the data provided by the glove. We apply the method to the recognition of hand gestures of LIBRAS, the Brazilian Sign Language [6]. The method uses the set of angles of finger joints and of the separation between fingers (given as intervals that en- close the uncertainties of the data obtained by the glove sensors) for the classification of hand configurations, and classifications of sequences of hand configurations for rec- ognizing gestures. The segmentation of gestures is based on the concept of monotonic gesture segment, sequences of gestures in which the variations of the angles of the finger joints have the same sign (non-increasing or non- decreasing), separated by reference hand configurations that mark the inflexion points in the sequence. Each gesture is characterized by a list of monotonic segments, which deter- mine a set of finite automata, which are able to recognize the gestures being considered. The paper is organized as follows. Section 2 presents a basic overview of fuzzy systems. Some concepts related to Interval Mathematics are discussed in Sect. 3. Our interval fuzzy rule-based method for hand gesture recognition is in- troduced in Sect. 4. The case study is presented in Sect. 5. The Conclusion is in Sect. 6. 2. Fuzzy Systems Fuzzy sets were introduced in 1965 by Zadeh [26] for representing vagueness in everyday life, providing an ap- proximate and effective means for describing the character- istics of a system that is too complex or ill-defined to be described by precise mathematical statements. In a fuzzy approach the relationship between elements and sets fol- lows a transition from membership to non membership that