This technical report provides a brief overview of how human hand gestures can be used in wearable Human Computer Interfaces (HCI).

Recent approaches to providing users with a more natural method of interacting with computer applications have shown that more than one mode of input can be both beneficial and intuitive as a communication medium between humans and computers. Two modalities in particular, whole-hand and speech input, represent a natural form of communication that has been ingrained in our physical and mental makeup since birth. In this thesis, we investigate the use of whole-hand and speech input in virtual environments in the context of two applications domains: scientific visualization and interior design. By examining the two modalities individually and in combination, and through the creation of two application prototypes (Multimodal Scientific Visualization Tool and Room Designer), we present anumber of contributions including a set of interface guidelines and interaction techniques for whole-hand and speech input.

This paper presents a portable system and method for recognizing the 26 hand shapes of the American Sign Language alphabet, using a novel glove-like device. Two additional signs, 'space', and 'enter ' are added to the alphabet to allow the user to form words or phrases and send them to a speech synthesizer. Since the hand shape for a letter varies from one signer to another, this is a 28class pattern recognition system. A three-level hierarchical classifier divides the problem into "dispatchers " and "recognizers. " After reducing pattern dimension from ten to three, the projection of class distributions onto horizontal planes makes it possible to apply simple linear discrimination in 2D, and Bayes ' Rule in those cases where classes had features with overlapped distributions. Twenty-one out of 26 letters were recognized with 100 % accuracy; the worst case, letter U, achieved 78%. 1.

Abstract. We present a gesture recognition method based on deformable shapes and curvature templates. Gestures are modeled using a spline representation that is enhanced with elastic properties: a gesture trajectory as a whole or any of its parts may stretch or bend. We regard such an approach as well-suited for dealing with the inherent variability of human gesture execution. The results of our gesture classifier are demonstrated with a video-based acquisition approach. Key words: gesture recognition, elastic matching, deformation energies 1

Abstract. This paper describes the preliminary results of the research work currently ongoing at our department and carried out as part of a project founded by the Commission of the European Union. In this paper a novel approach tohuman posture analysis and recognition using standard image processing techniques as well as hybrid neural information processing is presented. We rst develop a reliable and robust person localization module via a combination of oriented lters and threedimensional dynamic neural elds. Then we focus on the view-based recognition of the user's static gestural instructions from a prede ned vocabulary based on both a skin color model and statistical normalized moment invariants. The segmentation of the postures occurs by means of the skin color model based on the Mahalanobis metric. From the resulting binary image containing only regions which have been classi ed as skin candidates we extract translation and scale invariant moments. They are used as input for two di erent neural classi ers whose results are then compared. To train and test the neural classi ers we gathered the data from ve people performing 18 repetitions of eachof ve postures (our vocabulary): stop, go left, go right, hello left and hello right. The system is currently under development with constant updates and new developments. It uses input from a color video camera and is user-independent. The aim is to build a real-time system able to deal with dynamic gestures. 1.

Wearable computing and Augmented Reality applications call for less obtrusive and more intuitive human computer interfaces than keyboards and mice. One way to realise such interfaces is using gestures, e.g., for pointing in order to replace the mouse. The less obtrusive way of gesture recognition is to use computer vision based methods. This paper presents a computer vision-based gesture interface that is part of an Augmented Reality system. It can recognise a 3D pointing gesture, a click gesture, and five static gestures. A lookup-table based colour segmentation and a fast gesture recognition method are presented that enable for 25Hz performance on a standard PC.

Activity recognition from on-body sensors is affected by sensor degradation, interconnections failures, and jitter in sensor placement and orientation. We investigate how this may be balanced by exploiting redundant sensors distributed on the body. We recognize activities by a meta-classifier that fuses the information of simple classifiers operating on individual sensors. We investigate the robustness to faults and sensor scalability which follows from classifier fusion. We compare a reference majority voting and a naive Bayesian fusion scheme. We validate this approach by recognizing a set of 10 activities carried out by workers in the quality assurance checkpoint of a car assembly line. Results show that classification accuracy greatly increases with additional sensors (50% with 1 sensor, 80% and 98% with 3 and 57 sensors), and that sensor fusion implicitly allows to compensate for typical faults up to high fault rates. These results highlight the benefit of large on- body sensor network rather than a minimum set of sensors for activity recognition and prompts further investigation.

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The candidate confirms that the work submitted is her own and that appropriate credit has been given where reference has been made to the work of others. This copy is supplied on the understanding that it is copyright material and that no quotation from the thesis may be published without proper acknowledgementFor my father, David Jorge Schiffrin, Caminante, son tus huellas El camino y nada más; Caminante, no hay camino, Se hace camino al andar. Traveller, your footprints Are the road and nothing else; Traveller, there is no road, You make the road by walking. Al andar se hace camino Y al volver la vista atrás Se ve la senda que nunca