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... 'distance' dimension can take two values ('small' or 'large') depending on the amount of cognitive effort it takes to manipulate and evaluate the system. The combination of these two dimensions produces four types of systems (see Table 1). This space does not take into account the possible use of several devices or media of interaction. ...
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Increasingly natural, sensed, and touch-based input is being integrated into devices. Along the way, both custom and more general solutions have been developed for dealing with the uncertainty that is associated with these forms of input. However, it is difficult to provide dynamic, flexible, and continuous feedback about uncertainty using traditional interactive infrastructure. Our contribution is a general architecture with the goal of providing support for continual feedback about uncertainty. Our architecture is based on prior work in modeling uncertainty using Monte Carlo sampling, and tracks multiple interfaces -- one for each plausible and differentiable sequence of input that the user may have intended. Importantly, it considers how the presentation of uncertainty can be organized and implemented in a general way. Our primary contribution is a method for reducing the number of alternative interfaces and fusing possible interfaces into a single interface that both communicates uncertainty and allows for disambiguation. We demonstrate the value of this result through a collection of 11 new and existing feedback techniques along with two applications demonstrating the use of the feedback architecture.
Recent approaches to providing users with a more natural method of interacting with virtual environment applications have
shown that more than one mode of input can be both beneficial and intuitive as a communication medium between humans and computer
applications. Hand gestures and speech appear to be two of the most logical since users will typically be in environments
that will have them immersed in a virtual world with limited access to traditional input devices such as the keyboard or the
mouse. In this paper, we describe an ongoing research project to develop multimodal interfaces that incorporate 3D hand gestures
and speech in virtual environments.
Combining audio and image processing for understanding video content has several benefits when compared to using each modality
on their own. For the task of context and activity recognition in video sequences, it is important to explore both data streams
to gather relevant information. In this paper we describe a video context and activity recognition model. Our work extracts
a range of audio and visual features, followed by feature reduction and information fusion. We show that combining audio with
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Fusion engines are fundamental components of multimodal inter-active systems, to interpret input streams whose meaning can vary according to the context, task, user and time. Other surveys have considered multimodal interactive systems; we focus more closely on the design, specification, construction and evaluation of fusion engines. We first introduce some terminology and set out the major challenges that fusion engines propose to solve. A history of past work in the field of fusion engines is then presented using the BRETAM model. These approaches to fusion are then classified. The classification considers the types of application, the fusion principles and the temporal aspects. Finally, the challenges for future work in the field of fusion engines are set out. These include software frameworks, quantitative evaluation, machine learning and adaptation.
