3,658 research outputs found
ICANDO: Intellectual Computer AssistaNt for Disabled Operators
Publication in the conference proceedings of EUSIPCO, Florence, Italy, 200
Interactive voice response system and eye-tracking interface in assistive technology for disabled
Abstract. The development of ICT has been very fast in the last few decades and it is important that everyone can benefit from this progress. It is essential for designing user interfaces to keep up on this progress and ensure the usability and accessibility of new innovations. The purpose of this academic literature review has been to study the basics of multimodal interaction, emphasizing on context with multimodal assistive technology for disabled people. From various modalities, interactive voice response and eye-tracking were chosen for analysis. The motivation for this work is to study how technology can be harnessed for assisting disabled people in daily life
Ambient Gestures
We present Ambient Gestures, a novel gesture-based system designed to support ubiquitous âin the environmentâ interactions with everyday computing technology. Hand gestures and audio feedback allow users to control computer applications without reliance on a graphical user interface, and without having to switch from the context of a non-computer task to the context of the computer. The Ambient Gestures system is composed of a vision recognition software application, a set of gestures to be processed by a scripting application and a navigation and selection application that is controlled by the gestures. This system allows us to explore gestures as the primary means of interaction within a multimodal, multimedia environment. In this paper we describe the Ambient Gestures system, define the gestures and the interactions that can be achieved in this environment and present a formative study of the system. We conclude with a discussion of our findings and future applications of Ambient Gestures in ubiquitous computing
Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction
Assistive robots can potentially improve the quality of life and personal
independence of elderly people by supporting everyday life activities. To
guarantee a safe and intuitive interaction between human and robot, human
intentions need to be recognized automatically. As humans communicate their
intentions multimodally, the use of multiple modalities for intention
recognition may not just increase the robustness against failure of individual
modalities but especially reduce the uncertainty about the intention to be
predicted. This is desirable as particularly in direct interaction between
robots and potentially vulnerable humans a minimal uncertainty about the
situation as well as knowledge about this actual uncertainty is necessary.
Thus, in contrast to existing methods, in this work a new approach for
multimodal intention recognition is introduced that focuses on uncertainty
reduction through classifier fusion. For the four considered modalities speech,
gestures, gaze directions and scene objects individual intention classifiers
are trained, all of which output a probability distribution over all possible
intentions. By combining these output distributions using the Bayesian method
Independent Opinion Pool the uncertainty about the intention to be recognized
can be decreased. The approach is evaluated in a collaborative human-robot
interaction task with a 7-DoF robot arm. The results show that fused
classifiers which combine multiple modalities outperform the respective
individual base classifiers with respect to increased accuracy, robustness, and
reduced uncertainty.Comment: Submitted to IROS 201
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