Eye Gaze in Intelligent User Interfaces: Gaze-based Analyses, Models and Applications
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Remarkable progress in eye-tracking technologies opened the way to design novel attention-based intelligent user interfaces, and highlighted the importance of better understanding of eye-gaze in human-computer interaction and human-human communication. For instance, a user’s focus of attention is useful in interpreting the user’s intentions, their understanding of the conversation, and their attitude towards the conversation. In human face-to-face communication, eye gaze plays an important role in floor management, grounding, and engagement in conversation.
Eye Gaze in Intelligent User Interfaces draws on ideas from a number of contributors working on how attentional information can be applied to novel intelligent interfaces. Part I focuses on analyzing human eye gaze behaviors to reveal characteristics of human communication and cognition; Part II addresses estimation and prediction of the cognitive state of the users using gaze information; and Part III presents proposals of novel gaze-aware interfaces which integrate eye-trackers as a system component. The contributions highlight a direction for the future of human-computer interaction, and discuss issues in human attentional behaviors and face-to-face communication which are essential in designing gaze aware interactive interfaces.
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Behavior is part of conversational interaction, and the robot’s gaze policy will have an impact on both the human’s gaze behavior and the impressions they form about the agent they are interacting with. Robotic systems designed to learn language through interaction by exploiting the structure of child-directed speech (e.g., work by Saunders et al. 2010) could especially benefit from a gaze model that supports social engagement. In order to support natural and effective gaze interaction between.
Word and by computing how well they match over multiple pronunciations of the same word, we should be able to decide whether the word is a reference or not and if it is, to which object it is directed. We developed a model from this principle and we tested its effectiveness to detect automatically from a raw transcribed dialogue the words that are verbal references and to which object they refer to. 5.2 Background The model developed in this contribution is largely based on the tight.
Vertical the window w actually is. Both serve as the input for the successive classification This approach uses overall saccade shape of a number of subsequent saccades (see Fig. 8.2). The algorithm’s principal inputs are the incoming stream of gaze data, as well as the average font size of the area below the observed saccades, as well as the information whether there was text present at all. If there is no text present, such as when the user’s gaze is directed on an image, we already know that.
Differentiate between doubt and interest. Either because the difference between them was too small, or because they found that the emotional state doubt was already included in interest. Interestingly, all male students suggested further to add anger or frustration to the emotion selection. Regarding the necessity of a neutral emotional state, two students claimed that neutrality did not exist while reading. According to some of our participants’ further comments, text should always be assigned.
The respective direction, while all other representations disappear. For example, if the pen is moved horizontally to the right (R) and the key is not pressed (U), the object representation in upper right direction is moved to the upper right, while all other objects are faded out (Fig. 9.3b, right). In order to move the object from its initial position to the (green) target area T0 at the top of the display along the path illustrated in Fig. 9.2c, for both techniques users first would have to.