The eye gaze detection techniques can be classified on the basis of direct eye detection, appearance, template, shape, feature, motion, hybrid, regression, 3D methods etc. Investigation of eye gaze helps to understand various aspects of the user like attention, intention, desire and area of interest etc. The important advantage in using eye gaze systems is that the user can communicate from a distance, and there is no requirement of physical contact with the computer. Eye gaze technique is one of the very significant techniques of HCI and can be used as hands free pointing tool enabling hands-free operation of the display for the user. Human Computer Interaction (HCI) is an emerging technology. Our research sets the boundaries for more comfortable and easier interaction with notifications and discusses implications for target selections in AR while cycling. Participants favored the MAGIC Pointing approach, supporting cyclists in AR selection tasks. Our results show significantly faster response times for MAGIC Pointing compared to Dwell Time and Gestures, while Dwell Time led to a significantly lower error rate compared to Gestures. We assessed the efficiency regarding reaction times, error rates, and perceived task load. In a user study (N=18), participants confirmed notifications in Augmented Reality (AR) using the three interaction modalities in a simulated biking scenario. To address this issue, we evaluate three notification interaction modalities and investigate their impact on the interaction performance while cycling: gaze-based Dwell Time, Gestures, and Manual And Gaze Input Cascaded (MAGIC) Pointing. Participants preferred selecting by borders, which allowed them faster selections than the dwell time method.Ĭyclists' attention is often compromised when interacting with notifications in traffic, hence increasing the likelihood of road accidents. Data showed large advantages of the new entry methods over single character text entry in speed and accuracy. In a longitudinal study we compared participants performance during character-by-character text entry with bigram entry and with text entry with bigrams derived by word prediction. Moreover, we combined dwell time selection with selection by borders, providing an alternative selection method and extra functionality. Therefore, we introduced three different bigram building strategies. We adopted a typing interface based on hierarchical pie menus, pEYEwrite and included bigram text entry with one single pie iteration. There are two reasons for the relatively slow text entry: dwell time selection requires waiting a certain time, and single character entry limits the maximum entry speed. Eye typing could provide motor disabled people a reliable method of communication given that the text entry speed of current interfaces can be increased to allow for fluent communication.
0 Comments
Leave a Reply. |