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Sensors 2015, 15(6), 14679-14700; doi:10.3390/s150614679

Gaze-Assisted User Intention Prediction for Initial Delay Reduction in Web Video Access

1,2
,
1,2
and
1,2,*
1
School of Integrated Technology, Yonsei University, Incheon 406-840, Korea
2
Yonsei Institute of Convergence Technology, Yonsei University, Incheon 406-840, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Gianluca Paravati and Valentina Gatteschi
Received: 25 February 2015 / Revised: 13 June 2015 / Accepted: 16 June 2015 / Published: 19 June 2015
(This article belongs to the Special Issue HCI In Smart Environments)
View Full-Text   |   Download PDF [4195 KB, uploaded 19 June 2015]   |  

Abstract

Despite the remarkable improvement of hardware and network technology, the inevitable delay from a user’s command action to a system response is still one of the most crucial influence factors in user experiences (UXs). Especially for a web video service, an initial delay from click action to video start has significant influences on the quality of experience (QoE). The initial delay of a system can be minimized by preparing execution based on predicted user’s intention prior to actual command action. The introduction of the sequential and concurrent flow of resources in human cognition and behavior can significantly improve the accuracy and preparation time for intention prediction. This paper introduces a threaded interaction model and applies it to user intention prediction for initial delay reduction in web video access. The proposed technique consists of a candidate selection module, a decision module and a preparation module that prefetches and preloads the web video data before a user’s click action. The candidate selection module selects candidates in the web page using proximity calculation around a cursor. Meanwhile, the decision module computes the possibility of actual click action based on the cursor-gaze relationship. The preparation activates the prefetching for the selected candidates when the click possibility exceeds a certain limit in the decision module. Experimental results show a 92% hit-ratio, 0.5-s initial delay on average and 1.5-s worst initial delay, which is much less than a user’s tolerable limit in web video access, demonstrating significant improvement of accuracy and advance time in intention prediction by introducing the proposed threaded interaction model. View Full-Text
Keywords: gaze assisted; user intention prediction; threaded interaction model; initial delay reduction; web video prefetching gaze assisted; user intention prediction; threaded interaction model; initial delay reduction; web video prefetching
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lee, S.; Yoo, J.; Han, G. Gaze-Assisted User Intention Prediction for Initial Delay Reduction in Web Video Access. Sensors 2015, 15, 14679-14700.

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