Next Article in Journal
Performance Analysis of Several GPS/Galileo Precise Point Positioning Models
Next Article in Special Issue
Augmented Robotics Dialog System for Enhancing Human–Robot Interaction
Previous Article in Journal
Radar Sensing for Intelligent Vehicles in Urban Environments
Previous Article in Special Issue
Assessing Visual Attention Using Eye Tracking Sensors in Intelligent Cognitive Therapies Based on Serious Games
Article Menu

Export Article

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

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

School of Integrated Technology, Yonsei University, Incheon 406-840, Korea
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]   |  


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

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top