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

Lee S, Yoo J, Han G - Sensors (Basel) (2015)

Bottom Line: 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.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 Article: PubMed Central - PubMed

Affiliation: School of Integrated Technology, Yonsei University, Incheon 406-840, Korea. youb007@yonsei.ac.kr.

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.

No MeSH data available.


Influence of preparation time. (a) Normalized histogram of preparation time; (b) initial delay depending on preparation time (the vertical axis is drawn in binary logarithmic scale.
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f9-sensors-15-14679: Influence of preparation time. (a) Normalized histogram of preparation time; (b) initial delay depending on preparation time (the vertical axis is drawn in binary logarithmic scale.

Mentions: The preparation time differences between the cursor-gaze case and cursor-only case were statistically significant (F(1,970) = 75.121, p < 0.0005). Figure 9a illustrates the normalized histogram of the preparation time. The cursor-gaze case had a longer preparation time region than the cursor-only case. This distribution shift means that the cursor-gaze interrelationship allows activating the prefetching earlier than the cursor-only case. Figure 9b shows that the initial delay drastically decreased until the preparation time reached 1 s, although the gap between the upper bound and the lower bound of the initial delay tended to be saturated as the preparation time was higher than 3 s.


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

Lee S, Yoo J, Han G - Sensors (Basel) (2015)

Influence of preparation time. (a) Normalized histogram of preparation time; (b) initial delay depending on preparation time (the vertical axis is drawn in binary logarithmic scale.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507624&req=5

f9-sensors-15-14679: Influence of preparation time. (a) Normalized histogram of preparation time; (b) initial delay depending on preparation time (the vertical axis is drawn in binary logarithmic scale.
Mentions: The preparation time differences between the cursor-gaze case and cursor-only case were statistically significant (F(1,970) = 75.121, p < 0.0005). Figure 9a illustrates the normalized histogram of the preparation time. The cursor-gaze case had a longer preparation time region than the cursor-only case. This distribution shift means that the cursor-gaze interrelationship allows activating the prefetching earlier than the cursor-only case. Figure 9b shows that the initial delay drastically decreased until the preparation time reached 1 s, although the gap between the upper bound and the lower bound of the initial delay tended to be saturated as the preparation time was higher than 3 s.

Bottom Line: 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.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 Article: PubMed Central - PubMed

Affiliation: School of Integrated Technology, Yonsei University, Incheon 406-840, Korea. youb007@yonsei.ac.kr.

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.

No MeSH data available.