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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.


Relationship between hit-ratio and initial delay. (a) Normalized histogram of the hit-ratio; (b) influence of the hit-ratio.
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f8-sensors-15-14679: Relationship between hit-ratio and initial delay. (a) Normalized histogram of the hit-ratio; (b) influence of the hit-ratio.

Mentions: The hit-ratio differences between the cursor-gaze case and cursor-only case were statistically significant (F(1,109) = 20.339, p < 0.0005) with the one-way ANOVA test. Figure 8a shows a normalized histogram of the hit-ratio obtained from the implemented test system. The cursor-gaze case shows a much higher histogram than the cursor-only case in an over 90% hit-ratio region. The cursor-gaze case performed more precise prefetching decisions than the cursor-only case. This means that the cursor-gaze interrelationship leads to a high confidence decision, preventing unnecessary prefetching. Figure 8b shows that the initial delay decreased as the hit-ratio increased. However, this initial delay improvement tends to be saturated when the hit-ratio exceeds about 85%. This means that the introduction of additional input devices for hit-ratio enhancement higher than 85% may not be necessary.


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

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

Relationship between hit-ratio and initial delay. (a) Normalized histogram of the hit-ratio; (b) influence of the hit-ratio.
© Copyright Policy
Related In: Results  -  Collection

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

f8-sensors-15-14679: Relationship between hit-ratio and initial delay. (a) Normalized histogram of the hit-ratio; (b) influence of the hit-ratio.
Mentions: The hit-ratio differences between the cursor-gaze case and cursor-only case were statistically significant (F(1,109) = 20.339, p < 0.0005) with the one-way ANOVA test. Figure 8a shows a normalized histogram of the hit-ratio obtained from the implemented test system. The cursor-gaze case shows a much higher histogram than the cursor-only case in an over 90% hit-ratio region. The cursor-gaze case performed more precise prefetching decisions than the cursor-only case. This means that the cursor-gaze interrelationship leads to a high confidence decision, preventing unnecessary prefetching. Figure 8b shows that the initial delay decreased as the hit-ratio increased. However, this initial delay improvement tends to be saturated when the hit-ratio exceeds about 85%. This means that the introduction of additional input devices for hit-ratio enhancement higher than 85% may not be necessary.

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.