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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.


Measured CDF of the initial delay.
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f11-sensors-15-14679: Measured CDF of the initial delay.

Mentions: The initial delay differences between the without-prefetching case, cursor-gaze case and cursor-only case were statistically significant (F(2,139) = 59.617, p < 0.0005). Figure 11 shows a cumulative density function (CDF) of the initial delay for each version, demonstrating the overall performance of the proposed system. The cursor-gaze case recorded a 0.58-s initial delay as a median value, which was more than one-third of the without case that had a 1.72-s initial delay. Moreover, 45% of the cursor-gaze case was recorded under 0.5 s, which was perceived as an instantaneous access (IA), whereas only 18% of the cursor-only case and a negligible portion of the without case recorded under IA. On the other hand, all of the recorded initial delays of the cursor-gaze case were within about 1.5 s, which was much lower than the user tolerable limit (TL), i.e., 2 s, while only 65% of the recorded initial delay falls within the TL in the without case.


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

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

Measured CDF of the initial delay.
© Copyright Policy
Related In: Results  -  Collection

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

f11-sensors-15-14679: Measured CDF of the initial delay.
Mentions: The initial delay differences between the without-prefetching case, cursor-gaze case and cursor-only case were statistically significant (F(2,139) = 59.617, p < 0.0005). Figure 11 shows a cumulative density function (CDF) of the initial delay for each version, demonstrating the overall performance of the proposed system. The cursor-gaze case recorded a 0.58-s initial delay as a median value, which was more than one-third of the without case that had a 1.72-s initial delay. Moreover, 45% of the cursor-gaze case was recorded under 0.5 s, which was perceived as an instantaneous access (IA), whereas only 18% of the cursor-only case and a negligible portion of the without case recorded under IA. On the other hand, all of the recorded initial delays of the cursor-gaze case were within about 1.5 s, which was much lower than the user tolerable limit (TL), i.e., 2 s, while only 65% of the recorded initial delay falls within the TL in the without case.

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.