Geometrically Compensating Effect of End-to-End Latency in Moving-Target Selection Games

Lee, Injung; Kim, Sunjun; Lee, Byungjoo

doi:10.1145/3290605.3300790

Cited by 32 publications

(6 citation statements)

References 47 publications

(103 reference statements)

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“…Related works have shown that the MT increases as a target's speed V increases [24,26,34]. For error rate prediction, naturally, as the V increases, users are more likely to fail a task [30,32,33,39,50].…”

Section: Related Work Success or Error Rate Prediction Models For Poimentioning

confidence: 99%

Servo-Gaussian Model to Predict Success Rates in Manual Tracking

Yamanaka¹,

Usuba

Takahashi

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

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We propose a Servo-Gaussian model to predict success rates in continuous manual tracking tasks. Two tasks were conducted to validate this model: path steering and pursuit of a 1D moving target. We hypothesized that (1) hand movements follow the servo-mechanism model, (2) submovement endpoints form a bivariate Gaussian distribution, thus enabling us to predict the success rate at which a submovement endpoint falls inside the tolerance, and (3) the success rate for a whole trial can be predicted if the number of submovements is known. The cross-validation showed R 2 > 0.92 and MAE < 4.9% for steering and R 2 > 0.95 and MAE < 6.5% for pursuit tasks. These results demonstrate that our proposed model delivers high prediction accuracy even for unknown datasets.

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Section: Related Work Success or Error Rate Prediction Models For Poimentioning

confidence: 99%

Servo-Gaussian Model to Predict Success Rates in Manual Tracking

Yamanaka¹,

Usuba

Takahashi

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

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“…While Wobbrock et al applied a time limit as an objective constraint by using a metronome in their study [54], this speed-accuracy tradeoff was also valid when the priority was subjectively biased [63]. Besides the case of rapidly aimed movements, the error rate has also been investigated for tapping on a static Model 205:3 button within a given temporal window [30][31][32]. Despite the recent importance of finger-touch operations on smartphones and tablets, however, the only model for predicting the success rate while accounting for finger-touch ambiguity is the work by Bi and Zhai on pointing from an off-screen start [10].…”

Section: Success-rate Prediction For Pointing Tasksmentioning

confidence: 99%

Rethinking the Dual Gaussian Distribution Model for Predicting Touch Accuracy in On-screen-start Pointing Tasks

Yamanaka¹,

Usuba

2020

Proc. ACM Hum.-Comput. Interact.

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The dual Gaussian distribution hypothesis has been used to predict the success rate of target pointing on touchscreens. Bi and Zhai evaluated their success-rate prediction model in off-screen-start pointing tasks. However, we found that their prediction model could also be used for on-screen-start pointing tasks. We discuss the reasons why and empirically validate our hypothesis in a series of four experiments with various target sizes and distances. The prediction accuracy of Bi and Zhai's model was high in all of the experiments, with a 10-point absolute (or 14.9% relative) prediction error at worst. Also, we show that there is no clear benefit to integrating the target distance when predicting the endpoint variability and success rate.

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“…Lee et al [82] propose adjusting the geometry of a game world based on latency. Speciically, they adjust the gap distance between pipes based on latency for the game Flappy Bird.…”

Section: Atributementioning

confidence: 99%

A Survey and Taxonomy of Latency Compensation Techniques for Network Computer Games

Liu

Claypool

2022

ACM Comput. Surv.

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Computer games, one of the most popular forms of entertainment in the world, are increasingly online multiplayer, connecting geographically dispersed players in the same virtual world over a network. Network latency between players and the server can decrease responsiveness and increase inconsistency across players, degrading player performance and quality of experience. Latency compensation techniques are software-based solutions that seek to ameliorate the negative effects of network latency by manipulating player input and/or game states in response to network delays. We search, find, and survey over 80 papers on latency compensation, organizing their latency compensation techniques into a novel taxonomy. Our hierarchical taxonomy has eleven base technique types organized into four main groups. Illustrative examples of each technique are provided as well as demonstrated use of the techniques in commercial games.

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Geometrically Compensating Effect of End-to-End Latency in Moving-Target Selection Games

Cited by 32 publications

References 47 publications

Servo-Gaussian Model to Predict Success Rates in Manual Tracking

Servo-Gaussian Model to Predict Success Rates in Manual Tracking

Rethinking the Dual Gaussian Distribution Model for Predicting Touch Accuracy in On-screen-start Pointing Tasks

A Survey and Taxonomy of Latency Compensation Techniques for Network Computer Games

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