A Process for Anticipating and Executing Icon Selection in Graphical User Interfaces

Lane, David M.; Peres, S. Camille; Sándor, Anikó; Napier, H. Albert

doi:10.1207/s15327590ijhc1902_5

Cited by 20 publications

(9 citation statements)

References 7 publications

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“…Researchers already worked on algorithms to reduce pointing time through determining the difficulty of a task using Fitts' Law (Fitt, 1954), increasing target size (Bates, 1999;Hwang, Hollinworth, & Williams, 2013;Lane, Peres, Sándor, & Napier, 2005;McGuffin & Balakrishnan, 2005), employing larger cursor activation regions, moving targets closer to cursor location, dragging cursor to nearest target, changing CD ratio (Wobbrock, Fogarty, Liu, Kimuro, & Harada, 2009), and so on. It is certain that these algorithms will perform even better in the existence of a target prediction algorithm so that only correct or most probable targets could be dynamically altered.…”

Section: Target Prediction Modelmentioning

confidence: 99%

Multimodal Intelligent Eye-Gaze Tracking System

Biswas

Langdon

2015

International Journal of Human-Computer Interaction

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This article presents a series of user studies to develop a new eye-gaze tracking-based pointing system. We developed a new target prediction model that works for different input modalities and combined the eye-gaze tracking-based pointing with a joystick controller that can reduce pointing and selection times. The system finds important applications in cockpit of combat aircraft and for computer novice users. User studies confirmed that users can perform significantly faster using this new eye-gaze tracking-based system for both military and everyday computing tasks compared to existing input devices. As part of the study it was also found that the amplitude of maximum power component obtained through Fourier Transform of pupil signal significantly correlates with selection times and perceived cognitive load of users in terms of Task Load Index scores.

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Section: Target Prediction Modelmentioning

confidence: 99%

Multimodal Intelligent Eye-Gaze Tracking System

Biswas

Langdon

2015

International Journal of Human-Computer Interaction

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“…Many different approaches have been developed for this prediction task [25,18,21,3,22]. However, we will argue throughout this paper that those approaches have lacked important properties required for supporting pointing facilitation systems, or have been too simplistic to provide accurate predictions in the general cursor target prediction setting.…”

Section: Prediction Requirements For Pointing Facilitationmentioning

confidence: 99%

“…Early work specifically devoted to pointing target prediction treats the prediction task as a classification problem using properties of the motion trajectory state sequence as input. For example, Lane et al [21] provides short-cuts for selecting predicted targets and base target predictions primarily on the distance to each potential target. Though probabilistic techniques have been employed, e.g., using a multinomial distribution for targets conditioned on velocity, acceleration, and motion curvature characteristics [25], they have been in support of target classification rather than belief-based prediction.…”

Section: Classification Prediction Approachesmentioning

confidence: 99%

Probabilistic pointing target prediction via inverse optimal control

Ziebart

Dey

Bagnell

2012

Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces

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Numerous interaction techniques have been developed that make "virtual" pointing at targets in graphical user interfaces easier than analogous physical pointing tasks by invoking target-based interface modifications. These pointing facilitation techniques crucially depend on methods for estimating the relevance of potential targets. Unfortunately, many of the simple methods employed to date are inaccurate in common settings with many selectable targets in close proximity. In this paper, we bring recent advances in statistical machine learning to bear on this underlying target relevance estimation problem. By framing past target-driven pointing trajectories as approximate solutions to well-studied control problems, we learn the probabilistic dynamics of pointing trajectories that enable more accurate predictions of intended targets.

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“…Therefore, destination prediction should precede any pointing facilitation action. Existing prediction algorithms include the nearest neighbor, which chooses the target that is closest to the cursor's current position [24], and bearing angle, which assumes that the cursor moves in a nearly constant direction toward the intended endpoint [23], [25]. These methods treat destination inference as a classification problem and rely on known priors such as selection pattern(s).…”

Section: Related Workmentioning

confidence: 99%

Intent Inference for Hand Pointing Gesture-Based Interactions in Vehicles

Ahmad

Murphy

Langdon

et al. 2016

IEEE Trans. Cybern.

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Using interactive displays, such as a touchscreen, in vehicles typically requires dedicating a considerable amount of visual as well as cognitive capacity and undertaking a hand pointing gesture to select the intended item on the interface. This can act as a distractor from the primary task of driving and consequently can have serious safety implications. Due to road and driving conditions, the user input can also be highly perturbed resulting in erroneous selections compromising the system usability. In this paper, we propose intent-aware displays that utilize a pointing gesture tracker in conjunction with suitable Bayesian destination inference algorithms to determine the item the user intends to select, which can be achieved with high confidence remarkably early in the pointing gesture. This can drastically reduce the time and effort required to successfully complete an in-vehicle selection task. In the proposed probabilistic inference framework, the likelihood of all the nominal destinations is sequentially calculated by modeling the hand pointing gesture movements as a destination-reverting process. This leads to a Kalman filter-type implementation of the prediction routine that requires minimal parameter training and has low computational burden; it is also amenable to parallelization. The substantial gains obtained using an intent-aware display are demonstrated using data collected in an instrumented vehicle driven under various road conditions.

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A Process for Anticipating and Executing Icon Selection in Graphical User Interfaces

Cited by 20 publications

References 7 publications

Multimodal Intelligent Eye-Gaze Tracking System

Multimodal Intelligent Eye-Gaze Tracking System

Probabilistic pointing target prediction via inverse optimal control

Intent Inference for Hand Pointing Gesture-Based Interactions in Vehicles

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