Endpoint prediction using motion kinematics

Lank, Edward; Cheng, Yi-Chun Nikko; Ruiz, Jaime

doi:10.1145/1240624.1240724

Cited by 82 publications

(68 citation statements)

References 19 publications

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“…Difficulty staying on target (user moves pointer before clicking) -Make it harder to leave a target once on it [4,13] (or user moves mouse during click and falls off target) -Freeze mouse cursor while button is pressed [9].…”

Section: Targeting Errorsmentioning

confidence: 99%

Automatically detecting pointing performance

Hurst

Hudson

Mankoff

et al. 2008

Proceedings of the 13th International Conference on Intelligent User Interfaces

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Since not all persons interact with computer systems in the same way, computer systems should not interact with all individuals in the same way. This paper presents a significant step in automatically detecting characteristics of persons with a wide range of abilities based on observing their user input events. Three datasets are used to build learned statistical models on pointing data collected in a laboratory setting from individuals with varying ability to use computer pointing devices. The first dataset is used to distinguish between pointing behaviors from individuals with pointing problems vs. individuals without with 92.7% accuracy. The second is used to distinguish between pointing data from Young Adults and Adults vs. Older Adults vs. individuals with Parkinson's Disease with 91.6% accuracy. The final data set is used to predict the need for a specific adaptation based on a user's performance with 94.4% accuracy. These results suggest that it may be feasible to use such models to automatically identify computer users who would benefit from accessibility tools, and to even make specific tool recommendations.

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Section: Targeting Errorsmentioning

confidence: 99%

Automatically detecting pointing performance

Hurst

Hudson

Mankoff

et al. 2008

Proceedings of the 13th International Conference on Intelligent User Interfaces

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“…A number of studies have analysed cursor trajectory to predict the path and distance to the target (Murata, 1998;Asano et al, 2005;Lank et al, 2007). The difficulty with this approach is that people do not always follow predictable paths.…”

Section: Target Distractersmentioning

confidence: 99%

Augmenting graphical user interfaces with haptic assistance for motion-impaired operators

Asque

Day

Laycock

2014

International Journal of Human-Computer Studies

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Haptic assistance is an emerging field of research that is designed to improve human-computer interaction (HCI) by reducing error rates and targeting times through the use of force feedback. Haptic feedback has previously been investigated to assist motionimpaired computer users, however, limitations such as target distracters have hampered its integration with graphical user interfaces (GUIs). In this paper two new haptic assistive techniques are presented that utilise the 3DOF capabilities of the Phantom Omni. These are referred to as deformable haptic cones and deformable virtual switches. The assistance is designed specifically to enable motion-impaired operators to use existing GUIs more effectively. Experiment 1 investigates the performance benefits of the new haptic techniques when used in conjunction with the densely populated Windows on-screen keyboard (OSK). Experiment 2 utilises the ISO 9241-9 point-and-click task to investigate the effects of target size and shape. The results of the study prove that the newly proposed techniques improve interaction rates and can be integrated with existing software without many of the drawbacks of traditional haptic assistance. Deformable haptic cones and deformable virtual switches were shown to reduce the mean number of missed-clicks by at least 75% and reduce targeting times by at least 25%.

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“…Although devoted to a specific task (stroke selection on a screen-displayed keyboard), this work can be seen as a major step in the direction of continuous gesture analysis for computer-human interaction simplification. Lank et al provide estimators of gesture endpoint, using motion analysis [5]. None of the mentionned works proposed any method for combining selection and pick-release without button in the same application, which is the core contribution presented here.…”

Section: Related Workmentioning

confidence: 99%

Buttonless clicking: Intuitive select and pick-release through gesture analysis

Choumane

Casiez

Grisoni

2010

2010 IEEE Virtual Reality Conference (VR)

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Clicking is a key feature any interaction input system needs to provide. In the case of 3D input devices, such a feature is often difficult to provide (e.g. vision-based, or tracking systems for free-hand interaction do not natively provide any button). In this work, we show that it is actually possible to build an application that provides two classical interaction tasks (selection, and pick-release), without any button-like feature. Our method is based on trajectory and kinematic gesture analysis. In a preliminary study we exhibit the principle of the method. Then, we detail an algorithm to discriminate selection, pick and release tasks using kinematic criteria. We present a controlled experiment that validates our method with an average success rate equal to 90.1% across all conditions.

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Endpoint prediction using motion kinematics

Cited by 82 publications

References 19 publications

Automatically detecting pointing performance

Automatically detecting pointing performance

Augmenting graphical user interfaces with haptic assistance for motion-impaired operators

Buttonless clicking: Intuitive select and pick-release through gesture analysis

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