Haptic output in multimodal user interfaces

Münch, S.; Dillmann, Rüdiger

doi:10.1145/238218.238311

Cited by 28 publications

(13 citation statements)

References 9 publications

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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%

“…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. In contrast to our inverse optimal control approach, aggressive discretization of motion characteristics is needed limit the amount of data required to estimate model parameters and for predictions to remain computationally efficient.…”

Section: Classification Prediction Approachesmentioning

confidence: 99%

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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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Section: Prediction Requirements For Pointing Facilitationmentioning

confidence: 99%

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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“…They conclude that although the creation of an algorithm to perform such a task may be possible, the parameters that control it would vary substantially from device to device and from user to user. Munch & Dillmann (1997) describe a complete system that provides not only haptic feedback in a GUI, but also a target prediction system that attempts to mediate the application of this feedback. Their target prediction system relies on both trajectory analysis and a model of application behaviour to determine user destination.…”

Section: Introductionmentioning

confidence: 99%

Guidelines for the Design of Haptic Widgets

Oakley

Adams

Brewster

et al. 2002

People and Computers XVI - Memorable Yet Invisible

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Haptic feedback has been shown to improve user performance in Graphical User Interface (GUI) targeting tasks in a number of studies. These studies have typically focused on interactions with individual targets, and it is unclear whether the performance increases reported will generalise to the more realistic situation where multiple targets are presented simultaneously. This paper addresses this issue in two ways. Firstly two empirical studies dealing with groups of haptically augmented widgets are presented. These reveal that haptic augmentations of complex widgets can reduce performance, although carefully designed feedback can result in performance improvements. The results of these studies are then used in conjunction with the previous literature to generate general design guidelines for the creation of haptic widgets.

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“…One way of communicating this information to these users is through audio, but speech output limits the navigation and access of the visual data. To address these limitations, additional feedback can be provided to the visually impaired users through haptic or tactile displays [9], [10], [11].…”

Section: B Related Workmentioning

confidence: 99%

Using action research for complex research initiatives

Greeff

Coetzee

2009

2009 2nd International Conference on Adaptive Science &Amp; Technology (ICAST)

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Training methods and the transfer of knowledge and information has essentially remained unchanged for many years. With the pervasiveness of information and communication technology (ICT), an opportunity has been created to fundamentally change the status quo of training. This paper presents one approach of using ICT through the innovative combination of factors such as a person's abilities, styles and preferences with a multimodal interface to facilitate enhanced training. However, many ways exist to guide the research process of such a complex research initiative. Action research is one research method that lends itself to these complex projects. The paper uses the Ability Based Technology Interventions (AbTi) research project as a case study to analyse the effectiveness of the action research methodology. The paper describes the accumulated learning as obtained from the AbTi research project, as well as the action research methodology used, in order to ultimately be in a position to effect a fundamental change in training. It is found that action research can be an effective methodology for research initiatives of a complex nature.

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Haptic output in multimodal user interfaces

Cited by 28 publications

References 9 publications

Probabilistic pointing target prediction via inverse optimal control

Probabilistic pointing target prediction via inverse optimal control

Guidelines for the Design of Haptic Widgets

Using action research for complex research initiatives

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