Model-based evaluation of expert cell phone menu interaction

Amant, Robert St.; Horton, T. E.; Ritter, Frank E.

doi:10.1145/1229855.1229856

Cited by 74 publications

(22 citation statements)

References 24 publications

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“…8 shows the tree decomposition corresponding to the main goal of the GOMS model. The duration of a step in the GOMS model can be defined as the sum of the production cycle duration which equals to 50 ms and the duration of all actions included in the body of the step (Kieras et al, 1996;Amant et al, 2007) such as key presses which take 280 ms to be performed.…”

Section: Modeling the Interaction With The Contextual Assistant Usingmentioning

confidence: 99%

“…In the GOMS model, the user achieves goals by solving subgoals in a divide and conquer fashion (Dix et al, 2004). The GOMS model includes perceptual, cognitive, and motor components involved in task realization, and it is widely used in the evaluation of human-computer interaction (Amant et al, 2007). The third model selected is the Fitts' Law model (Fitts, 1954), which is based on a mathematical model.…”

Section: Introductionmentioning

confidence: 99%

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Towards analytical evaluation of human machine interfaces developed in the context of smart homes

Chikhaoui

Pigot

2010

Interacting with Computers

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a b s t r a c tDesigning human machine interfaces that respect the ergonomic norms and following rigorous approaches constitutes a major concern for computer systems designers. The increased need on easily accessible and usable interfaces leads researchers in this domain to create methods and models that make it possible to evaluate these interfaces in terms of utility and usability. Two different approaches are currently used to evaluate human machine interfaces, empirical approaches that require user involvement in the interface development process, and analytical approaches that do not associate the user during the interface development process. This paper presents a study of user performance on two principal tasks of the contextual assistant's interface, developed in the context of smart homes, to assist persons with cognitive disabilities. We use three different methods to analyze and evaluate this interface, focusing basically on time of execution. Two of the models developed are based on cognitive models, which are ACT-R and GOMS and the third one is based on the Fitts' Law model. The results show that, all models give a good prediction of user performance, even if the cognitive models show better accuracy of the user performance. Furthermore, they provide a better insight into cognitive abilities required to interact with the interface.

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Section: Modeling the Interaction With The Contextual Assistant Usingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards analytical evaluation of human machine interfaces developed in the context of smart homes

Chikhaoui

Pigot

2010

Interacting with Computers

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“…Studies on revised models on touch gestures reflecting the mobile environment are actively carried out recently, when smart devices such as cellphones and tablet PCs embedded with a touch screen are vigorously released in the markets (Amant et al, 2007;Holleis et al, 2007;Lee and Myung, 2009;Back and Myung, 2011;Choi et al, 2013). These studies predicted gesture performance time by revising or re-defining the existing operator.…”

Section: Introductionmentioning

confidence: 99%

A Unit Touch Gesture Model of Performance Time Prediction for Mobile Devices

Kim¹,

Myung²

2016

Journal of the Ergonomics Society of Korea

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Objectiv e:The aim of this study is to propose a unit touch gesture model, which would be useful to predict the performance time on mobile devices. Background:When estimating usability based on Model-based Evaluation (MBE) in interfaces, the GOMS model measured 'operators' to predict the execution time in the desktop environment. Therefore, this study used the concept of operator in GOMS for touch gestures. Since the touch gestures are comprised of possible unit touch gestures, these unit touch gestures can predict to performance time with unit touch gestures on mobile devices. Method:In order to extract unit touch gestures, manual movements of subjects were recorded in the 120 fps with pixel coordinates. Touch gestures are classified with 'out of range', 'registration', 'continuation' and 'termination' of gesture. Results:As a results, six unit touch gestures were extracted, which are hold down (H), Release (R), Slip (S), Curved-stroke (Cs), Path-stroke (Ps) and Out of range (Or). The movement time predicted by the unit touch gesture model is not significantly different from the participants' execution time. The measured six unit touch gestures can predict movement time of undefined touch gestures like user-defined gestures. Conclusion:In conclusion, touch gestures could be subdivided into six unit touch gestures. Six unit touch gestures can explain almost all the current touch gestures including user-defined gestures. So, this model provided in this study has a high predictive power. The model presented in the study could be utilized to predict the performance time of touch gestures. Application:The unit touch gestures could be simply added up to predict the performance time without measuring the performance time of a new gesture.

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“…Despite this, mobile phones are still often regarded as being difficult to use [1]. Due to the size restrictions of mobile phones, providing a more usable interface is hard to achieve.…”

Section: Introductionmentioning

confidence: 99%

SmartActions: Context-Aware Mobile Phone Shortcuts

Vetek

Flanagan

Colley

et al. 2009

Lecture Notes in Computer Science

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Abstract. Mobile phones are often regarded as difficult to use due to their size restrictions. To improve on this, in this paper we described our approach using unsupervised learning to automate common tasks on a mobile phone, thereby requiring less key presses, by means of context-dependent quick-access shortcuts presented in the homescreen of the phone. We also briefly reviewed some of our user study findings, and raised the issue of possible privacy concerns with our implementation.

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Model-based evaluation of expert cell phone menu interaction

Cited by 74 publications

References 24 publications

Towards analytical evaluation of human machine interfaces developed in the context of smart homes

Towards analytical evaluation of human machine interfaces developed in the context of smart homes

A Unit Touch Gesture Model of Performance Time Prediction for Mobile Devices

SmartActions: Context-Aware Mobile Phone Shortcuts

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