Predicting Task Execution Time on Natural User Interfaces based on Touchless Hand Gestures

Erazo, Orlando; Pino, José A.

doi:10.1145/2678025.2701394

Cited by 19 publications

(12 citation statements)

References 76 publications

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“…Here, a is the actual observed execution time, p is the predicted execution time, and n is the number of observations. This measure is often used in the literature to assess the validity of a revised KLM (e.g., [48,61,69]), following the analysis of the original KLM [27]. Table 7 and Figure 2 show the average observed times and the predicted execution times for each of the five tasks for the two smartwatches (Apple Watch Series 3 and Samsung Gear S3).…”

Section: Discussionmentioning

confidence: 99%

A Predictive Fingerstroke-Level Model for Smartwatch Interaction

Al-Megren

2018

MTI

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The keystroke-level model (KLM) is commonly used to predict the time it will take an expert user to accomplish a task without errors when using an interactive system. The KLM was initially intended to predict interactions in conventional set-ups, i.e., mouse and keyboard interactions. However, it has since been adapted to predict interactions with smartphones, in-vehicle information systems, and natural user interfaces. The simplicity of the KLM and its extensions, along with their resource-and time-saving capabilities, has driven their adoption. In recent years, the popularity of smartwatches has grown, introducing new design challenges due to the small touch screens and bimanual interactions involved, which make current extensions to the KLM unsuitable for modelling smartwatches. Therefore, it is necessary to study these interfaces and interactions. This paper reports on three studies performed to modify the original KLM and its extensions for smartwatch interaction. First, an observational study was conducted to characterise smartwatch interactions. Second, the unit times for the observed interactions were derived through another study, in which the times required to perform the relevant physical actions were measured. Finally, a third study was carried out to validate the model for interactions with the Apple Watch and Samsung Gear S3. The results show that the new model can accurately predict the performance of smartwatch users with a percentage error of 12.07%; a value that falls below the acceptable percentage dictated by the original KLM~21%.

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Section: Discussionmentioning

confidence: 99%

A Predictive Fingerstroke-Level Model for Smartwatch Interaction

Al-Megren

2018

MTI

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“…Erazo and Pino (2015) conducted a literature review and defined a set of operators specifically aimed at the stroke phrase (s-phrase) of mid-air (or touchless) hand gestures. They focused on a specific area of gestures and did not fully discuss the results of their findings from the literature review and did not consider all possibilities for mid-air hand gestures.…”

Section: Reviews and Surveys Of Gestural Interfacesmentioning

confidence: 99%

Understanding 3D Mid-Air Hand Gestures with Interactive Surfaces and Displays: A Systematic Literature Review

Groenewald

Anslow

Islam

et al. 2016

Electronic Workshops in Computing

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“…To acquire the object (target), the user performs an action to position the selection tool (e.g., a finger, cursor) over the target. A tracking system also needs to acquire all the necessary information from the user through his/her actions or other movements [9,10]. Lin and Woldegiorgis [11] investigated the performance of a direct pointing technique, wherein participants directly moved a hand to reach for a real/virtual target shown continuously in a VE with projection display.…”

Section: Introductionmentioning

confidence: 99%

The Effects of Augmented Reality Interaction Techniques on Egocentric Distance Estimation Accuracy

2019

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Recent developments in virtual environment applications allow users to interact with three-dimensional (3D) objects in virtual environments. As interaction with 3D objects in virtual environments becomes more established, it is important to investigate user performance with such interaction techniques within a specific task. This study investigated two interaction modes, direct and indirect, depending on how the users interacted with the 3D objects, by measuring the accuracy of egocentric distance estimation in a stereoscopic environment. Fourteen participants were recruited to perform an acquisition task with both direct pointing and indirect cursor techniques at three egocentric distances and three task difficulty levels. The accuracy of the egocentric distance estimation, throughput, and task completion time were analyzed for each interaction technique. The indirect cursor technique was found to be more accurate than the direct pointing one. On the other hand, a higher throughput was observed with the direct pointing technique than with the indirect cursor technique. However, there were no significant differences in task completion time between the two interaction techniques. The results also showed accuracy to be higher at the greatest distance (150 cm from the participant) than at the closer distances of 90 cm and 120 cm. Furthermore, the difficulty of the task also significantly affected the accuracy, with accuracy lower in the highest difficulty condition than in the medium and low difficulty conditions. The findings of this study contribute to the understanding of user-interaction techniques in a stereoscopic environment. Furthermore, developers of virtual environments may refer to these findings in designing effective user interactions, especially those in which performance relies on accuracy.

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Predicting Task Execution Time on Natural User Interfaces based on Touchless Hand Gestures

Cited by 19 publications

References 76 publications

A Predictive Fingerstroke-Level Model for Smartwatch Interaction

A Predictive Fingerstroke-Level Model for Smartwatch Interaction

Understanding 3D Mid-Air Hand Gestures with Interactive Surfaces and Displays: A Systematic Literature Review

The Effects of Augmented Reality Interaction Techniques on Egocentric Distance Estimation Accuracy

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