User-defined motion gestures for mobile interaction

Ruiz, Jaime; Li, Yang; Lank, Edward

doi:10.1145/1978942.1978971

Cited by 363 publications

(286 citation statements)

References 28 publications

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“…To explore the breadth of potential hand-to-face gestures and their mapping to interactive tasks, we elicited user input through a guessability study [1,24,27,33]. For a set of common mobile tasks, as in [24], we asked participants to suggest suitable gestures on the face (above the neck) and on the HWD.…”

Section: Guessability Studymentioning

confidence: 99%

“…For a set of common mobile tasks, as in [24], we asked participants to suggest suitable gestures on the face (above the neck) and on the HWD.…”

Section: Guessability Studymentioning

confidence: 99%

“…For exploring potentially rich and vast gesture sets, userelicitation or guessability studies have shown favourable results [1,24,27,33]. Wobbrock et al [33] found that eliciting gestures from users resulted in over 40% more gestures than if asked by expert designers.…”

Section: Overview and Rationalementioning

confidence: 99%

“…Wobbrock et al [33] found that eliciting gestures from users resulted in over 40% more gestures than if asked by expert designers. This motivated the use of such an approach to identify gestures for a multitouch tabletop [33], for mobile motion gestures [24], and for foot interaction [1]. As in these previous works, we focus primarily on human capabilities.…”

Section: Overview and Rationalementioning

confidence: 99%

“…For these reasons in this first exploration, we elicit gestures for common mobile tasks. Inspired by Ruiz et al [24] we divide tasks into two categories: action (tasks 1 to 9) and navigation (tasks 10 to 22). Action includes tasks such as answering a call, taking a picture or selection.…”

Section: Mobile Tasks Consideredmentioning

confidence: 99%

See 4 more Smart Citations

Exploring the use of hand-to-face input for interacting with head-worn displays

Serrano

Ens

Irani

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

103

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We propose the use of Hand-to-Face input, a method to interact with head-worn displays (HWDs) that involves contact with the face. We explore Hand-to-Face interaction to find suitable techniques for common mobile tasks. We evaluate this form of interaction with document navigation tasks and examine its social acceptability. In a first study, users identify the cheek and forehead as predominant areas for interaction and agree on gestures for tasks involving continuous input, such as document navigation. These results guide the design of several Hand-to-Face navigation techniques and reveal that gestures performed on the cheek are more efficient and less tiring than interactions directly on the HWD. Initial results on the social acceptability of Hand-to-Face input allow us to further refine our design choices, and reveal unforeseen results: some gestures are considered culturally inappropriate and gender plays a role in selection of specific Hand-to-Face interactions. From our overall results, we provide a set of guidelines for developing effective Hand-to-Face interaction techniques.

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Section: Guessability Studymentioning

confidence: 99%

“…For a set of common mobile tasks, as in [24], we asked participants to suggest suitable gestures on the face (above the neck) and on the HWD.…”

Section: Guessability Studymentioning

confidence: 99%

Section: Overview and Rationalementioning

confidence: 99%

Section: Overview and Rationalementioning

confidence: 99%

Section: Mobile Tasks Consideredmentioning

confidence: 99%

See 3 more Smart Citations

Exploring the use of hand-to-face input for interacting with head-worn displays

Serrano

Ens

Irani

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

103

View full text Add to dashboard Cite

show abstract

Interacting with Mobile Media

Sundar

Cho

Wang

2017

The Wiley Handbook of Human Computer Interaction

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GestureTeach: A gesture guided online teaching interactive model

Liu,

Yao,

Zhang

et al. 2023

Computer Animation & Virtual

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Online education has become more popular and effective due to the availability of high‐speed internet and technological innovations, which allow people from different locations to access educational resources and opportunities. However, online classes often face challenges such as limited interactivity and display options, which can affect the quality and effectiveness of the online learning experience. In this article, we propose GestureTeach, a new pedagogical paradigm that enables free handwriting interaction and animation generation for online teaching. GestureTeach uses gestures as a natural and intuitive way of interaction, which enhances the teacher's intention expression and the student's engagement. GestureTeach also generates animations from handwritten sketches, which improves the display effects of the interaction and the student's knowledge comprehension. We conducted a two‐stage study with 15 teachers and 90 students to evaluate the effectiveness of GestureTeach in facilitating classroom interaction. The results show that GestureTeach is preferred by both teachers and students over traditional online teaching methods and has the potential to transform the online teaching landscape by providing a seamless and interactive experience.

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User-defined motion gestures for mobile interaction

Cited by 363 publications

References 28 publications

Exploring the use of hand-to-face input for interacting with head-worn displays

Exploring the use of hand-to-face input for interacting with head-worn displays

Interacting with Mobile Media

GestureTeach: A gesture guided online teaching interactive model

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