One-handed interaction with augmented virtual objects on mobile devices

Seo, Byung-Kuk; Choi, Junyeoung; Han, Jae-Hyek; Park, Hanhoon; Park, Jong-Il

doi:10.1145/1477862.1477873

Cited by 30 publications

(23 citation statements)

References 15 publications

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“…An ANOVA for the completed dataset also revealed significant differences (F (2, 87) = 18.1 and p = 2.63 × 10 −7 < 0.05). Here, a post-hoc analysis using Tukey's HSD test showed that HSD = 479.4, with |M 1 −M 2 | = 471.9, |M 2 −M 3 | = 730.2, and |M 3 − M 1 | = 1202.1, and thus no significant difference between the [1,10] and [1,20] groups, but differences among the other groups ( [1,20], [1,30] and [1,10], [1,30]). Figure 3 illustrates these results.…”

Section: Task Completedmentioning

confidence: 84%

“…This work focused mainly on the tracking technique, and it has not been demonstrated and evaluated as part of a complete interaction solution featuring different types of interaction and comparative performance evaluations. Seo et al [20] estimated the palm pose by filtering potential hand regions based on skin color and subsequently applying a distance transformation to find the biggest area. The virtual object appeared on top of this detected region and could move along with the palm.…”

Section: Related Workmentioning

confidence: 99%

“…We were interested in locating any range of drastic performance drop-off to determine the best suitability of our 4×4 grid-board method. A random number was chosen from three different ranges, [1,10], [1,20], or [1,30], and displayed on the screen (Figure 2 (c)). The users' task was to move their hands/fingers in or out the AR scene to control the value to match the displayed number.…”

Section: Goalsmentioning

confidence: 99%

“…Every 10 seconds, if users could not finish the task, the system would time out, recorded 10 seconds as the maximum time, and move on to the next random number; otherwise, the completion time was recorded internally and the next trial started. We asked users to do this task 75 times, with 25 trials from [1,10], 25 from [1,20], and 25 from [1,30], in random order.…”

Section: Goalsmentioning

confidence: 99%

“…Full data Completed data Mean SD Mean SD [1,10] 69.2% 7102.2 946.5 5866.8 718.6 [1,20] 53.6% 8076.7 590.0 6338.7 776.6 [1,30] 39.3% 8886.8 410.4 7068.9 837.9 Table 2. Results of continuous value adjustment Table 2 reports the results of our first (continuous value adjustment) test, with the collected dataset sliced two ways: The full dataset represents all 2250 runs (25 runs × 3 ranges × 30 participants) we collected during the study.…”

Section: Task Completedmentioning

confidence: 99%

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Real-time hand interaction for augmented reality on mobile phones

Chun¹,

Höllerer

2013

Proceedings of the 2013 International Conference on Intelligent User Interfaces

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Over the past few years, Augmented Reality has become widely popular in the form of smart phone applications, however most smart phone-based AR applications are limited in user interaction and do not support gesture-based direct manipulation of the augmented scene. In this paper, we introduce a new AR interaction methodology, employing users' hands and fingers to interact with the virtual (and possibly physical) objects that appear on the mobile phone screen. The goal of this project was to support different types of interaction (selection, transformation, and fine-grain control of an input value) while keeping the methodology for hand detection as simple as possible to maintain good performance on smart phones. We evaluated our methods in user studies, collecting task performance data and user impressions about this direct way of interacting with augmented scenes through mobile phones.

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Section: Task Completedmentioning

confidence: 84%

Section: Related Workmentioning

confidence: 99%

Section: Goalsmentioning

confidence: 99%

Section: Goalsmentioning

confidence: 99%

Section: Task Completedmentioning

confidence: 99%

See 3 more Smart Citations

Real-time hand interaction for augmented reality on mobile phones

Chun¹,

Höllerer

2013

Proceedings of the 2013 International Conference on Intelligent User Interfaces

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Manipulating augmented virtual character using dynamic hierarchical pointing interface

Kim

Hwang

2018

Computer Animation & Virtual

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In this paper, we introduce a bare-hand interaction method for controlling an augmented virtual character. Our method is utilizing a dynamic hierarchical structure of virtual buttons on the natural marker of the environment. Unlike the existing virtual button method, the proposed method can be used to select a very fine level. This method is adequate for controlling an augmented virtual character with a users' various intentions. We implemented several examples of manipulating an augmented virtual character such as guiding the character, selecting part of the character, and selecting a target object for the character. We also compared the performance of full arrangement and dynamic hierarchical arrangement of virtual buttons. Our method outperforms the full arrangement method, especially in the low index of difficulty. In the user study with 15 participants, the users responded that the proposed method was significantly better than the existing full arrangement method regarding efficiency and satisfaction in the case of the fine level selection.

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