Experimental Analysis of Barehand Mid-air Mode-Switching Techniques in Virtual Reality

Surale, Hemant Bhaskar; Matulic, Fabrice; Vogel, Daniel

doi:10.1145/3290605.3300426

Cited by 42 publications

(25 citation statements)

References 82 publications

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“…All input methods and corresponding gestures are summarised in Table 1. We cover a wide range of different input methods and gestures, many of which are frequently used in virtual environments (e.g., touch: [42,76], mid-air: [72,77], eye gaze: [42,55]). We decided to pre-record the interactions of both the human in the real world and the avatars in a virtual environment.…”

Section: Preamblementioning

confidence: 99%

Observing Virtual Avatars: The Impact of Avatars’ Fidelity on Identifying Interactions

Mathis

Vaniea

Khamis

2021

Academic Mindtrek 2021

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Figure 1: We investigate the impact of an avatar's fidelity on a bystander's performance when identifying the avatar's touch, mid-air, and eye gaze gestures. We had an abstract avatar (➋) and two more realistic avatars (➌, ➍), which are provided by Microsoft Research [24] and modified based on our investigation. We use touch, mid-air, and eye gaze gestures performed by a human in the real world (➊) as our baseline.

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Section: Preamblementioning

confidence: 99%

Observing Virtual Avatars: The Impact of Avatars’ Fidelity on Identifying Interactions

Mathis

Vaniea

Khamis

2021

Academic Mindtrek 2021

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“…Specifically, hand gestures to do mode-switching have been discussed in HMD. Surale et al [31] presented two studies to evaluate barehand mid-air mode-switching techniques for VR HMD. They followed the subtraction method and compared dominant and non-dominant hand gestures in a VR line-drawing task.…”

Section: Mode-switching and Related Studies In Hmdmentioning

confidence: 99%

“…Having an efficient mode-switching technique or mechanism can be supportive for scenarios that involve more than one mode. The existing research tended to focus on and suggested using hand-based interactions for mode switching [29,31]. However, hand-based mode-switching techniques are not suitable or ideal for many VR applications, for example, when users need to use both hands for interaction, which is common in HMD [12].…”

Section: Introductionmentioning

confidence: 99%

“…We conducted two user studies to evaluate their usability and performance. In the first study, we collected and compared the performance and users' subjective feedback of head gestures in an extended linedrawing task which was designed based on the certified "subtraction method" [4,13,30,31,33]. Results from this study show that move forward, move backward, roll left, and roll right had better performance and were preferred by the participants.…”

Section: Introductionmentioning

confidence: 99%

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Exploring Head-based Mode-Switching in Virtual Reality

Shi

Zhu

Liang

et al. 2021

Preprint

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Mode-switching supports multilevel operations using a limited number of input methods. In Virtual Reality (VR) head-mounted displays (HMD), common approaches for mode-switching use buttons, controllers, and users' hands. However, they are inefficient and challenging to do with tasks that require both hands (e.g., when users need to use two hands during drawing operations). Using head gestures for mode-switching can be an efficient and cost-effective way, allowing for a more continuous and smooth transition between modes. In this paper, we explore the use of head gestures for mode-switching especially in scenarios when both users' hands are performing tasks. We present a first user study that evaluated eight head gestures that could be suitable for VR HMD with a dual-hand line-drawing task. Results show that move forward, move backward, roll left, and roll right led to better performance and are preferred by participants. A second study integrating these four gestures in Tilt Brush, an opensource painting VR application, is conducted to further explore the applicability of these gestures and derive insights. Results show that Tilt Brush with head gestures allowed users to change modes with ease and led to improved interaction and user experience. The paper ends with a discussion on some design recommendations for using head-based mode-switching in VR HMD.

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“…Behavioral authentication refers to the authentication of persons' identities using their behavioral characteristics [27], [31], [32], [44], such as gestures, gaits, keystrokes, or signatures [5], [8], [18], [28], [33]. Many studies have confirmed that behavioral characteristics are significantly different among individuals, so these characteristics are proposed as certificates for authentication.…”

Section: Related Workmentioning

confidence: 99%

Exploring the Hand and Finger-Issued Behaviors Toward Natural Authentication

et al. 2020

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As wearables, such as smartwatches and data gloves, become ubiquitous in our daily lives, it is possible to capture users' behaviors as they go, and then to authenticate them using biometrics. A variety of behaviors have been exploited to authenticate users in a plethora of scenarios. However, there is still a lack of systematic investigations on what types of behaviors present better performance for authentication, and therefore, limiting our understanding of the custom design of behavioral ''passwords'' to fit people's needs. This paper is designed to help answer this question. First, we design a collection of behaviors, which is used to explore the effect of different types of behaviors on authentication. This collection involves hand and finger behaviors since they are common and natural human actions; it also reasonably covers both basic movements and complex behaviors, two major categories of behaviors. We evaluated authentication performance along the dimensions of behaviors, users (sensitivity) and classification models (independence). A valuable finding shows that finger-issued movements present almost equivalent performance with complex behaviors. Therefore, it suggests applying them to the wearable and mobile devices as an alternative to complex behaviors, and could considerably reduce requirements for computational resources compared to complex behaviors. Furthermore, they extend the applications of behavioral authentication to a wider array of scenarios, like performing behavioral authentication such as moving fingers in one's pocket, which is common for wearable and mobile applications.

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Experimental Analysis of Barehand Mid-air Mode-Switching Techniques in Virtual Reality

Cited by 42 publications

References 82 publications

Observing Virtual Avatars: The Impact of Avatars’ Fidelity on Identifying Interactions

Observing Virtual Avatars: The Impact of Avatars’ Fidelity on Identifying Interactions

Exploring Head-based Mode-Switching in Virtual Reality

Exploring the Hand and Finger-Issued Behaviors Toward Natural Authentication

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