Radi-Eye: Hands-Free Radial Interfaces for 3D Interaction using Gaze-Activated Head-Crossing

Sidenmark, Ludwig; Potts, Dominic; Bapisch, Bill; Gellersen, Hans

doi:10.1145/3411764.3445697

Cited by 28 publications

(13 citation statements)

References 49 publications

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“…To recognize head gestures or orientations, researchers have explored various sensing techniques such as motion sensing [14], acoustic sensing (Soundr [53]), capacitive sensing [16], vision-based sensing [23,35], and so on. Radi-Eye [40] is a hands-free radial interfaces for interaction in 3D space using both gaze and head crossing gestures. HeadCross [50] and HeadGesture [52] presented a hands-free interface using head movements for HMDs.…”

Section: Hands-free Gesture Inputmentioning

confidence: 99%

HeadText: Exploring Hands-free Text Entry using Head Gestures by Motion Sensing on a Smart Earpiece

Xu¹,

Wang²,

Fang³

et al. 2022

Preprint

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Fig. 1. Application scenarios of HeadText. Fig.a shows that users could perform head gestures to respond to messages when they are eating and their hands are holding the cake. Fig. b presents that a user could use HeadText to input text on a smart watch, whose text entry is originally limited due to the small screen size. Fig.c shows that HeadText could be utilized to help users with motor impairments for text entry. Fig. d presents the application scenario where users could perform head gestures for text entry on the smart glass which is private and socially acceptable in public. We present HeadText, a hands-free technique on a smart earpiece for text entry by motion sensing. Users input text utilizing only head gestures for key selection, word selection, word commitment and word cancelling tasks. Head gesture recognition is supported by motion sensing on a smart earpiece to capture head moving signals and machine learning algorithms (K-Nearest-Neighbor (KNN)with a Dynamic Time Warping (DTW) distance measurement). A 10-participant user study proved that HeadText could recognize head gestures at an accuracy of 94.29%. After that, the second user study presented that HeadText could achieve a maximum accuracy

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Section: Hands-free Gesture Inputmentioning

confidence: 99%

HeadText: Exploring Hands-free Text Entry using Head Gestures by Motion Sensing on a Smart Earpiece

Xu¹,

Wang²,

Fang³

et al. 2022

Preprint

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show abstract

“…We found the idea transferable to eye and hand interfaces, as we are not normally looking at our hands during visual exploration, but naturally do so when we manipulate a target [43]. In Look&Cross, eye and head are aligned for selection in a radial menu [45]. As in Look&Cross we are adopting gaze for pre-selection in menus, but we use manual target crossing for confirmation instead of crossing by head pointer.…”

Section: Alignment Of Input From Separate Pointing Modalitiesmentioning

confidence: 99%

Gaze-Hand Alignment

Lystbæk

Rosenberg

Pfeuffer

et al. 2022

Proc. ACM Hum.-Comput. Interact.

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Gaze and freehand gestures suit Augmented Reality as users can interact with objects at a distance without need for a separate input device. We propose Gaze-Hand Alignment as a novel multimodal selection principle, defined by concurrent use of both gaze and hand for pointing and alignment of their input on an object as selection trigger. Gaze naturally precedes manual action and is leveraged for pre-selection, and manual crossing of a pre-selected target completes the selection. We demonstrate the principle in two novel techniques, Gaze&Finger for input by direct alignment of hand and finger raised into the line of sight, and Gaze&Hand for input by indirect alignment of a cursor with relative hand movement. In a menu selection experiment, we evaluate the techniques in comparison with Gaze&Pinch and a hands-only baseline. The study showed the gaze-assisted techniques to outperform hands-only input, and gives insight into trade-offs in combining gaze with direct or indirect, and spatial or semantic freehand gestures.

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“…Head-gaze interaction is one of the most common approaches, which has been widely studied for target selection (e.g., [2,11,17,21]) and text entry (e.g., [35,37,41]) in VR/AR HMD. As head movements can be performed accurately yet effectively, head-gaze interaction has been used as a standalone approach and combined with or supported by other modalities, such as eyebased interaction [2,11,15,21,26,27,35]. Head-gaze has also been used, for example, to interact in games [1], map interfaces [8], and to construct new interaction methods [6,26,27,38].…”

Section: Head-based Interactionmentioning

confidence: 99%

“…As head movements can be performed accurately yet effectively, head-gaze interaction has been used as a standalone approach and combined with or supported by other modalities, such as eyebased interaction [2,11,15,21,26,27,35]. Head-gaze has also been used, for example, to interact in games [1], map interfaces [8], and to construct new interaction methods [6,26,27,38].…”

Section: Head-based Interactionmentioning

confidence: 99%

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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Radi-Eye: Hands-Free Radial Interfaces for 3D Interaction using Gaze-Activated Head-Crossing

Cited by 28 publications

References 49 publications

HeadText: Exploring Hands-free Text Entry using Head Gestures by Motion Sensing on a Smart Earpiece

HeadText: Exploring Hands-free Text Entry using Head Gestures by Motion Sensing on a Smart Earpiece

Gaze-Hand Alignment

Exploring Head-based Mode-Switching in Virtual Reality

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