Character Input in Augmented Reality: An Evaluation of Keyboard Position and Interaction Visualisation for Head-Mounted Displays

Frutos-Pascual, Maite; Gale, Clara; Harrison, Jake Michael; Creed, Chris; Williams, Ian

doi:10.1007/978-3-030-85623-6_29

Cited by 6 publications

(2 citation statements)

References 44 publications

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“…There are also various research studies exploring the input methods in the VR field [31,52,80,84], most of which are based on a physical keyboard [9,21] or virtual keyboard [2,16] for interaction in virtual environments. Many of these interaction methods can also be used in augmented reality [10,17,43,76]. Meanwhile, researchers have developed new keyboards [56] based on hybrid methods [31] and touch-sensitive physical keyboards [47,52,53].…”

Section: Related Work 21 Text Input On Ar Glassesmentioning

confidence: 99%

Handwriting Velcro

Fang

Zhang

Zhan

et al. 2022

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Text input is a desired feature for AR glasses. While there already exist various input modalities (e.g., voice, mid-air gesture), the diverse demands required by different input scenarios can hardly be met by the small number of fixed input postures offered by existing solutions. In this paper, we present Handwriting Velcro, a novel text input solution for AR glasses based on flexible touch sensors. The distinct advantage of our system is that it can easily stick to different body parts, thus endowing AR glasses with posture-adaptive handwriting input. We explored the design space of on-body device positions and identified the best interaction positions for various user postures. To flatten users' learning curves, we adapt our device to the established writing habits of different users by training a 36-character (i.e., A-Z, 0-9) recognition neural network in a human-in-the-loop manner. Such a personalization attempt ultimately achieves a low error rate of 0.005 on average for users with different writing styles. Subjective feedback shows that our solution has a good performance in system practicability and social acceptance. Empirically, we conducted a heuristic study to explore and identify the best interaction Position-Posture Correlation. Experimental results show that our Handwriting Velcro excels similar work [6] and commercial product in both practicality (12.3 WPM) and user-friendliness in different contexts.

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Section: Related Work 21 Text Input On Ar Glassesmentioning

confidence: 99%

Handwriting Velcro

Fang

Zhang

Zhan

et al. 2022

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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“…and hand-based input [6,7,22], gaze or head-based input [15,26] and novel device-based input [12]. Other efforts have focused on improving the design of a virtual keyboard by optimizing its location and orientation [10,24]. Research has also examined different methods for improving entry rates, accuracy and user experience such as by integrating speech recognition [1] and tactile feedback [13].…”

Section: Related Workmentioning

confidence: 99%

Efficient Special Character Entry on a Virtual Keyboard by Hand Gesture-Based Mode Switching

Song

Dudley

Kristensson

2022

2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)

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The need to support efficient text input in virtual reality continues to attract significant research attention. However, much of this research understandably focuses exclusively on core text input tasks involving the entry of standard alphabetic characters. The less common though still critical task of entering special characters is often ignored. In this paper we focus on this niche use case as chiefly encountered when entering passwords. Current commercial virtual keyboards allow users to switch between different layers of the keyboard in order to access capital letters, numerals and special characters by pressing an explicit mode-switch button. We propose a new method of switching between layers of a virtual keyboard using hand gestures. Critically, these hand gestures are seamlessly performed in conjunction with key selections to deliver an efficient and intuitive interaction. We report on a user study with 16 participants entering standard passwords comparing our gesture-based mode switching approach to a conventional button-based baseline. We find that with practice our proposed method results in significantly faster entry rates without any deterioration in accuracy. Feedback from users also indicates that our technique is considered efficient and comfortable to use.

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