DepthText: Leveraging Head Movements towards the Depth Dimension for Hands-free Text Entry in Mobile Virtual Reality Systems

Lu, Xueshi; Yu, Difeng; Liang, Hai-Ning; Feng, Xiyu; Xu, Wenge

doi:10.1109/vr.2019.8797901

Cited by 20 publications

(19 citation statements)

References 6 publications

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“…Usually, head gestures are used for pointing and assisting key selection in AR/VR Head-Mounted Displays [41,46]. DepthText [27] leveraged head movements towards the depth dimension for hands-free text entry in mobile virtual reality systems. RingText [47] presented a dwell-free and hands-free text entry technique for mobile head-mounted displays using head motions.…”

Section: Hands-free and Gesture-based Text Entrymentioning

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 and Gesture-based Text Entrymentioning

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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“…NeckType leverages the forward (z-axis) acceleration movements, which can be accurately captured by the built-in IMU sensor of current HMDs, as the letter selection mechanism. NeckType is an enhanced version of DepthText [19,42] and employed the Dynamic time warping (DTW) [3] algorithm for input recognition. To perform a selection with NeckType, users first moved the pointer on a target key with the head rotation and confirmed the character by moving the head forward for about 1cm, which the human neck can do with ease [19].…”

Section: Necktypementioning

confidence: 99%

“…NeckType is an enhanced version of DepthText [19,42] and employed the Dynamic time warping (DTW) [3] algorithm for input recognition. To perform a selection with NeckType, users first moved the pointer on a target key with the head rotation and confirmed the character by moving the head forward for about 1cm, which the human neck can do with ease [19].…”

Section: Necktypementioning

confidence: 99%

Exploration of Hands-free Text Entry Techniques For Virtual Reality

Liang

et al. 2020

Preprint

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Text entry is a common activity in virtual reality (VR) systems. There is a limited number of available hands-free techniques, which allow users to carry out text entry when users' hands are busy such as holding items or hand-based devices are not available. The most used hands-free text entry technique is DwellType, where a user selects a letter by dwelling over it for a specific period. However, its performance is limited due to the fixed dwell time for each character selection. In this paper, we explore two other hands-free text entry mechanisms in VR: BlinkType and NeckType, which leverage users' eye blinks and neck's forward and backward movements to select letters. With a user study, we compare the performance of the two techniques with DwellType. Results show that users can achieve an average text entry rate of 13.47, 11.18 and 11.65 words per minute with BlinkType, NeckType, and DwellType, respectively. Users' subjective feedback shows BlinkType as the preferred technique for text entry in VR.

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“…Assim, a Tabela 4 apresenta uma síntese comparativa de características específicas entre: as técnicas padrão de entrada de texto para RV mobile (ray-casting), as técnicas apresentadas pelos trabalhos correlatos e a técnica proposta pelo presente trabalho. Vale mencionar que as técnicas de entrada de texto baseadas em reconhecimento de voz possuem limitações relacionadas a edição de texto (GRUBERT et al, 2018b) e a seu uso em ambientes públicos: estas técnicas podem causar problemas de privacidade aos usuários e tais ambientes com barulho podem prejudicar o reconhecimento de voz (XU et al, 2019;LU et al, 2019;GRUBERT et al, 2018b). Ainda, técnicas baseadas em reconhecimento de voz podem não ser precisas ao tentar reconhecer siglas, palavras novas ou palavras em outras línguas.…”

Section: Trabalhos Correlatosunclassified

“…Entre os três trabalhos identificados como fortemente correlatos, dois estudos apresentaram técnicas alternativas para entrada de texto para RV mobile (XU et al, 2019;LU et al, 2019) e um para Realidade Mista (RM) (KNIERIM et al, 2020). A técnica e desempenho do usuário.…”

Section: Trabalhos Correlatosunclassified

Técnica de entrada de texto por meio de um teclado ambíguo controlado por uma mão para realidade virtual baseada em smartphones

Palmeira¹

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Faço um agradecimento primeiramente à Deus que, em sua graça e misericórdia, me deu o sonho, a oportunidade e a capacitação. Sou grato, também, ao meu pai, Eduardo Telles Palmeira e à minha mãe, Odaque Queiroz Palmeira, pelo amor, investimento e confiança. Agradeço de modo particular ao meu pai, que teve sua formação acadêmica na primeira turma de Engenharia Elétrica com ênfase em Computação da UFU (1990) e me apresentou aos meus orientadores. Ainda, agradeço à ele pela colaboração nesse projeto.Agradeço, também, especialmente à minha irmã Anna Carollina Queiroz Palmeira, que admiro muito, pelos conselhos de vida e inspiração. À minha noiva Aline Megumi Matsushita, por compartilhar seus sonhos comigo. Ao meu sogro, Raul Matsushita, pelos conselhos sobre a carreira acadêmica que escolhi seguir. À minha tia, Maria Cione Chrisóstomo, por me apoiar e abrigar em sua casa por alguns meses. Aos meus primos, Lucas Gonçalves e Isabella Telles, pela companhia e suporte. Aos meus amigos de laboratório, Victor Saint Martin, Ígor Moraes, Victor Barbosa, Júlio Melazzo, Leonardo Nery e Gustavo Pacheco, pela colaboração e amizade. Agradeço também aos professores Alexandre Cardoso, Edgard Lamounier e Regis Kopper pelos ensinamentos e orientações; a todos do Grupo de Realidade Virtual e Aumentada (GRVA) da UFU, por seus eventuais conselhos; e a todos que, mesmo não estando citados aqui, indiretamente tornaram possível a realização desse trabalho -sem vocês nada disso seria possível.Por fim, agradeço ao apoio da Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) por fornecer o fomento necessário para a realização do meu projeto.

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DepthText: Leveraging Head Movements towards the Depth Dimension for Hands-free Text Entry in Mobile Virtual Reality Systems

Cited by 20 publications

References 6 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

Exploration of Hands-free Text Entry Techniques For Virtual Reality

Técnica de entrada de texto por meio de um teclado ambíguo controlado por uma mão para realidade virtual baseada em smartphones

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