When Smart Devices Interact With Pervasive Screens

Ng, Pai Chet; She, James; Jeon, Kang Eun; Baldauf, Matthias

doi:10.1145/3115933

Cited by 34 publications

(13 citation statements)

References 63 publications

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“…Dari hasil pengukuran yang telah dilakukan disimpulkan bahwa perlu dikembangkan lebih lanjut. Dengan demikian, penelitian ini melanjutkan pekerjaan pada fase pemasangan perangkat dan modalitas interaksi seperti yang didefinisikan dalam [10].…”

Section: Pendahuluanunclassified

Pembuatan File Input berbasis Chat untuk Screen-Smart Device Interaction (SSI)

Assidiqi

Fudhola²,

Muhammad³

et al. 2021

J-TIT

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Public display memiliki potensi untuk menjadi media diseminasi informasi kepada publik. Sayangnya, mayoritas public display memiliki kecenderungan untuk diabaikan oleh orang-orang yang melewatinya. Beberapa riset menunjukkan bahwa pengguna akan melihat sebuah public display jika dapat berinteraksi dengan display tersebut. Salah satu pendekatannya adalah Screen-Smart Device Interaction (SSI). Pada penelitian ini, dikembangkan sebuah interaksi untuk menampilkan sebuah file dari smart device ke sebuah display menggunakan antar muka berbasis Chat. Pengujian dilakukan dengan alat ukur SUS dan UEQ-S. Dari pengujian diperoleh kesimpulan bahwa hasil yang dikembangkan termasuk kategori acceptable dengan skor SUS 72.8. Akan tetapi dari UEQ-S diperoleh skor hedonic yang lebih rendah dari pragmatic, yang artinya perlu pengembangan lebih lanjut terkait tampilan dari prototype yang diujikan.

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Section: Pendahuluanunclassified

Pembuatan File Input berbasis Chat untuk Screen-Smart Device Interaction (SSI)

Assidiqi

Fudhola²,

Muhammad³

et al. 2021

J-TIT

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“…Apart from implicit interaction, a large body of work investigated explicit input through modalities beyond keys and buttons, such as touch [13], mid-air gestures [14], feet-input [15], mobile devices [16], eye gaze [17], or multi-modal combinations of them [18].…”

Section: Interacting With Public Displaysmentioning

confidence: 99%

Predicting mid-air gestural interaction with public displays based on audience behaviour

Gentile

Khamis

Milazzo

et al. 2020

International Journal of Human-Computer Studies

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Knowledge about the expected interaction duration and expected distance from which users will interact with public displays can be useful in many ways. For example, knowing upfront that a certain setup will lead to shorter interactions can nudge space owners to alter the setup. If a system can predict that incoming users will interact at a long distance for a short amount of time, it can accordingly show shorter versions of content (e.g., videos/advertisements) and employ at-a-distance interaction modalities (e.g., mid-air gestures). In this work, we propose a method to build models for predicting users' interaction duration and distance in public display environments, focusing on mid-air gestural interactive displays. First, we report our findings from a field study showing that multiple variables, such as audience size and behaviour, significantly influence interaction duration and distance. We then train predictor models using contextual data, based on the same variables. By applying our method to a mid-air gestural interactive public display deployment, we build a model that predicts interaction duration with an average error of about 8 s, and interaction distance with an average error of about 35 cm. We discuss how researchers and practitioners can use our work to build their own predictor models, and how they can use them to optimise their deployment.

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“…Ng et al [22] present a survey on screen-smart device interaction (SSI), which also covers methods for content transfer. Two general method types are recognized: vision-based and radio-based.…”

Section: Related Workmentioning

confidence: 99%

“…Of vision-based methods, QR codes have been used actively [1,10,22], which allow smartphone users to scan a code using the device-integrated camera, to receive content such as a link to a website. Of radio-based methods, near-field communication (NFC) technology has been utilized for content transfer [2,7,22,26]. For instance, Hardy and Rukzio [7] attached individual NFC tags behind each content item, thereby allowing items to be transferred by touching the corresponding item with a mobile device.…”

Section: Related Workmentioning

confidence: 99%

Pocket Transfers

Mäkelä

Khamis

Mecke

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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We present Pocket Transfers: interaction techniques that allow users to transfer content from situated displays to a personal mobile device while keeping the device in a pocket or bag. Existing content transfer solutions require direct manipulation of the mobile device, making interaction slower and less flexible. Our introduced techniques employ touch, midair gestures, gaze, and a multimodal combination of gaze and mid-air gestures. We evaluated the techniques in a novel user study (N=20), where we considered dynamic scenarios where the user approaches the display, completes the task, and leaves. We show that all pocket transfer techniques are fast and seen as highly convenient. Mid-air gestures are the most efficient touchless method for transferring a single item, while the multimodal method is the fastest touchless method when multiple items are transferred. We provide guidelines to help researchers and practitioners choose the most suitable content transfer techniques for their systems.

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When Smart Devices Interact With Pervasive Screens

Cited by 34 publications

References 63 publications

Pembuatan File Input berbasis Chat untuk Screen-Smart Device Interaction (SSI)

Pembuatan File Input berbasis Chat untuk Screen-Smart Device Interaction (SSI)

Predicting mid-air gestural interaction with public displays based on audience behaviour

Pocket Transfers

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