Attention-based design of augmented reality interfaces

Bonanni, Leonardo; Lee, Chia-Hsun; Selker, Ted

doi:10.1145/1056808.1056883

Cited by 74 publications

(39 citation statements)

References 6 publications

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“…Common device types perform either monocular [17] or binocular [18] vision augmentation by presenting content to the device operator; see, e.g., [19] for an overview. As operator performance augmentation is typically the goal behind AR content display, evaluations such as in [20][21][22] showcase issues for the various system types. Perceptual issues for these content presentation approaches have been evaluated in the past, as well, such as for item segmentation [23], depth perception [24], contrast and color perception [25] or the field-of-view [26].…”

Section: Related Workmentioning

confidence: 99%

Visual Interface Evaluation for Wearables Datasets: Predicting the Subjective Augmented Vision Image QoE and QoS

Bauman

Seeling

2017

Future Internet

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Abstract:As Augmented Reality (AR) applications become commonplace, the determination of a device operator's subjective Quality of Experience (QoE) in addition to objective Quality of Service (QoS) metrics gains importance. Human subject experimentation is common for QoE relationship determinations due to the subjective nature of the QoE. In AR scenarios, the overlay of displayed content with the real world adds to the complexity. We employ Electroencephalography (EEG) measurements as the solution to the inherent subjectivity and situationality of AR content display overlaid with the real world. Specifically, we evaluate prediction performance for traditional image display (AR) and spherical/immersive image display (SAR) for the QoE and underlying QoS levels. Our approach utilizing a four-position EEG wearable achieves high levels of accuracy. Our detailed evaluation of the available data indicates that less sensors would perform almost as well and could be integrated into future wearable devices. Additionally, we make our Visual Interface Evaluation for Wearables (VIEW) datasets from human subject experimentation publicly available and describe their utilization.

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Section: Related Workmentioning

confidence: 99%

Visual Interface Evaluation for Wearables Datasets: Predicting the Subjective Augmented Vision Image QoE and QoS

Bauman

Seeling

2017

Future Internet

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“…We have demonstrated an augmented reality kitchen in which text projected on appliances distracted people. However, people's performance was improved with ecological feedback such as waves projected above the sink, fire above a burner and the sound of a cold wind when a freezer opened [65]. The surface characteristics of feedback presentation deeply affected its usefulness.…”

Section: Beyond Usable Systemsmentioning

confidence: 99%

Understanding Considerate Systems - UCS (Pronounced: You See Us)

Selker

2011

2011 International Conference on Technologies and Applications of Artificial Intelligence

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“…Apesar do aumento na utilização de ambientes de RA, eles apresentam, principalmente, no ensino e na aprendizagem, um grande desafio: o de projetar interfaces para o usuário, pois projetistas de RA não têm estabelecido um conjunto de diretrizes ou heurísticas para ajudar na implementação (JEONS, SHIM, 2006;KULAS, KLINKER, 2004;FERNANDES, SANCHES, 2008;VANDERDONCKT, CHIEU, 2004;BOANANNI, LEE, 2005). Segundo Wang e Reeves (2007), a usabilidade não tem recebido um foco adequado, visto que não se volta para os diferentes usuários e suas diferentes capacidades na utilização das tecnologias.…”

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