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Future Internet 2017, 9(3), 40; doi:10.3390/fi9030040

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

Department of Computer Science, Central Michigan University, Mount Pleasant, MI 48859, USA
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Received: 30 June 2017 / Revised: 17 July 2017 / Accepted: 24 July 2017 / Published: 28 July 2017
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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. View Full-Text
Keywords: augmented reality; quality of experience; quality of service; electroencephalography; image quality augmented reality; quality of experience; quality of service; electroencephalography; image quality
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Bauman, B.; Seeling, P. Visual Interface Evaluation for Wearables Datasets: Predicting the Subjective Augmented Vision Image QoE and QoS. Future Internet 2017, 9, 40.

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