Augmenting Human Cognition with Adaptive Augmented Reality

Doswell, Jayfus T.; Skinner, Anna

doi:10.1007/978-3-319-07527-3_10

Cited by 19 publications

(2 citation statements)

References 23 publications

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“…(2020) proposed a novel multi-indicator evaluation model for mixed reality surgical navigation systems that evaluate the user’s perception in regards to safety, comfort and efficiency and combines subjective and objective evaluation criteria. Doswell and Skinner (2014) identified the need for HMD-based, scientifically grounded methods that identify HCI related interaction modalities to be addressed to optimise user performance and cognitive load. These comprise information presentation, user input and system feedback.…”

Section: Discussionmentioning

confidence: 99%

Utility of optical see-through head mounted displays in augmented reality-assisted surgery: A systematic review

Birlo

Edwards

Clarkson

et al. 2022

Medical Image Analysis

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Section: Discussionmentioning

confidence: 99%

Utility of optical see-through head mounted displays in augmented reality-assisted surgery: A systematic review

Birlo

Edwards

Clarkson

et al. 2022

Medical Image Analysis

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“…These technologies are being researched and implemented in different domains, such as medical training, commercial VR, collaborative and remote workspaces, rehabilitation, and serious games [7]. Cognitive load is one of the most effective types of data fueling self-adaptive technologies [9], [12]- [16]. This project is built on the same perspective with the feedback loop depicted in Fig.…”

Section: Introductionmentioning

confidence: 99%

Predicting Cognitive Failures in Virtual Reality Using Pupillometry

Niknam,

Botev

2024

2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)

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Pupil dilation has consistently been investigated and confirmed as a reliable measure of cognitive load. In this study, we aim to explore the possibility of predicting cognitive failures in Virtual Reality by monitoring variations in pupil dilation during cognitive processing. To this end, we collected eye-tracking data from an individual performing a mental arithmetic task over two months, totaling 700 minutes. We achieved promising prediction results by training a neural network on the collected data, particularly considering the dataset's imbalanced nature. The ability to predict impending cognitive failures generally holds significant implications across various domains, including education, delegating decision-making tasks to autonomous systems, or self-adaptive virtual environments and user interfaces.

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