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Work item:
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J.CLE-ARVR
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Subject/title:
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Terminology, Metrics and Functional requirements for Cognitive Load Estimation for Augmented and Virtual Reality (AR/VR) services
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Status:
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Under study
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Approval process:
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AAP
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Type of work item:
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Recommendation
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Version:
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New
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Equivalent number:
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-
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Timing:
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2025-11 (Medium priority)
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Liaison:
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-
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Supporting members:
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-
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Summary:
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Cognitive load estimation has a rich history, although its exact definition is often debated among scientists. The most popular theory on cognitive load originates from the education technology sector. However, in much of the literature, cognitive load is estimated through comparison or correlation of various physiological parameters, with electroencephalography (EEG) and eye gaze metrics being the most common. While cognitive load analysis has been explored under various names such as behaviour monitoring and drowsiness detection in automotive and aviation sectors, recent advancements in augmented and virtual reality (AR/VR) present new challenges and opportunities for cognitive load estimation technology.
Major industries use various terms like Metaverse (Facebook), Mesh (Microsoft), and Nth Floor (Accenture) to invest in and commercialize immersive media-related products. Traditionally, immersive media is described along a continuum between reality and virtual reality, with intermediate systems known as Augmented and Mixed Reality systems. The International Standardization Organization defines mixed and augmented reality as systems that use a mixture of representations of physical world data and virtual world data as their presentation medium. Amidst commercialization efforts and advertisements from software giants, end-users often find themselves confused about the best solution for their needs. There is a lack of studies analyzing end-users’ feedback across the continuum of immersive media. Cognitive load estimation for AR/VR systems often requires parameters measurable by standalone devices with higher external validity. Unlike many EEG studies, high-end EEG systems may not be useful, as they often require sophisticated grounding.
By understanding cognitive load in immersive media experiences, developers can create more accessible audio-visual content that caters to the needs of users with varying cognitive abilities, ultimately enhancing inclusivity and usability.
In this context, the proposed new ITU-T Recommendation J.CLE-ARVR aims to investigate how improving accessibility in audio-visual content can be integrated into AR/VR systems, and proposes to outline the following:
Definition of terms for cognitive load estimation and related concepts such as stress, boredom, and engagement.
Metrics for estimating cognitive load in AR/VR systems.
Acceptance criteria for cognitive load estimation systems in AR/VR applications.
Minimum technical specifications for devices used in cognitive load estimation.
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Comment:
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-
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Reference(s):
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Historic references:
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Contact(s):
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First registration in the WP:
2024-05-30 13:40:35
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Last update:
2024-09-18 14:14:26
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