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Exploring 2D, 3D and Spatial Audio User Interfaces in VR for Reminiscence Therapy

2023· article· en· W4388727214 on OpenAlexaff
Álvaro Uribe-Quevedo, Camilo Arevalo, Julián Villegas, Winnie Sun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsUsabilityHuman–computer interactionComputer scienceMultimediaReminiscenceAudio feedbackUser experience designCognitive loadVirtual realityUser interfaceInterface (matter)CognitionPsychologyEngineering

Abstract

fetched live from OpenAlex

Reminiscence Therapy (RT) is a non-pharmacological treatment focused on helping individuals with dementia recollect memories through their senses. Traditionally, RT therapy has relied on multimedia, where caregivers choose videos, pictures, and audio files. Since cross-sensory stimuli play an important role during RT, the adoption of Virtual Reality (VR) has spiked as the technology becomes commonplace. Our previous work has focused on prototyping a VR RT tool through co-design and usability testing, utilizing hand tracking for ease of use. In this research, we aim to further understand usability, presence, and cognitive load effects by comparing a 2D Graphical User Interface (GUI), a radio-style 3D User Interface (3DUI), a curved-style 3DUI, and a spatial audio User Interface UI that allows playing, stopping, and adjusting the volume of an audio file. Our preliminary study indicates that a traditional 2D GUI has higher usability, and lower cognitive load, while a curved-style 3DUI with 3D push buttons and a slider for volume around the user has a higher presence than the radio-style 3DUI and the spatial audio UI.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.238

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.171
GPT teacher head0.317
Teacher spread0.146 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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