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Record W4315695094 · doi:10.2196/40651

Negami: An Augmented Reality App for the Treatment of Spatial Neglect After Stroke

2023· article· en· W4315695094 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR Serious Games · 2023
Typearticle
Languageen
FieldNeuroscience
TopicSpatial Neglect and Hemispheric Dysfunction
Canadian institutionsnot available
Fundersnot available
KeywordsVirtual realityAugmented realityUsabilityNeglectContext (archaeology)PsychologySpatial contextual awarenessStroke (engine)Physical medicine and rehabilitationComputer scienceMedicineHuman–computer interactionArtificial intelligencePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: A widely applied and effective rehabilitation method for patients experiencing spatial neglect after a stroke is "visual exploration training." Patients improve their ipsilesional bias of attention and orientation by training exploration movements and search strategies toward the contralesional side of space. In this context, gamification can have a positive influence on motivation for treatment and thus on the success of treatment. In contrast to virtual reality applications, treatment enhancements through augmented reality (AR) have not yet been investigated, although they offer some advantages over virtual reality. OBJECTIVE: This study aimed to develop an AR-based app (Negami) for the treatment of spatial neglect that combines visual exploration training with active, contralesionally oriented rotation of the eyes, head, and trunk. METHODS: The app inserts a virtual element (origami bird) into the real space surrounding the patient, which the patient explores with the camera of a tablet. Subjective reports from healthy elderly participants (n=10) and patients with spatial neglect after stroke (n=10) who trained with the new Negami app were analyzed. Usability, side effects, and game experience were assessed by various questionnaires. RESULTS: Training at the highest defined difficulty level was perceived as differently challenging but not as frustrating by the group of healthy elderly participants. The app was rated with high usability, hardly any side effects, high motivation, and entertainment. The group of patients with spatial neglect after stroke consistently evaluated the app positively on the dimensions of motivation, satisfaction, and fun. CONCLUSIONS: The Negami app represents a promising extension by adding AR to traditional exploration training for spatial neglect. Through participants' natural interaction with the physical surrounding environment during playful tasks, side effects as symptoms of cybersickness are minimized and patients' motivation appeared to markedly increase. The use of AR in cognitive rehabilitation programs and the treatment of spatial neglect seems promising and should receive further investigation.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.475

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 head0.033
GPT teacher head0.310
Teacher spread0.277 · 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