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Record W3140173391 · doi:10.1115/1.4050186

An Inconspicuous, Integrated Electronic Travel Aid for Visual Impairment

2021· article· en· W3140173391 on OpenAlexfundno aff
Alain Boldini, Andy Louis Garcia, Marc Sorrentino, Mahya Beheshti, Okpe Ogedegbe, Yi Fang, Maurizio Porfiri, John‐Ross Rizzo

Bibliographic record

VenueASME Letters in Dynamic Systems and Control · 2021
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsnot available
FundersYork UniversityDivision of Civil, Mechanical and Manufacturing InnovationNew York UniversityNational Science Foundation
KeywordsLeverage (statistics)Wearable computerVisual impairmentComputer scienceHuman–computer interactionPopulationObstacleAssistive technologyWearable technologyComputer visionArtificial intelligenceMedicineEmbedded system

Abstract

fetched live from OpenAlex

Abstract With a globally aging population, visual impairment is an increasingly pressing problem for our society. Visual disability drastically reduces quality of life and constitutes a large cost to the health care system. Mobility of the visually impaired is one of the most critical aspects affected by this disability, and yet, it relies on low-tech solutions, such as the white cane. Many avoid solutions entirely. In part, reluctance to use these solutions may be explained by their obtrusiveness, a strong deterrent for the adoption of many new devices. Here, we leverage new advancements in artificial intelligence, sensor systems, and soft electroactive materials toward an electronic travel aid with an obstacle detection and avoidance system for the visually impaired. The travel aid incorporates a stereoscopic camera platform, enabling computer vision, and a wearable haptic device that can stimulate discrete locations on the user’s abdomen to signal the presence of surrounding obstacles. The proposed technology could be integrated into commercial backpacks and support belts, thereby guaranteeing a discreet and unobtrusive solution.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.954
Threshold uncertainty score0.653

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 head0.007
GPT teacher head0.265
Teacher spread0.258 · 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 designBench or experimental
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

Citations19
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueASME Letters in Dynamic Systems and ControlSame topicTactile and Sensory InteractionsFrench-language works237,207