MétaCan
Menu
Back to cohort
Record W3141201723 · doi:10.1109/tim.2021.3069834

An RFID-Based Assistive Glove to Help the Visually Impaired

2021· article· en· W3141201723 on OpenAlexaff
Paniz Sedighi, Mohammad Hesam Norouzi, Mehdi Delrobaei

Bibliographic record

VenueIEEE Transactions on Instrumentation and Measurement · 2021
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsWestern University
FundersIran National Science Foundation
KeywordsHuman–computer interactionWearable computerComputer scienceAssistive technologyPerceptionRadio-frequency identificationWired gloveIdentification (biology)BlindnessWearable technologyAssistive deviceComputer visionSonificationSensory substitutionArtificial intelligenceEmbedded systemPhysical medicine and rehabilitationVirtual realityPsychologyComputer security

Abstract

fetched live from OpenAlex

Recent studies have focused on facilitating perception and outdoor navigation for people who live with blindness or some form of vision loss. However, a significant portion of these studies is centered around treatment and vision rehabilitation, leaving some immediate needs, such as interaction with the surrounding objects or recognizing colors and fine patterns without tactile feedback. This study targets such needs and delivers a straightforward communication method with the environment using a wearable, unobtrusive device. We initially discuss the advantages and limitations of related works to draw out the best-fitting design concepts. Then, we introduce the potential for emerging technologies such as radio frequency identification. We present the design details and the experimental results of an assistive glove to allow people with vision disabilities to interact with the environment more efficiently. Based on the collected data from 17 blindfolded healthy participants, the implemented system’s success rate in identifying objects was about 96.32%. Overall, 70% of the users found the device very satisfactory.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.509

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 studies0.0010.000
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 head0.076
GPT teacher head0.319
Teacher spread0.243 · 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

Citations22
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Instrumentation and MeasurementSame topicTactile and Sensory InteractionsFrench-language works237,207