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Record W4317105980 · doi:10.18280/jesa.550608

Development of a Prototype Sensory Device as a Substitute for Single Sided Deaf People in Developing Nations

2022· article· en· W4317105980 on OpenAlexvenueno aff
Adedotun O. Adetunla, Olanrewaju Kolade, Adeyinka M. Adeoye, Saheed Akande

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2022
Typearticle
Languageen
FieldComputer Science
TopicHand Gesture Recognition Systems
Canadian institutionsnot available
Fundersnot available
KeywordsSensory systemSensory substitutionDecibelPerceptionHearing lossAudiologySensory lossComputer scienceNeurosciencePsychologyCommunicationMedicine

Abstract

fetched live from OpenAlex

Hearing loss is the inability to hear sounds ranging from 20 decibels or more in one or both ears. It can affect one or both ears and leads to difficulty in hearing speech or sounds in general. Single-sided deafness or unilateral hearing loss is a very widespread disability. However, most people only see hearing loss as being a binary problem assuming that you either have perfect hearing in both ears or are completely deaf in both ears, and dismiss the other types of hearing loss. Sensory substitution involves remapping the information gathered by one sensory receptor to another. Sensory receptors regardless of the signals they receive or capture, all encode the gathered information as electrochemical signals. This biological property of sensory receptors, coupled with the human brain’s neuroplasticity allows sensory receptors to be substituted, giving rise to new methods of sensory perception. This study aims to develop a sensory device known as a localizer. The localizer detects sound using numerous sound sensors, and feeds the input to the microcontrollers which then use the input to control the eccentric mass motor by implementing various motor drivers. The results gotten from this prototype device shows great improvement in the ability of a single-sided deaf person to localize sound.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.721
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.052
GPT teacher head0.289
Teacher spread0.237 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

Explore more

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