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Record W7071881976

Tools and methods dedicated to the design and selection of earplugs that are adapted to the user' earcanal morphology and physically comfortable

2023· other· en· W7071881976 on OpenAlexfundaboutno aff

Bibliographic record

VenueEspace École de technologie supérieure (École de technologie supérieure) · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersMitacsInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail
KeywordsAttenuationSelection (genetic algorithm)Variety (cybernetics)Acoustic attenuationQuality (philosophy)Material selectionRange (aeronautics)
DOInot available

Abstract

fetched live from OpenAlex

Disposable and reusable earplugs are widely used to prevent hearing loss in the workplace. To effectively protect users, earplugs must provide adequate sound attenuation and be worn consistently. The attenuation of earplugs depends on many factors, including the morphology of the user's earcanal and the physical characteristics of the earplug, which must be able to fit the earcanal and create an acoustic seal. Even if a proper fit is feasible, discomforts experienced by the wearer can make him/her deteriorate intentionally the fit quality or remove the protector which causes a drastic reduction in protection. Acoustical test fixtures (ATFs), dedicated to earplugs attenuation testing, are equipped with straight cylindrical earcanals of a single size and are therefore unable to assess how well earplugs can fit different earcanal morphologies. An ATF intended to test how earplugs can fit different users (in the designing phase of the earplug for example) should allow for a variety of earcanals shapes. There is thus, a need for more realistic artificial ears available in a variety of sizes and shapes and morphologically representative of targeted populations. In addition, disposable and reusable earplugs are available in a wide variety of shapes and materials, but there is no consensus on a simple and straightforward selection method that will ensure sufficient attenuation for a given worker (field attenuation estimation systems exist but are not widely deployed in the field). It is not known which model and size of earplug is best suited for each unique earcanal, and the packaging of earplugs gives little indication on the subject. Thus, there is a need for methods to select earplugs using simple field-specific tools. Finally, even if an earplug provides the right amount of attenuation to the user when properly fitted, its effectiveness decreases significantly if worn intermittently. One of the main causes of misuse or non-use of earplugs is the discomfort they induce to the user. Discomfort results from interactions between various characteristics of the earplug (e.g., shape or softness), users (e.g., earcanal morphology), and the work environment (e.g., temperature, duration of work shift), which form the triad concept. Knowledge of the relationship between triad characteristics and comfort could help in the design of more comfortable earplugs. This thesis addresses the challenges of (i) designing dedicated tools (artificial ears) for testing and designing earplugs that provide good fit and attenuation to the widest range of earcanal morphologies, (ii) selecting earplugs that fit users' earcanal morphologies using earcanal sizing tools easily accessible in the field, and (iii), understanding the physical discomfort of earplugs by identifying the triad characteristics related to the main attributes of this comfort dimension and assessed in the field. Three papers successively address these challenges. In the first paper, a methodology to cluster earcanals according to their morphology in order to design artificial ears dedicated to the measurement of sound attenuation was developed and applied to a sample of earcanals from Canadian workers. Morphological indicators of earcanals that correlate with the attenuations of six commercial earplug models were first identified. Three clusters of earcanals were then generated using statistical analysis and an artificial intelligence-based algorithm. The clusters differ in the length of the earcanal and in the area and ovality of the cross-section of the first bend. The group with small earcanals and round first bend cross-section shows significantly higher earplug-induced attenuation than the cluster with larger, more oval first bend crosssection. In the second paper, the morphological database constructed in the first paper is compared to earcanal size assessed using the 3MTM Eargage earcanal sizing tool (EST) (which is a simple and inexpensive tool that can be deployed in the field to assess earcanal size). Relationships between the attenuation measured on participants for 6 different earplugs and the earcanal size assessed with the EST are established using box plots and comparison tests. The results show that the EST can help in the selection of earplugs by detecting people with extra-large earcanals who are most likely to be under-protected. In the third paper, the comfort of 7 different models of disposable and reusable earplugs was evaluated in the field with 173 participants exposed daily to noise at their workplace using questionnaires. The characteristics of the triad (person/earplug/environment) were assessed both by questionnaires and in the laboratory by objective measurements. Linear mixed-effects modeling showed that high radial force and friction coefficient of the earplugs promote physical discomfort. In addition, workers found their earplugs less physically uncomfortable if they were accustomed to wearing them before participating in the study. Workers with a large circular earcanal entrance cross-section found their earplugs more physically annoying and painful. Overall, this thesis provides design and selection tools for designing and selecting more physically comfortable earplugs and that are adapted to the user's morphology.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0220.013

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 head0.038
GPT teacher head0.315
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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 routes2
Has abstractyes

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