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Enregistrement 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 sur OpenAlexfundaboutno aff

Notice bibliographique

RevueEspace École de technologie supérieure (École de technologie supérieure) · 2023
Typeother
Langueen
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesMitacsInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail
Mots-clésAttenuationSelection (genetic algorithm)Variety (cybernetics)Acoustic attenuationQuality (philosophy)Material selectionRange (aeronautics)
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,008
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,074

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,008
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0030,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0220,013

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,038
Tête enseignante GPT0,315
Écart entre enseignants0,277 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission2
Résumé présentoui

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