Designing earplugs adapted for the widest number of earcanals requires acoustical test fixtures (ATFs) geometrically representative of the population. Most existing ATFs are equipped with unique sized straight cylindrical earcanals, considered representative of average human morphology, and are therefore unable to assess how earplugs can fit different earcanal morphologies. In this study, a methodology to cluster earcanals as a function of their morphologies with the objective of designing artificial ears dedicated to sound attenuation measurement is developed and applied to a sample of Canadian workers’ earcanals. The earcanal morphologic indicators that correlate with the attenuations of six models of commercial earplugs are first identified. Three clusters of earcanals are then produced using statistical analysis and an artificial intelligence-based algorithm. In the sample of earcanals considered in this study, the identified clusters differ by the earcanal length and by the surface and ovality of the first bend cross section. The cluster that comprises earcanals with small girth and round first bend cross section shows that earplugs induced attenuation significantly higher than the cluster that includes earcanals with a bigger and more oval first bend cross section.
How in-ear devices fit in the ear strongly influences the acoustical and mechanical (dis)comforts induced to the wearer. As important variations in the ear geometry exist based on gender, age and ethnicity, several studies collected ear anthropometric data as a basis for designing ear-mounted products. However, most of these studies focused on the ergonomic design of earbuds, and are thus limited to the geometry of the pinna, and concha (where earbuds fit). Few studies explored geometrical earcanal data for the design of intra-auricular hearing protectors that fit up to the earcanal second bend. The design of earplugs that fit to the widest range of earcanals requires realistic acoustical test fixtures representative of the population. This study uses statistical analysis and artificial intelligence based algorithms to cluster 32 Canadian workers pairs of earmolds scans as a function of earcanal curvilinear axis length, entrance, first and second bend cross sections area and circumference, but also earcanal tortuosity and cross sections aspect ratios. Dimensions relevant to cluster earcanals will be collected on a hundred of ears in a future study to design artificial ears which capture the inter-individual variability in mechanical and acoustical objective indicators related to the most important earplugs (dis)comfort attributes.
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