In-ear earphone design-oriented pressure sensitivity evaluation on the external ear

Yan, Yan; Liu, Yonghong; Jiang, Rui; Liu, Kexiang; Du, Yujia; Wang, Haining

doi:10.1080/00140139.2022.2146759

Cited by 1 publication

(1 citation statement)

References 36 publications

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“…Eight regions of the ear, including concha, anthelix, antitragus, crux of helix, notch above tragus, tragus, incisura intertragica and external auditory canal, were partitioned 32 , which is shown in Fig. 6.…”

Section: Evaluation Indexmentioning

confidence: 99%

An earphone fit deviation analysis algorithm

Yan

Liu

Wang

2023

Sci Rep

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This study provides an accurate method for evaluating the fit of earphones, which could be used for establishing a linkage between interference/gap values with human perception. Seven commercial CAD software tools stood out and were explored for the analysis of the deviation between earphone and ear. However, the current deviation analysis method remains to be improved for earphone fit evaluation due to excessive points in the calculation (Geomagic Wrap and Siemens NX), lack of value on interference (Geomagic Control X), computation boundary required (Rapidform XOR/Redesign), repetitive computation with same points and inclined calculation line segment or even invalid calculation (Solidworks, Creo). Therefore, an accurate deviation analysis algorithm was promoted, which calculated the deviation between earphone and ear exactly and classified the interference set and gap set precisely. There are five main procedures of this algorithm, which are point cloud model pre-processing, the generation of distance vectors, the discrimination of interference set and gap set, the discrimination of validity, and statistical analysis and visualization. Furthermore, the usability and validity of the deviation analysis algorithm were verified through statistical analysis and comparing visual effects based on the earphone-wearing experiment. It is certified that the deviation analysis algorithm is appropriate for earphone fit evaluation and the eight indexes of this algorithm were proved to be related to subjective comfort scores. It is meaningful for ear-worn product fit analysis, design, and development phases.

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Section: Evaluation Indexmentioning

confidence: 99%