Evidence map›Paper›PMID 36658281›Full record

ArticleScientific reports2023

An earphone fit deviation analysis algorithm.

Yan Yan, Yonghong Liu, Haining Wang

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.8field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Yan YanSchool of Design, Hunan University, Changsha, 410082, China.
Yonghong LiuSchool of Design, Hunan University, Changsha, 410082, China.
Haining WangSchool of Design, Hunan University, Changsha, 410082, China. wanghn@hnu.edu.cn.
Hunan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Hearing AidsAlgorithmsHumansSoftware

Identifiers

PMID36658281
PMCPMC9852584
OpenAlexW4317435040

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.