Evidence map›Paper›PMID 39663718›Full record

ArticleAdvanced healthcare materials2025

In-Ear Electronics with Mechanical Adaptability for Physiological Sensing.

Shuyun Zhuo, Zihuan Wu, Chris Williams, Chithiravel Sundaresan, Shideh Kabiri Ameri

Abstract read
In one paragraph

Article in Advanced healthcare materials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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

5 authors.

Shuyun ZhuoDepartment of Electrical and Computer Engineering, Queen's University, Kingston, ON, K7L 3N6, Canada.
Zihuan WuDepartment of Electrical and Computer Engineering, Queen's University, Kingston, ON, K7L 3N6, Canada.
Chris WilliamsDepartment of Electrical and Computer Engineering, Queen's University, Kingston, ON, K7L 3N6, Canada.
Chithiravel SundaresanDepartment of Electrical and Computer Engineering, Queen's University, Kingston, ON, K7L 3N6, Canada.
Shideh Kabiri AmeriDepartment of Electrical and Computer Engineering, Queen's University, Kingston, ON, K7L 3N6, Canada.ORCID 0000-0001-6931-3011

Funding

Canada Foundation for Innovation 40336NSERC Alliance Missions grant 570743-2021
6 · The paper itself

Abstract

Significant developments have been made in the field of wearable healthcare by utilizing soft materials for the construction of electronic sensors. However, the lack of adaptability to complex topologies, such as ear canal, results in inadequate sensing performance. Here, we report an in-ear physiological sensor with mechanical adaptability, which softens upon contact with the ear canal's skin, thus reducing the sensor-skin mechanical mismatch and interface impedance. An efficient strategy of mechanical adjustment and switching is exploited to increase the softness of the device, leading to a significant decrease in Young's modulus from 30.5 MPa of thermoplastic polyurethane (TPU) to 0.86 MPa of TPU/Ecoflex foam (TEF).The mechanical adaptability at body temperature endows the in-ear device improved device-canal contact area and interface stability. As a result, the TEF-based in-ear device demonstrates reliable sensing, low motion artifact, and high comfort in electroencephalography (EEG) and core body temperature sensing. High quality EEG signals of alpha, beta, delta, and gamma are measured during different activities. Moreover, the TEF-based in-ear device exhibits high reusability for over 4 months, which makes it suitable for long-term healthcare monitoring.

Indexed as

Wearable Electronic DevicesElectroencephalographyElectronicsHumansMonitoring, PhysiologicPolyurethanesPolyurethanesin‐ear electronicsmechanical switchingphysiological sensingreusablesensor‐skin interfacestiffness adaptability

Identifiers

PMID39663718
PMCPMC11773109

What OpenQuestion holds

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Registered trials

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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.