Evidence map›Paper›PMID 41486555›Full record

ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Artificial Intelligence-Driven Soft Bioelectronics for Self-Powered Respiration Monitoring.

Xinkai Xu, Xiao Xiao, Rui Guo, Jun Chen

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Artificial Intelligence-Driven Soft Bioelectronics for Self-Powered Respiration Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
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

4 authors.

Xinkai XuDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA.
Xiao XiaoDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA.
Rui GuoDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA.
Jun ChenDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, California, USA.ORCID https://orcid.org/0000-0002-3439-0495

Funding

Magnetoelastic Vascular GraftsR01HL175135 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Jun Chen · 2025 to 2026
$1.1M
A soft magnetoelastic microneedle patch for rapid skin cancer screeningR01CA287326 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Jun Chen · 2024 to 2026
$1.1M
American Heart Association-American Stroke Association 23IPA1054908American Heart Association-American Stroke Association 23SCEFIA1157587American Heart Association-American Stroke Association 23TPA1141360American Heart Association Innovative Project Award 23IPA1054908American Heart Association's Second Century Early Faculty Independence Award 23SCEFIA1157587American Heart Association Transformational 23TPA1141360Makoto Watanabe Excellence in Research Award at the UCLA Samueli School of Engineering, the Office of Naval Research Young Investigator Award N00014-24-1-2065National Science Foundation 2425858NCI NIH HHS R01 CA287326NHLBI NIH HHS R01 HL175135NIH HHS R01 CA287326NIH HHS R01 HL175135
6 · The paper itself

Abstract

Respiration is a critical physiological process that reflects the health status of the human body. Self-powered bioelectronic devices for respiration monitoring have shown great promise, driven by their advantages in miniaturization, cost-effectiveness, high sensitivity, and excellent reliability. This work examines the recent advances in artificial intelligence-driven, self-powered respiration monitoring sensors based on triboelectricity, piezoelectricity, and magnetoelasticity, with a focus on their sensing performance and signal transduction mechanisms. A comparative analysis of their performance characteristics and applicable scenarios is presented, together with a discussion of practical considerations including breathability, wearing comfort, and waterproof performance, and an overview of the relative performance and application suitability of the three technologies. Furthermore, this report envisions future directions including long-term multi-scenario data collection and big data-driven respiratory diseases diagnostics. We believe that the widespread implementation of artificial intelligence-driven, self-powered respiration monitoring sensors will play a pivotal role in reshaping healthcare and advancing intelligent interventions for respiratory diseases, ultimately promoting global health and well-being.

Indexed as

Artificial IntelligenceRespirationWearable Electronic DevicesHumansMonitoring, Physiologicartificial intelligencemagnetoelasticitypiezoelectricityrespiration monitoringself‐poweredtriboelectricity

Identifiers

PMID41486555
PMCPMC12904045

What Socratic 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 Socratic graph.