Evidence map›Paper›PMID 41104294›Full record

ArticleIEEE sensors letters2025

A Wearable Sweat Rate Sensor with Adaptive Sweat Ion Concentration Calibration.

Mohammad Shafiqul Islam, Sangwon Cha, Wenxin Cai, Munia Ferdoushi, Yasser Khan

Abstract read
In one paragraph

Article in IEEE sensors letters, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Liquid Metal-Enabled Soft Bioelectronics for Human Health Monitoring.Small (Weinheim an der Bergstrasse, Germany) · 2026
    Review
  3. Review
  4. 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

5 authors.

Mohammad Shafiqul IslamDepartment of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA.
Sangwon ChaDepartment of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA.
Wenxin CaiThe Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Munia FerdoushiDepartment of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA.
Yasser KhanDepartment of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089, USA.

Funding

Novel multimodal neural, physiological, and behavioral sensing and machine learning for mental statesR61MH135407 · NIMH · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Maryam Shanechi · 2024 to 2026
$2.8M
NIMH NIH HHS R61 MH135407
6 · The paper itself

Abstract

Wearable sensors for continuous physiological biomarker monitoring offer a non-invasive and personalized approach to healthcare. Sweat, as a readily accessible biofluid, serves as a valuable medium for biomarker detection. However, accurate biomarker analysis relies on precise sweat rate measurements, as sweat concentration and secretion rates are interdependent. To address this, it is critical to calibrate sweat ion concentration to enhance the accuracy of sweat rate monitoring. Moreover, the distribution of sweat glands varies across different body regions, necessitating application-specific design modifications for wearable sweat rate sensors. Effective sensor implementation requires adaptability in design while ensuring compatibility with scalable fabrication techniques. In this work, we present a digital fabrication method that allows for easy customization and large-area manufacturability. A key advancement in our approach is the integration of secondary capacitive sensing to continuously monitor sweat ion concentration, which is incorporated into real-time sweat rate calculations. This calibration technique enhances measurement precision by accounting for fluctuations in sweat composition. Additionally, we developed customized readout electronics and a mobile application for real-time sweat rate visualization, enhancing user accessibility and convenience. By incorporating sweat ion concentration calibration through capacitive sensing, our sensor system significantly improves the accuracy and reliability of sweat rate measurements, advancing real-time perspiration monitoring for personalized health applications.

Indexed as

Capacitive Sweat Rate SensorDigital DesignInkjet PrintingSweat ConcentrationSweat RateSweat Sensing

Identifiers

PMID41104294
PMCPMC12524163

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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.