Evidence map›Paper›PMID 42345892›Full record

ReviewBiosensors2026

Advances in Wearable Biosensors for Non-Invasive Biofluid Monitoring.

Rajib Mondal, Manob Jyoti Saikia

Abstract readReview
In one paragraph

Review in Biosensors, 2026. 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. Review
  2. Review
  3. 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

2 authors.

Rajib MondalElectrical and Computer Engineering Department, University of Memphis, Memphis, TN 38152, USA.ORCID 0009-0000-9262-9954
Manob Jyoti SaikiaElectrical and Computer Engineering Department, University of Memphis, Memphis, TN 38152, USA.ORCID 0000-0001-6656-4333

Funding

U.S. National Science Foundation 2514612
6 · The paper itself

Abstract

Chronic diseases such as cardiovascular disorders, diabetes, neurological conditions, and kidney disease continue to rise worldwide. These conditions create a growing demand for continuous, non-invasive, and personalized health monitoring technologies. Wearable biosensors meet this need by enabling real-time physiological and biochemical measurements outside traditional clinical settings. Among wearable biosensors, those based on biofluids like sweat, tears, and saliva provide a painless alternative to blood sampling. These fluids also grant access to metabolites, electrolytes, hormones, proteins, and disease related biomarkers that reflect systemic health status. Advanced sensing technology allow us to continuously track health status by analyzing key biomarkers in these accessible biofluids. This review summarizes recent advances in non-invasive wearable biosensors and focuses on their sensing principles which includes biorecognition elements, signal transduction mechanisms, and data acquisition strategies. We also discussed key sensing modalities, including electrochemical, optical, thermal, and piezoelectric approaches, highlighting their advantages for wearable integration and performance in biofluid sensing. Finally the review also outlines recent developments and applications of these systems in biofluid sensing. In the end we highlights existing challenges, potential solutions, and future directions toward clinically deployable, AI-assisted precision healthcare systems.

Indexed as

Biosensing TechniquesBody FluidsWearable Electronic DevicesBiomarkersDigital HealthHumansMonitoring, PhysiologicBiomarkersbiofluid analysiscontinuous monitoringflexible electronicshuman digital twinsnon-invasive sensingpoint-of-care diagnosticsprecision healthcarewearable biosensorswireless health monitoring

Identifiers

PMID42345892
PMCPMC13296539

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.