Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Microbial Biosensors01:17

Microbial Biosensors

Microbial biosensors are analytical devices that utilize living microbes to detect specific substances through measurable signals. These devices consist of two main components: biosensing organisms and signal-transducing elements. Biosensing organisms, such as Escherichia coli or Saccharomyces cerevisiae, are typically housed in multiwell plates connected to transducers, enabling rapid, real-time detection of target analytes.Signal Generation MechanismWhen a target analyte—such as...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Machine Learning-Enabled Layer-Wise Melting Quality Recognition for Laser Powder Bed Fusion Process via In Situ Monitoring.

Materials (Basel, Switzerland)·2026
Same author

Corrigendum to "Identification and functional characterization of SmABCG24 regulating tanshinone transport in Salvia miltiorrhiza" [Int. J. Biol. Macromol. 360 (2026) 151837].

International journal of biological macromolecules·2026
Same author

Identification and functional characterization of SmABCG24 regulating tanshinone transport in Salvia miltiorrhiza.

International journal of biological macromolecules·2026
Same author

Mendelian randomization identifies the characteristic plasma metabolite profile of meningioma.

Archives of medical science : AMS·2026
Same author

Enhancing Metabolic Engineering in Medicinal Plants Through Prime Editing.

Plant biotechnology journal·2026
Same author

Artificial Intelligence Informed Hydrogel Biomaterials in Additive Manufacturing.

Gels (Basel, Switzerland)·2025
Same journal

Investigating Nonlinear Fatigue Damage Evolution of SBS-Modified Asphalt Mixtures with Physical Gel Structure.

Gels (Basel, Switzerland)·2026
Same journal

Nano-Iron (III) Oxide-Doped Poly (Itaconic Acid-Co-Acrylamide)/Sodium Alginate Hydrogel for Saline-Alkali Soil Amelioration and Wheat Growth.

Gels (Basel, Switzerland)·2026
Same journal

Evaluation of Starch-Derived Hydrogel Systems for Artifact-Cleaning Applications.

Gels (Basel, Switzerland)·2026
Same journal

Bioorthogonally Cross-Linked Injectable PEG Hydrogel with Robust Hemostatic and Antibacterial Properties.

Gels (Basel, Switzerland)·2026
Same journal

Robust Polyurethane Hydrogels Based on Dynamic Disulfide Bonds and Pendant Tertiary Amines with Room-Temperature Self-Healing and pH Responsiveness.

Gels (Basel, Switzerland)·2026
Same journal

An Environmentally Tolerant 5A Hydrogel with Photothermal Effect for Frostbite Treatment.

Gels (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: May 28, 2026

Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
10:03

Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

Published on: July 22, 2022

Machine-Learning-Enabled Hydrogel Biosensors for Wearable Health Monitoring.

Zhizhou Zhang1,2

  • 1School of Engineering, The University of Manchester, Manchester M13 9PL, UK.

Gels (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) is advancing conductive hydrogel biosensors for health monitoring by improving signal analysis and material design. Overcoming challenges like stability and data scarcity is key for reliable wearable health technology.

Keywords:
biosensorshydrogelsmachine learningmaterial designmultimodal data fusionwearable biosensorswearable deviceswearable health monitoring

More Related Videos

Hollow Microneedle-based Sensor for Multiplexed Transdermal Electrochemical Sensing
08:19

Hollow Microneedle-based Sensor for Multiplexed Transdermal Electrochemical Sensing

Published on: June 1, 2012

Related Experiment Videos

Last Updated: May 28, 2026

Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
10:03

Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

Published on: July 22, 2022

Hollow Microneedle-based Sensor for Multiplexed Transdermal Electrochemical Sensing
08:19

Hollow Microneedle-based Sensor for Multiplexed Transdermal Electrochemical Sensing

Published on: June 1, 2012

Area of Science:

  • Biomedical Engineering
  • Materials Science
  • Data Science

Background:

  • Conductive hydrogel biosensors are crucial for wearable health monitoring.
  • Challenges include hydration stability, data scarcity, and device variability.
  • Machine learning (ML) offers solutions to these limitations.

Purpose of the Study:

  • To review recent advances in ML for conductive hydrogel biosensors.
  • To outline strategies for overcoming current bottlenecks.
  • To provide guidelines for standardization and clinical translation.

Main Methods:

  • Review of ML applications in electrochemical, mechanical, and optical transduction.
  • Exploration of polymer informatics and graph-based representations for material design.
  • Analysis of physics-informed models for signal interpretation.

Main Results:

  • ML improves feature extraction, drift compensation, and generalization in biosensor applications.
  • ML aids in predicting gel properties and guiding material selection.
  • Advanced analytics enhance the reliability of biosensor data.

Conclusions:

  • ML is transforming hydrogel biosensor design and deployment.
  • Standardized datasets and robust models are essential for generalization.
  • Actionable guidelines are provided for developing reliable hydrogel wearables for clinical use.