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

You might also read

Related Articles

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

Sort by
Same author

Mendelian randomization analysis of 1,400 genetically predicted blood metabolites and ovarian cancer risk.

Archives of medical science : AMS·2026
Same author

Experiment and Modelling of Ultrasonic Vibration-Assisted Creep-Aging Tensile for 7055-T6 Alloy.

Materials (Basel, Switzerland)·2026
Same author

Stabilizing-sensing synergistic geogrid for high-speed railways.

Nature communications·2026
Same author

Carbon Nanotube-Aramid Fiber Enabled Dielectric Dispersion in Paper Composites for Ultrabroadband Lightweight Absorbers.

Nano letters·2026
Same author

An RSM-Based Investigation on the Process-Performance Correlation and Microstructural Evolution of Friction Stir Welded 7055 Al/2195 Al-Li Dissimilar T-Joints.

Materials (Basel, Switzerland)·2026
Same author

Tunable flexible capacitive sensor for dynamic pressure monitoring.

Microsystems & nanoengineering·2026

Related Experiment Video

Updated: Jun 25, 2025

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

5.2K

AI-Driven Sensing Technology: Review.

Long Chen1, Chenbin Xia1, Zhehui Zhao1

  • 1Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China.

Sensors (Basel, Switzerland)
|May 25, 2024
PubMed
Summary

Machine learning and deep learning enhance sensor technologies, improving accuracy and enabling new applications in engineering and biomedical fields. This integration drives innovation in sensor design, performance, and predictive capabilities.

Keywords:
AI-driven sensing applicationsML/DL algorithmperformance enhancementsensing technology

More Related Videos

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

23.0K
Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

418

Related Experiment Videos

Last Updated: Jun 25, 2025

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization
06:00

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

5.2K
Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches
07:23

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

23.0K
Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

418

Area of Science:

  • Sensor Technology
  • Artificial Intelligence
  • Machine Learning
  • Deep Learning

Background:

  • Machine learning (ML) and deep learning (DL) are revolutionizing sensing technologies.
  • These advancements significantly improve sensor accuracy, sensitivity, and adaptability.
  • The integration of artificial intelligence (AI) with sensors is a key driver of progress.

Purpose of the Study:

  • To review the fusion of ML/DL algorithms with sensor technologies.
  • To highlight the impact of AI on sensor design, calibration, and performance.
  • To showcase novel applications and future trends in AI-driven sensing.

Main Methods:

  • Review of current literature on ML/DL in sensor technology.
  • Analysis of AI algorithm integration for sensor enhancement.
  • Examination of exemplary applications across various fields.

Main Results:

  • AI algorithms significantly upgrade sensor functionalities and performance.
  • ML/DL enable advancements in sensor calibration, compensation, and object recognition.
  • New applications are emerging in industrial automation, robotics, and biomedical engineering.

Conclusions:

  • The synergy between AI and sensors offers transformative potential.
  • Addressing current challenges will unlock further advancements in sensing capabilities.
  • Future trends point towards more intelligent and adaptive sensor systems.