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

A Self-Powered Dressing Based on a Zn-Mo Galvanic Cell for Accelerated Wound Repair.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Concurrent control of natural and robotic limbs through a tactile-encoded brain-computer interface.

Nature communications·2026
Same author

Correction to "Conductive Microneedle Patch with Electricity-Triggered Drug Release Performance for Atopic Dermatitis Treatment".

ACS applied materials & interfaces·2026
Same author

PEDOT: PSS for Implantable and Wearable Bioelectronics: From Material Engineering and Energy Storage to Clinical Translation.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Restoration of endogenous electric fields with a glucose-powered symbiotic bioabsorbable bandage for diabetic wound healing.

Science advances·2026
Same author

A Wearable Thermoelectric Respiratory Sensing System for Quantitative Pulmonary Function Monitoring.

ACS nano·2026
Same journal

A Droplet-Microarray Platform for Multiplex Profiling of Breast Cancer Exosome Subtypes in Patients' Blood Plasma Samples.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same journal

Material-Dependent Functionalization of CVD-Grown TMDC Monolayers Probed by Vibrational Nanospectroscopy.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same journal

BandGap Modulated Charge Gating of Semiconductor Coatings Stabilizes Zinc Metal Anodes.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same journal

For High Capacity: Upcycling of Spent Graphite Catalytic via Precisely Tailoring Water-Gas Reaction.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same journal

Electronic Engineering of Donor-Acceptor Covalent Organic Frameworks via Fluorine Substitution for Efficient Solar Hydrogen Production.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same journal

Correction to: "A Gold Nanocage/Cluster Hybrid Structure for Whole-Body Multispectral Optoacoustic Tomography Imaging, EGFR Inhibitor Delivery, and Photothermal Therapy".

Small (Weinheim an der Bergstrasse, Germany)·2026
See all related articles

Related Experiment Video

Updated: Jan 17, 2026

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

Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

Published on: July 22, 2022

4.9K

AI-Enhanced Wearable Technology for Human Physiological Signal Detection: Challenges and Future Directions.

Jiahao Wan1, Shunyuan Xu2, Jinzhi Lin1

  • 1School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.

Small (Weinheim an Der Bergstrasse, Germany)
|September 24, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances wearable devices for physiological monitoring by improving data analysis accuracy and enabling personalized health insights. This technology offers advanced solutions for complex, real-time health data, overcoming traditional limitations.

Keywords:
artificial intelligence (AI)health monitoringmultimodal data fusionphysiological signalswearable devices

More Related Videos

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings
10:45

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

Published on: January 22, 2018

8.0K
A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
04:24

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

12.3K

Related Experiment Videos

Last Updated: Jan 17, 2026

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

Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

Published on: July 22, 2022

4.9K
A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings
10:45

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

Published on: January 22, 2018

8.0K
A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
04:24

A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program

Published on: April 19, 2019

12.3K

Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Materials Science

Background:

  • Wearable devices are increasingly vital for monitoring physiological signals due to technological advances and an aging population.
  • Traditional signal analysis methods struggle with complex, multimodal, nonlinear, and personalized physiological data.
  • Artificial intelligence (AI) offers novel solutions for analyzing physiological signals from wearable devices.

Purpose of the Study:

  • To review recent advancements in AI for wearable physiological signal monitoring.
  • To explore AI's benefits in signal extraction, classification, personalized health, and human-computer interaction.
  • To analyze AI applications across various physiological signal types and discuss future trends.

Main Methods:

  • Systematic review of AI technologies (deep learning, machine learning, multimodal fusion) in wearable physiological monitoring.
  • Analysis of AI's impact on signal accuracy, personalization, and real-time processing.
  • Examination of AI applications in bioelectric, mechanical, chemical, and thermal signal analysis.

Main Results:

  • AI significantly improves the accuracy and real-time performance of physiological signal processing.
  • AI facilitates personalized health monitoring, disease prediction, and optimized human-computer interaction.
  • AI demonstrates effective applications in analyzing diverse physiological signals from wearable sensors.

Conclusions:

  • AI is revolutionizing wearable physiological monitoring, enhancing data analysis and enabling personalized healthcare.
  • Challenges remain in data privacy, algorithm generalization, real-time processing, and model interpretability.
  • Future development hinges on advancements in AI algorithms, materials, chips, and interdisciplinary collaboration.