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

Methods of Classification and Identification01:28

Methods of Classification and Identification

2.3K
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
2.3K

You might also read

Related Articles

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

Sort by
Same author

ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration.

Sensors (Basel, Switzerland)·2024
Same author

Machine learning framework for precise localization of bleached corals using bag-of-hybrid visual feature classification.

Scientific reports·2023
Same author

Comprehensive Analysis of Compressible Perceptual Encryption Methods-Compression and Encryption Perspectives.

Sensors (Basel, Switzerland)·2023
Same author

Deep Reinforcement Learning-Based Coordinated Beamforming for mmWave Massive MIMO Vehicular Networks.

Sensors (Basel, Switzerland)·2023
Same author

MAC Protocols for mmWave Communication: A Comparative Survey.

Sensors (Basel, Switzerland)·2022
Same author

Measurement of Shear Strengths of Cu Films Using Precise Chip Forming.

Materials (Basel, Switzerland)·2022
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

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

Related Experiment Video

Updated: May 3, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

8.9K

WiFi-Based Human Identification with Machine Learning: A Comprehensive Survey.

Manal Mosharaf1, Jae B Kwak2, Wooyeol Choi1

  • 1Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea.

Sensors (Basel, Switzerland)
|October 16, 2024
PubMed
Summary
This summary is machine-generated.

Researchers are using WiFi signals to identify people, overcoming challenges like poor lighting. Machine learning models analyze signal fluctuations for accurate human identification, paving the way for future wireless sensing technologies.

Keywords:
WiFideep learninghuman identificationhuman sensingmachine learning

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K

Related Experiment Videos

Last Updated: May 3, 2026

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

8.9K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.4K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

2.6K

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Human identification technologies face limitations in challenging environments such as poor lighting, occlusion, and non-line-of-sight conditions.
  • Radio Frequency (RF) wireless signals, specifically Wireless Fidelity (WiFi), offer a promising alternative for robust human identification.
  • Machine learning (ML) models can analyze subtle WiFi signal fluctuations induced by human presence.

Purpose of the Study:

  • To provide a comprehensive survey of recent advancements in WiFi-based human identification.
  • To review machine learning models, system architectures, and methodologies employed in WiFi sensing for identification.
  • To discuss system evaluations, limitations, and future trends in wireless signal-based human identification.

Main Methods:

  • Analysis of WiFi signal fluctuations caused by human presence.
  • Development and application of machine learning algorithms for human identification.
  • Comprehensive literature review of existing WiFi-based human identification systems and techniques.

Main Results:

  • Machine learning models significantly enhance identification accuracy using WiFi signals.
  • WiFi-based human identification effectively addresses limitations of traditional methods in adverse conditions.
  • Recent research demonstrates practical implementations and system evaluations of this technology.

Conclusions:

  • WiFi-based human identification presents an innovative and effective solution for various applications.
  • Further research is needed to overcome existing limitations and explore future potential.
  • Wireless signals offer a transformative approach to human identification, moving beyond traditional sensing modalities.