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

Classification of Systems-I01:26

Classification of Systems-I

270
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
270
Classification of Systems-II01:31

Classification of Systems-II

214
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
214
Force Classification01:22

Force Classification

1.4K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.4K
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

3.1K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
3.1K
Methods of Classification and Identification01:28

Methods of Classification and Identification

123
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...
123
Classification of Signals01:30

Classification of Signals

705
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
705

You might also read

Related Articles

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

Sort by
Same author

Adaptive conformity promotes cooperation within structured populations.

Chaos (Woodbury, N.Y.)·2026
Same author

E-contact facilitated by conversational agents reduces interethnic prejudice and anxiety in Afghanistan.

Communications psychology·2024
Same author

Channel Selection in Uncoordinated IEEE 802.11 Networks Using Graph Coloring.

Sensors (Basel, Switzerland)·2023
Same author

Access Control Mechanism for IoT Environments Based on Modelling Communication Procedures as Resources.

Sensors (Basel, Switzerland)·2018
Same author

Optimized Sensor Network and Multi-Agent Decision Support for Smart Traffic Light Management.

Sensors (Basel, Switzerland)·2018
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: Aug 23, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.6K

Fuzzy Ontology-Based System for Driver Behavior Classification.

Susel Fernandez1, Takayuki Ito2, Luis Cruz-Piris1

  • 1Universidad de Alcalá, Departamento de Automática, Escuela Politécnica Superior, Campus Universitario, Ctra. Madrid-Barcelona, Km. 33,600, 28805 Madrid, Spain.

Sensors (Basel, Switzerland)
|October 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a Fuzzy Rule-Based System to classify driver behavior for enhanced road safety within intelligent transportation systems. The system effectively models driving styles, improving traffic flow and reducing accidents.

Keywords:
classificationdriver behaviorfuzzy rule-based systemknowledgesensor networks

More Related Videos

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

4.0K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K

Related Experiment Videos

Last Updated: Aug 23, 2025

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.6K
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

4.0K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K

Area of Science:

  • Intelligent Transportation Systems
  • Road Safety Engineering
  • Artificial Intelligence in Transportation

Background:

  • Human error is a primary cause of road accidents globally.
  • Modeling driver behavior is crucial for improving road safety.
  • Intelligent Transportation Systems (ITS) leverage technology to enhance traffic efficiency and accident prevention.

Purpose of the Study:

  • To develop and evaluate a Fuzzy Rule-Based System (FRBS) for classifying driver behavior profiles.
  • To integrate the driver classification system into an ITS architecture for real-time driving assistance.
  • To assess the system's effectiveness in improving road safety and traffic management.

Main Methods:

  • Development of a knowledge base comprising an ontology and driving rules for the FRBS.
  • Ontology modeling of driver behavior entities and their relationships within the traffic environment.
  • Integration of the FRBS into an ITS architecture to provide personalized driving recommendations.

Main Results:

  • The ontology effectively models traffic scenarios with an F1 score of 0.9.
  • The driver classification system achieved an F1 score of 0.84, outperforming Random Forest and Naive Bayes.
  • Recommended alternative routes led to an average time gain of 66.4% for drivers.

Conclusions:

  • The proposed FRBS is effective for classifying driver behavior and enhancing road safety.
  • The system's integration into ITS architecture provides valuable driving assistance.
  • The results demonstrate significant potential for reducing negative transportation events like crashes and jams.