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

Observational Learning01:12

Observational Learning

973
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
973
Data Collection by Observations01:08

Data Collection by Observations

15.0K
Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
15.0K
Methods of Classification and Identification01:28

Methods of Classification and Identification

1.2K
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...
1.2K
Naturalistic Observations02:30

Naturalistic Observations

17.2K
If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
17.2K
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

557
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
557
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

274
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
274

You might also read

Related Articles

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

Sort by
Same author

Position: Topological Deep Learning is the New Frontier for Relational Learning.

Proceedings of machine learning research·2025
Same author

Post Take-Over Performance Varies in Drivers of Automated and Connected Vehicle Technology in Near-Miss Scenarios.

Human factors·2023
Same author

TDA-Net: Fusion of Persistent Homology and Deep Learning Features for COVID-19 Detection From Chest X-Ray Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2021
Same author

TWIST REGIONS AND COEFFICIENTS STABILITY OF THE COLORED JONES POLYNOMIAL.

Transactions of the American mathematical society·2021
Same author

Investigating the safety and operational benefits of mixed traffic environments with different automated vehicle market penetration rates in the proximity of a driveway on an urban arterial.

Accident; analysis and prevention·2021
Same author

Graph based analysis for gene segment organization In a scrambled genome.

Journal of theoretical biology·2020

Related Experiment Video

Updated: Feb 1, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K

A hierarchical machine learning classification approach for secondary task identification from observed driving

Osama A Osman1, Mustafa Hajij2, Sogand Karbalaieali1

  • 1Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, United States.

Accident; Analysis and Prevention
|December 17, 2018
PubMed
Summary
This summary is machine-generated.

This study uses machine learning to detect and identify secondary tasks, like texting, from driving behavior. This can help prevent crashes by identifying dangerous distractions.

Keywords:
Accident investigationsDetectionDistracted drivingDriving behaviorEnsemble treeIdentificationIn-vehicle systemsMachine learningSecondary tasks

More Related Videos

Driving Under the Influence: How Music Listening Affects Driving Behaviors
07:25

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Published on: March 27, 2019

13.1K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.0K

Related Experiment Videos

Last Updated: Feb 1, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

4.5K
Driving Under the Influence: How Music Listening Affects Driving Behaviors
07:25

Driving Under the Influence: How Music Listening Affects Driving Behaviors

Published on: March 27, 2019

13.1K
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
08:58

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

Published on: November 19, 2018

13.0K

Area of Science:

  • Engineering
  • Computer Science
  • Transportation Safety

Background:

  • Distracted driving causes thousands of fatalities and injuries annually.
  • Previous research focused on detecting secondary task engagement, but not specific task types.
  • Identifying specific secondary tasks is crucial for targeted safety interventions.

Purpose of the Study:

  • To propose a machine learning methodology for detecting and classifying secondary tasks drivers engage in.
  • To differentiate between specific secondary tasks such as cellphone use and passenger interaction.
  • To evaluate the effectiveness of various ensemble tree classification methods for this purpose.

Main Methods:

  • A bi-level hierarchical classification approach was developed.
  • Five driving behavior parameters (speed, acceleration, yaw rate, etc.) and their standard deviations were used as input.
  • Nine ensemble tree classification methods were compared for performance.

Main Results:

  • Secondary task engagement detection accuracy ranged from 66% to 99.8% (Decision Tree).
  • Specific secondary task identification accuracy ranged from 55% to 79%, with Random Forest achieving 82.2%.
  • The proposed methodology shows promise in identifying risky driving behaviors.

Conclusions:

  • The developed machine learning model can effectively detect and classify secondary tasks from driving behavior.
  • This technology can serve as a countermeasure to prevent crashes by identifying unlawful secondary tasks.
  • Alerting drivers to risky behavior changes can improve overall road safety.