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

Force Classification01:22

Force Classification

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,...
IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
The...
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...

You might also read

Related Articles

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

Sort by
Same author

Machine Learning Approach for Application-Tailored Nanolubricants' Design.

Nanomaterials (Basel, Switzerland)·2022
See all related articles

Related Experiment Videos

Human Fall Detection with Infrared Imaging: A Comparison of Graph Convolutional Networks and YOLO.

Karol Perliński1, Artur Faltyński1, Aleksandra Świetlicka1

  • 1Institute of Automatic Control and Robotics, Poznan University of Technology, 61-131 Poznań, Poland.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

This study compares artificial intelligence methods for detecting human falls using infrared imaging. Graph convolutional networks (GCNs) and YOLO achieved high accuracy, paving the way for advanced elderly care monitoring systems.

Keywords:
YOLOfall detectiongraph convolutional networkshuman motion analysisinfrared imaging

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Biomedical Engineering

Background:

  • Elderly care and healthcare monitoring systems require accurate fall detection.
  • Infrared imaging offers a privacy-preserving method for continuous monitoring.
  • Existing AI methods need evaluation for real-time human fall analysis in infrared environments.

Purpose of the Study:

  • To comparatively analyze Graph Convolutional Networks (GCNs) and YOLO object detection algorithms for human fall event analysis.
  • To assess the performance of GCNs and YOLOv8 in detecting and classifying falls using infrared imaging.
  • To explore the potential of AI in developing practical healthcare monitoring solutions for fall detection.

Main Methods:

  • Developed a GCN model utilizing 2D and 3D skeletal data represented as graph structures.
  • Evaluated the real-time detection capabilities of YOLOv8 on infrared video frames.
  • Tested AI models for accuracy in detecting and classifying fall directions under infrared conditions.

Main Results:

  • The GCN model achieved over 99% classification accuracy for human falls.
  • YOLOv8 demonstrated real-time fall detection capabilities on infrared video.
  • Both AI approaches showed promise in analyzing fall events in infrared imaging.

Conclusions:

  • GCNs and YOLOv8 are effective AI tools for human fall detection in infrared imaging.
  • A hybrid framework combining YOLO's spatial localization and GCN's motion analysis is proposed for enhanced future applications.
  • These AI advancements can significantly improve elderly care and healthcare monitoring systems.