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 Experiment Video

Updated: May 16, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Automatic monocular system for human fall detection based on variations in silhouette area.

Behzad Mirmahboub1, Shadrokh Samavi, Nader Karimi

  • 1Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran. mirmahboub@yahoo.com

IEEE Transactions on Bio-Medical Engineering
|November 30, 2012
PubMed
Summary

This study introduces a novel, single-camera system for automatic fall detection in seniors. The method uses silhouette area variations, proving effective for independent living and patient monitoring.

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

BrightVAE: luminosity enhancement in underexposed endoscopic images.

International journal of computer assisted radiology and surgery·2026
Same author

Dual-Branch Multi-Task Regressor and Transformer Model for Endoscopic Image Classification.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Resolution Enhancement of Prostate 3D MRI and Ultrasound Using Implicit Neural Representations.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

LPD-Net: A Lightweight and Efficient Deep Learning Model for Accurate Colorectal Polyp Segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Using fMRI Time Series and Functional Connectivity for Autism Classification: Integrating Mamba and KAN in Domain-Adversarial Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Tri-Model Integration: Advancing Breast Cancer Immunohistochemical Image Generation through Multi-Method Fusion.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Area of Science:

  • Computer Vision
  • Biomedical Engineering
  • Gerontology

Background:

  • The aging population is increasing globally, with many seniors living alone.
  • Falls are a significant risk for older adults, often requiring urgent medical attention.
  • Automatic fall detection systems can enhance independent living for seniors and patients.

Purpose of the Study:

  • To develop a robust and simple fall detection system using a single camera.
  • To overcome the view-dependent limitations of existing vision-based fall detection methods.
  • To propose a view-invariant feature for accurate fall classification.

Main Methods:

  • Utilized variations in silhouette area extracted from single-camera video sequences.
  • Employed a straightforward background separation technique to obtain silhouettes.

More Related Videos

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Using Motion Capture Technology in the Instrumented Timed Up and Go Test to Detect the Risk of Falling in Aged Adults
05:26

Using Motion Capture Technology in the Instrumented Timed Up and Go Test to Detect the Risk of Falling in Aged Adults

Published on: October 25, 2024

Related Experiment Videos

Last Updated: May 16, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Using Motion Capture Technology in the Instrumented Timed Up and Go Test to Detect the Risk of Falling in Aged Adults
05:26

Using Motion Capture Technology in the Instrumented Timed Up and Go Test to Detect the Risk of Falling in Aged Adults

Published on: October 25, 2024

  • Fed the view-invariant silhouette area feature into a Support Vector Machine (SVM) classifier.

    • Main Results:

    • Demonstrated that the silhouette area variation is a view-invariant feature for fall detection.
    • Achieved promising results in classifying falls and normal activities using a public dataset.
    • The proposed single-camera approach simplifies system complexity compared to multi-camera setups.

    Conclusions:

    • The proposed method offers an effective and simplified approach to automatic fall detection.
    • Silhouette area variation is a reliable feature for fall detection, independent of camera viewpoint.
    • This technology has the potential to significantly improve safety and independence for the elderly population.