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

You might also read

Related Articles

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

Sort by
Same author

Polyzwitterionic hydrogel electrolytes based on imidazolium cations and sulfonate anions enable stable Zn-I<sub>2</sub> batteries by regulating Zn deposition and inhibiting polyiodide shuttle.

Chemical science·2026
Same author

A Programmed Drug-Loaded and Penetration-Delivery Functionalized Microneedle Patch for Synergistic Obesity Treatment.

Advanced healthcare materials·2026
Same author

Surgical Clipping of a Trilobed Anterior Communicating Artery Aneurysm: 2-Dimensional Operative Video.

World neurosurgery·2026
Same author

Association Between Domestic Water Hardness and Chronic Kidney Disease: A Prospective Cohort Study From the UK Biobank.

Mayo Clinic proceedings·2026
Same author

Flow-mediated pseudolesion mimicking an intracranial aneurysm secondary to cervical internal carotid artery dissection: illustrative case.

Journal of neurosurgery. Case lessons·2026
Same author

Self-assembling scaffolds epigenetically reactivate and electroactively guide neuronal regeneration to restore central neural circuits.

Nature communications·2026

Related Experiment Video

Updated: Jan 17, 2026

Automated Gait Analysis to Assess Functional Recovery in Rodents with Peripheral Nerve or Spinal Cord Contusion Injury
06:31

Automated Gait Analysis to Assess Functional Recovery in Rodents with Peripheral Nerve or Spinal Cord Contusion Injury

Published on: October 6, 2020

6.6K

Deep Learning-based Gait Recognition and Evaluation of the Wounded.

Chuanchuan Liu1, Ling-Hu Cai1, Yi-Fei Shen2

  • 1Department of Emergency Medicine, https://ror.org/05w21nn13The First Affiliated Hospital (Southwest Hospital) of Army Medical University, Chongqing, P.R. China.

Disaster Medicine and Public Health Preparedness
|September 24, 2025
PubMed
Summary

Artificial intelligence (AI) using gait analysis can rapidly assess traumatic injuries remotely. This technology aids disaster response by identifying limping, improving triage and early intervention when access is limited.

Keywords:
Gait recognitionYOLOv5assessment of the injuriesdeep learning

More Related Videos

3D Kinematic Gait Analysis for Preclinical Studies in Rodents
10:19

3D Kinematic Gait Analysis for Preclinical Studies in Rodents

Published on: August 3, 2019

11.3K
Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits
06:25

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Published on: August 12, 2019

9.0K

Related Experiment Videos

Last Updated: Jan 17, 2026

Automated Gait Analysis to Assess Functional Recovery in Rodents with Peripheral Nerve or Spinal Cord Contusion Injury
06:31

Automated Gait Analysis to Assess Functional Recovery in Rodents with Peripheral Nerve or Spinal Cord Contusion Injury

Published on: October 6, 2020

6.6K
3D Kinematic Gait Analysis for Preclinical Studies in Rodents
10:19

3D Kinematic Gait Analysis for Preclinical Studies in Rodents

Published on: August 3, 2019

11.3K
Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits
06:25

Paw-Print Analysis of Contrast-Enhanced Recordings PrAnCER: A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Published on: August 12, 2019

9.0K

Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Disaster Medicine

Background:

  • Remote injury assessment during disasters is challenging due to site inaccessibility.
  • Traditional methods struggle with timely triage in inaccessible disaster zones.

Purpose of the Study:

  • To explore the feasibility of artificial intelligence (AI) for rapid traumatic injury assessment using gait analysis.
  • To develop an AI model for remote gait analysis in disaster scenarios.

Main Methods:

  • Utilized a dataset of 4500 gait images across humans, dogs, and rabbits.
  • Trained a deep learning object detection model (YOLOv5) to classify normal vs. limping gaits.
  • Evaluated model performance through statistical validation and case studies.

Main Results:

  • The YOLOv5 model achieved high accuracy in distinguishing normal and limping gaits across species.
  • Quantitative metrics confirmed the model's reliability for gait pattern recognition.
  • Qualitative analysis demonstrated potential for remote, rapid traumatic injury assessment.

Conclusions:

  • AI, specifically deep convolutional neural networks like YOLOv5, shows promise for fast, remote injury assessment during disaster response.
  • This AI approach can assist healthcare professionals in identifying injury risks when physical access is limited.
  • Improved triage efficiency and early intervention are potential benefits for disaster victims.