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

Artificial platelets suppressing deep vein thrombosis via competitive adhesion.

Bioactive materials·2026
Same author

Roles of metabolic dysregulation in osteoarthritis.

Frontiers in cell and developmental biology·2026
Same author

Underwater image enhancement via multi-angle polarization decomposition.

Optics express·2026
Same author

EIF2α-ATF4-CHAC1 Signalling Links ER Stress to Ferroptosis in Human Aortic Smooth Muscle Cells: Mechanistic Insights and Therapeutic Implications.

Journal of cellular and molecular medicine·2026
Same author

Analysis of latent classes and influencing factors of intrinsic capacity among elderly patients receiving maintenance hemodialysis.

Geriatric nursing (New York, N.Y.)·2026
Same author

Prevalence, perinatal risk factors and clinical outcomes of respiratory <i>Ureaplasma</i> species colonization in hospitalized preterm infants.

Frontiers in pediatrics·2026

Related Experiment Video

Updated: Feb 28, 2026

Automated Gait Analysis in Mice with Chronic Constriction Injury
06:49

Automated Gait Analysis in Mice with Chronic Constriction Injury

Published on: October 17, 2017

10.9K

Edge AI-Based Gait-Phase Detection for Closed-Loop Neuromodulation in SCI Mice.

Ahnsei Shon1,2,3, Justin T Vernam1,2,3, Xiaolong Du2,3

  • 1Department of Neurological Surgery, School of Medicine, University of Louisville, Louisville, KY 40202, USA.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel AI system for real-time gait phase detection in spinal cord injury (SCI) mice. The technology enables precise, phase-locked stimulation for improved locomotion recovery in closed-loop neuromodulation.

Keywords:
closed-loop neuromodulationedge AIgait analysismachine learningreal-time systemspinal cord injurytreadmill locomotionvision AI

More Related Videos

Sagittal Plane Kinematic Gait Analysis in C57BL/6 Mice Subjected to MOG35-55 Induced Experimental Autoimmune Encephalomyelitis
13:02

Sagittal Plane Kinematic Gait Analysis in C57BL/6 Mice Subjected to MOG35-55 Induced Experimental Autoimmune Encephalomyelitis

Published on: November 4, 2017

9.3K
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

6.7K

Related Experiment Videos

Last Updated: Feb 28, 2026

Automated Gait Analysis in Mice with Chronic Constriction Injury
06:49

Automated Gait Analysis in Mice with Chronic Constriction Injury

Published on: October 17, 2017

10.9K
Sagittal Plane Kinematic Gait Analysis in C57BL/6 Mice Subjected to MOG35-55 Induced Experimental Autoimmune Encephalomyelitis
13:02

Sagittal Plane Kinematic Gait Analysis in C57BL/6 Mice Subjected to MOG35-55 Induced Experimental Autoimmune Encephalomyelitis

Published on: November 4, 2017

9.3K
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

6.7K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Real-time gait phase detection is crucial for closed-loop neuromodulation in spinal cord injury (SCI) research.
  • Existing methods often require offline processing or are too computationally intensive for embedded systems.

Purpose of the Study:

  • To develop a hybrid AI sensing architecture for real-time kinematic extraction and gait-phase classification.
  • To enable low-latency, on-device gait-phase classification for closed-loop neuromodulation in SCI mice.

Main Methods:

  • A vision AI module was used for marker-assisted, high-speed pose estimation to extract hindlimb joint angles.
  • A lightweight edge AI model on a microcontroller classified gait phase and generated stimulation triggers.
  • The system was evaluated using treadmill locomotion in SCI mice.

Main Results:

  • The hybrid AI system achieved real-time kinematic extraction and gait-phase classification.
  • The system generalized to new SCI gait patterns without retraining.
  • Precise phase-locked biphasic stimulation was demonstrated in a closed-loop evaluation.

Conclusions:

  • This work presents a low-latency, attachment-free framework for gait-responsive neuromodulation.
  • The developed system supports the future translation of wearable or implantable closed-loop neurorehabilitation systems.
  • The AI-based approach advances real-time control for SCI locomotion recovery.