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

Glutamate Ionotropic Kainate Receptors as Therapeutic Targets in Enzalutamide-Resistant and Neuroendocrine Prostate Cancer.

International journal of molecular sciences·2026
Same author

Lower limb motion intention recognition using multi-source able-bodied gait signals.

Journal of neural engineering·2026
Same author

Quantitative analysis of evoked haptic sensations by transcutaneous electrical nerve stimulation for electrode array size optimization.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Bridging data gaps to support the Global Plastics Treaty.

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

A BIN2-SOG1 molecular module regulates replication stress response independently on ATR.

Science advances·2026
Same author

Chromosomal scale genome assembly of medicinal plant Sophora tonkinensis.

BMC genomics·2026

Related Experiment Video

Updated: Apr 23, 2026

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
06:17

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

92

SCGait a novel method for person identification applied to legged robots.

Penglin Qin1, Guanghua Xu2,3,4, Qingqiang Wu1

  • 1School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China.

Scientific Reports
|April 21, 2026
PubMed
Summary

This study introduces a novel gait recognition method (SCGait) for legged robots, achieving 82.2% accuracy. The system integrates with Yolo for effective person identification and tracking, demonstrating high performance in real-world scenarios.

Keywords:
Gait encodingGait recognitionGraph convolutional networkLegged robotLoss functionPerson following

More Related Videos

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.4K
Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

Published on: April 23, 2020

8.1K

Related Experiment Videos

Last Updated: Apr 23, 2026

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats
06:17

Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats

Published on: April 3, 2026

92
Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.4K
Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT

Published on: April 23, 2020

8.1K

Area of Science:

  • Robotics
  • Computer Vision
  • Biometrics

Background:

  • Legged robots commonly use speaker recognition or Ultra Wide Band (UWB) positioning for human identification and tracking.
  • These methods require active user cooperation, limiting their practical applications.

Purpose of the Study:

  • To develop a passive and accurate gait recognition method for legged robots.
  • To create an integrated person identification-tracking system for legged robots.

Main Methods:

  • Proposed a "Symmetry-encoding and pseudo-Centroid loss optimized Gait recognition method" (SCGait).
  • Combined SCGait with the Yolo object detection algorithm.
  • Evaluated the system on the CASIA-B dataset and custom test videos.

Main Results:

  • SCGait achieved a mean test accuracy of 82.2% on the CASIA-B dataset.
  • The integrated system demonstrated 91.8% identification accuracy and 36 FPS in multi-person scenes.
  • Ablation studies confirmed the generalization ability of the gait encoding and loss function.

Conclusions:

  • The SCGait method offers a robust solution for gait recognition in legged robots.
  • The integrated system provides effective person identification and tracking for applications like companion and industrial robots.