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

Rat robot autonomous border detection based on wearable sensors.

Bioinspiration & biomimetics·2025
Same author

Locomotion Joint Angle and Moment Estimation With Soft Wearable Sensors for Personalized Exosuit Control.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2025
Same author

Synergy in motion: Exploring the similarity and variability of muscle synergy patterns in healthy individuals.

Human movement science·2024
Same author

Current developments of robotic hip exoskeleton toward sensing, decision, and actuation: A review.

Wearable technologies·2024
Same author

Battery-free temperature logger for deep-sea hydrothermal fluids based on heat pipe heat exchangers and thermoelectric generators.

The Review of scientific instruments·2023
Same author

Reducing the muscle activity of walking using a portable hip exoskeleton based on human-in-the-loop optimization.

Frontiers in bioengineering and biotechnology·2023

Related Experiment Video

Updated: Nov 1, 2025

Low-Cost Gait Analysis for Behavioral Phenotyping of Mouse Models of Neuromuscular Disease
05:53

Low-Cost Gait Analysis for Behavioral Phenotyping of Mouse Models of Neuromuscular Disease

Published on: July 18, 2019

17.1K

Machine-learning-based children's pathological gait classification with low-cost gait-recognition system.

Linghui Xu1,2, Jiansong Chen3, Fei Wang4

  • 1Ningbo Research Institute, Zhejiang University, Ningbo, 315100, China.

Biomedical Engineering Online
|June 23, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a low-cost system for recognizing pathological gaits in children using plantar pressure data. The developed intelligent gait recognition method (IGRM) achieves high accuracy, enabling early detection and intervention for conditions like scoliosis.

Keywords:
Feature extractionGait classificationPathological gait recognitionPressure-sensor array

More Related Videos

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

8.8K
Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
08:56

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

14.1K

Related Experiment Videos

Last Updated: Nov 1, 2025

Low-Cost Gait Analysis for Behavioral Phenotyping of Mouse Models of Neuromuscular Disease
05:53

Low-Cost Gait Analysis for Behavioral Phenotyping of Mouse Models of Neuromuscular Disease

Published on: July 18, 2019

17.1K
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

8.8K
Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
08:56

Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults

Published on: November 7, 2014

14.1K

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Pediatric Health

Background:

  • Pathological gaits in children can lead to severe musculoskeletal conditions such as osteoarthritis and scoliosis.
  • Early detection and therapeutic intervention are crucial for mitigating long-term health consequences.
  • Existing automated, low-cost gait recognition systems for children are limited.

Purpose of the Study:

  • To design and validate a cost-effective pathological gait recognition system (PGRS) for children.
  • To develop an intelligent gait recognition method (IGRM) utilizing only plantar pressure information.
  • To achieve high accuracy and real-time performance in identifying pathological gaits in pediatric populations.

Main Methods:

  • A PGRS was developed using an 8x8 pressure-sensor array.
  • An IGRM based on machine learning and plantar pressure data was implemented for static and dynamic gait analysis.
  • Experiments involved 17 children recognizing normal, toe-in, toe-out, and flat gaits, with performance evaluated using cross-validation, recall, precision, and time cost.

Main Results:

  • The IGRM demonstrated practical applicability with high average accuracy in both static and dynamic sections.
  • Intra-subject recognition accuracy reached 92.41% (static) and 97.79% (dynamic).
  • Inter-subject recognition accuracy was 85.78% (static) and 78.81% (dynamic), with static accuracy slightly lower due to less natural postures.

Conclusions:

  • A low-cost PGRS is feasible, offering high precision and real-time gait recognition capabilities for children.
  • The system shows potential for computer-aided supervision of both normal and pathological gaits using plantar pressure patterns.
  • This technology can aid in gait abnormality rectification through timely feedback and intervention.