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

Minimum number of inertial measurement units needed to identify significant variations in walk patterns of overweight individuals walking on irregular surfaces.

Scientific reports·2023
Same author

Dichalcogenide and Metal Oxide Semiconductor-Based Composite to Support Plasmonic Catalysis.

ACS omega·2023
Same author

Estimation of SPIO Nanoparticles Uptakes by Macrophages Using Transmission Electron Microscopy.

International journal of molecular sciences·2022
Same author

Walking Speed Classification from Marker-Free Video Images in Two-Dimension Using Optimum Data and a Deep Learning Method.

Bioengineering (Basel, Switzerland)·2022
Same author

Evaluating the difference in walk patterns among normal-weight and overweight/obese individuals in real-world surfaces using statistical analysis and deep learning methods with inertial measurement unit data.

Physical and engineering sciences in medicine·2022
Same author

MXene-GaN van der Waals metal-semiconductor junctions for high performance multiple quantum well photodetectors.

Light, science & applications·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Nov 7, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.2K

Using a Deep Learning Method and Data from Two-Dimensional (2D) Marker-Less Video-Based Images for Walking Speed

Tasriva Sikandar1, Mohammad F Rabbi2, Kamarul H Ghazali1

  • 1Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan 26600, Malaysia.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces ratio-based body measurements from 2D images to classify walking speeds. This novel approach accurately assesses gait in various settings, aiding health assessments in clinical and aged care environments.

Keywords:
2D imageLSTMdeep learninggait impairmenthuman mobilitymarker-less videoquasi-periodic patternrehabilitationwalking speed classificationwalking speed pattern

More Related Videos

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
06:54

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

14.4K
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

11.0K

Related Experiment Videos

Last Updated: Nov 7, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.2K
Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
06:54

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

14.4K
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

11.0K

Area of Science:

  • Biomechanics
  • Computer Vision
  • Machine Learning

Background:

  • Human body measurement data from walking provides insights into functional movement and health.
  • Two-dimensional (2D) image sequences offer a simple method for estimating body measurements for gait analysis.
  • Current 2D methods rely on body markers and are sensitive to camera distance variations.

Purpose of the Study:

  • To propose novel ratio-based body measurements from 2D images for gait analysis.
  • To develop a deep learning model for classifying walking speeds (slow, normal, fast).
  • To assess the robustness of the proposed method in indoor and outdoor environments.

Main Methods:

  • Extraction of five ratio-based body measurements from marker-less 2D walking images.
  • Utilizing a deep learning-based bidirectional long short-term memory (LSTM) classification model.
  • Validation of the method's performance in both indoor and outdoor settings.

Main Results:

  • Achieved average classification accuracies of 88.08% (indoor) and 79.18% (outdoor).
  • Demonstrated that ratio-based measurements are independent of body-worn garments.
  • Showed robustness against changes in the distance between the individual and the camera.

Conclusions:

  • The proposed ratio-based body measurements offer a reliable method for gait analysis using 2D images.
  • The deep learning model effectively classifies walking speeds, showing high accuracy.
  • This technique has significant potential for practical applications in healthcare settings like clinics and aged care homes.