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

Heterogeneous iron oxide nanoparticles anchored on carbon nanotubes for high-performance lithium-ion storage and fenton-like oxidation.

Journal of colloid and interface science·2021
Same author

Cost-effectiveness analysis of the integrated control strategy for schistosomiasis japonica in a lake region of China: a case study.

Infectious diseases of poverty·2021
Same author

Targeting Gα<sub>13</sub>-integrin interaction ameliorates systemic inflammation.

Nature communications·2021
Same author

Screening and mitigating major threats of regional development to water ecosystems using ecosystem services as endpoints.

Journal of environmental management·2021
Same author

Facile synthesis of a rod-like porous carbon framework confined magnetite nanoparticle composite for superior lithium-ion storage.

Journal of colloid and interface science·2021
Same author

Wafer-Scale and Full-Coverage Two-Dimensional Molecular Monolayers Strained by Solvent Surface Tension Balance.

ACS applied materials & interfaces·2021

Related Experiment Video

Updated: Mar 14, 2026

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

10.8K

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors.

Du-Xin Liu1,2,3, Xinyu Wu4,5,6, Wenbin Du7,8,9

  • 1Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. dx.liu@siat.ac.cn.

Sensors (Basel, Switzerland)
|October 1, 2016
PubMed
Summary

This study introduces a new method for recognizing gait phases in lower-limb exoskeletons using only joint angle sensors. This approach simplifies the exoskeleton

Keywords:
gait phase classificationgait phase recognitionlower-limb exoskeleton

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

11.4K
Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS
05:25

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Published on: June 7, 2024

1.8K

Related Experiment Videos

Last Updated: Mar 14, 2026

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

10.8K
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

11.4K
Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS
05:25

Author Spotlight: Assessing Brain Activity in Robotic-Assisted Lower Limb Rehabilitation Using fNIRS

Published on: June 7, 2024

1.8K

Area of Science:

  • Robotics
  • Biomechanics
  • Machine Learning

Background:

  • Gait phase recognition is crucial for controlling lower-limb exoskeletons.
  • Existing methods often rely on multiple sensor types, increasing complexity.
  • Joint angular sensors are essential for closed-loop control and readily available.

Purpose of the Study:

  • To develop a novel gait phase recognition method for lower-limb exoskeletons.
  • To utilize only existing joint angular sensors, simplifying the system.
  • To improve the accuracy and efficiency of gait phase identification.

Main Methods:

  • Calculating and classifying gait deviation distances using Fisher's linear discriminant method.
  • Dividing a gait cycle into eight distinct gait phases.
  • Developing and training a multilayer perceptron model with phase-labeled gait data.

Main Results:

  • The Fisher's linear discriminant method effectively classified gait deviation distances.
  • The multilayer perceptron model achieved a 94.45% average correct rate of set (CRS).
  • The model demonstrated an 87.22% average correct rate of phase (CRP) on the testing set, indicating accurate gait phase prediction.

Conclusions:

  • The proposed method accurately recognizes gait phases using only joint angular sensors.
  • This approach eliminates the need for additional sensors, simplifying exoskeleton systems.
  • The findings contribute to more efficient and accessible lower-limb exoskeleton control.