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

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

442
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
442

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Mobgap: A State-of-the-Art Python Framework for Reproducible Estimation and Algorithm Validation of Digital Mobility Outcomes from a Single Wearable Device.

Sensors (Basel, Switzerland)·2026
Same author

Reliability and minimal detectable change of knee mechanics during gait and squatting, using markerless motion capture in the workplace.

Gait & posture·2026
Same author

Classifying mental stress from eye tracking data: deep learning approaches for out-of-the-lab conditions.

Scientific reports·2026
Same author

Exploring Attitudes Toward AI-Based Contactless Sensors in Health Among Five Stakeholder Groups: Qualitative Study.

Journal of medical Internet research·2026
Same author

Contactless Sleep Staging With Radar: A Transfer Learning Approach.

IEEE open journal of engineering in medicine and biology·2026
Same author

Acceptance, Perceived Usefulness, and Data Sharing in Mobile Health Apps Among Patients With Breast Cancer: Cross-Sectional Survey Study.

JMIR cancer·2026

Related Experiment Video

Updated: May 25, 2025

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

10.2K

High-Knee-Flexion Posture Recognition Using Multi-Dimensional Dynamic Time Warping on Inertial Sensor Data.

Annemarie F Laudanski1, Arne Küderle2, Felix Kluge2

  • 1Biomechanics of Human Mobility Laboratory, Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

Sensors (Basel, Switzerland)
|February 26, 2025
PubMed
Summary

This study developed a sensor framework using multi-dimensional Dynamic Time Warping (mDTW) to detect occupational high-flexion postures from inertial measurement unit (IMU) data, showing robust performance in real-world settings.

Keywords:
accelerometerdynamic time warpinggyroscopehigh-knee flexioninertial sensorsknee osteoarthritisoccupational ergonomicsposture classification

More Related Videos

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

7.8K
In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
07:43

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

2.9K

Related Experiment Videos

Last Updated: May 25, 2025

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

10.2K
An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

7.8K
In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
07:43

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

2.9K

Area of Science:

  • Biomechanics
  • Wearable Technology
  • Machine Learning

Background:

  • Continuous inertial data collection requires advanced algorithms to interpret human movements, considering variations in speed and duration.
  • Occupational settings frequently involve high-flexion postures, necessitating accurate detection and measurement methods.

Purpose of the Study:

  • To create a sensor-based framework for identifying and quantifying high-flexion postures common in occupational environments.
  • To utilize inertial measurement unit (IMU) data for posture analysis.

Main Methods:

  • Joint angle estimations (ankle, knee, hip) from IMU data were normalized for time and scale.
  • A multi-dimensional Dynamic Time Warping (mDTW) algorithm was employed for posture classification.
  • A dataset from 50 participants was used for model development and validation.

Main Results:

  • The mDTW model achieved 82.3% accuracy on the testing set and 55.6% on the validation set, improving to 86% and 74.6% after imbalance adjustment.
  • Highest misclassifications were observed between squatting variations and stooping.
  • The model demonstrated robustness in identifying postures from participants not involved in its development.

Conclusions:

  • The developed mDTW model shows significant potential for accurately measuring postural adoption in occupational settings.
  • This sensor-based framework offers a viable solution for quantitative postural analysis in real-world applications.