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

Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

411
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
411
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

557
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
557
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

685
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...
685
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

637
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
637

You might also read

Related Articles

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

Sort by
Same author

Eco-efficient extraction of daidzein and genistein from soybean meal: Integrating enzymatic hydrolysis and HIUS-PLE.

Food chemistry·2025
Same author

Bridging Domain Gaps in Computational Pathology: A Comparative Study of Adaptation Strategies.

Sensors (Basel, Switzerland)·2025
Same author

Nutritional and technological potential of umbu-caja and soursop co-product flours.

Food research international (Ottawa, Ont.)·2025
Same author

Electrospun adsorbent membrane of PLA containing chitosan for toxic metal ions removal from aqueous solution: Effect of chitosan incorporation.

International journal of biological macromolecules·2025
Same author

Author Correction: An interpretable machine learning system for colorectal cancer diagnosis from pathology slides.

NPJ precision oncology·2024
Same author

An interpretable machine learning system for colorectal cancer diagnosis from pathology slides.

NPJ precision oncology·2024

Related Experiment Video

Updated: Nov 30, 2025

Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.6K

Machine Learning Improvements to Human Motion Tracking with IMUs.

Pedro Manuel Santos Ribeiro1, Ana Clara Matos2, Pedro Henrique Santos2

  • 1Faculty of Engineering, University of Porto, Dr. Roberto Frias Street, 4200-465 Porto, Portugal.

Sensors (Basel, Switzerland)
|November 13, 2020
PubMed
Summary
This summary is machine-generated.

Machine learning models significantly improve human motion tracking using Inertial Measurement Units (IMUs). Advanced methods enhance zero-velocity detection and position estimation, reducing accumulated errors for more accurate body segment tracking.

Keywords:
IMUhuman motion trackingmachine learning

More Related Videos

FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis
10:02

FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis

Published on: December 24, 2014

12.1K
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

8.3K

Related Experiment Videos

Last Updated: Nov 30, 2025

Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.6K
FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis
10:02

FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis

Published on: December 24, 2014

12.1K
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

8.3K

Area of Science:

  • Biomechanics
  • Wearable Technology
  • Machine Learning

Background:

  • Inertial Measurement Units (IMUs) are widely used for human motion tracking.
  • Accumulated errors in IMU data hinder accurate body segment position tracking over time.
  • Improved IMU data processing is crucial for enhanced motion analysis applications.

Purpose of the Study:

  • To develop and evaluate Machine Learning (ML) methods for improving IMU-based human motion tracking.
  • To enhance the accuracy of body segment position estimation by addressing error accumulation.
  • To compare ML-based approaches against traditional methods for zero-velocity detection and motion estimation.

Main Methods:

  • Utilized classifiers (Random Forest, SVM, LSTM) for robust zero-velocity detection, identifying periods of sensor inactivity.
  • Integrated ML regression models, particularly Long-Short-Term Memory (LSTM) networks, for estimating sensor displacement during movement.
  • Developed a unified LSTM model combining zero-velocity detection and motion estimation for simultaneous processing.

Main Results:

  • ML classifiers outperformed traditional detectors in identifying IMU stop periods across various motions and body segments.
  • LSTM-based regression models for displacement estimation showed no significant improvement over basic double integration with drift removal.
  • The combined LSTM model demonstrated superior performance, achieving lower average position tracking errors compared to sequential methods.

Conclusions:

  • Machine learning, especially LSTMs, offers a powerful approach to significantly improve IMU-based human motion tracking accuracy.
  • A unified ML model integrating detection and estimation provides a more effective solution for mitigating cumulative errors in IMU data.
  • These advancements pave the way for more reliable and precise applications of IMUs in biomechanics and motion analysis.