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

Gyroscope01:02

Gyroscope

4.5K
A gyroscope is defined as a spinning disk in which the axis of rotation is free to assume any orientation. When spinning, the orientation of the spin axis is unaffected by the orientation of the body that encloses it. The body or vehicle enclosing the gyroscope can be moved from place to place, while the orientation of the spin axis remains the same. This makes gyroscopes very useful in navigation, especially where magnetic compasses cannot be used, such as in crewed and crewless spacecraft,...
4.5K

You might also read

Related Articles

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

Sort by
Same author

Wind imaging using simultaneous fringe sampling with field-widened Michelson interferometers.

Applied optics·2022
Same author

Do We Need Psychiatric Hospitals?

Mental health (London)·2017
Same author

Boarding-Out Elderly Psychiatric Patients.

Mental health (London)·2017
Same author

Psychiatrist in the Old People's Ward.

Mental health (London)·2017

Related Experiment Video

Updated: Mar 21, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

1.4K

Quaternion-Based Gesture Recognition Using Wireless Wearable Motion Capture Sensors.

Shamir Alavi1, Dennis Arsenault2, Anthony Whitehead3

  • 1Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada. shamiralavi@cmail.carleton.ca.

Sensors (Basel, Switzerland)
|May 3, 2016
PubMed
Summary

This study developed a multi-sensor system for human motion capture and gesture recognition, achieving high accuracy for small gestures. Interactivity is possible, but user-specific training is needed for broader application.

Keywords:
artificial neural networksgesture recognitionmachine learningpattern analysisquaternionssupport vector machineswearable sensors

More Related Videos

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography
04:06

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

Published on: January 12, 2024

1.1K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.6K

Related Experiment Videos

Last Updated: Mar 21, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

1.4K
Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography
04:06

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

Published on: January 12, 2024

1.1K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

5.6K

Area of Science:

  • Human-Computer Interaction
  • Biomedical Engineering
  • Machine Learning

Background:

  • Human motion capture is crucial for understanding movement.
  • Gesture recognition systems require robust and accurate data.
  • Current systems face challenges in generalizability and user independence.

Purpose of the Study:

  • To develop and implement a unified multi-sensor system for human motion capture.
  • To classify six distinct gestures using wireless motion sensors.
  • To compare machine learning algorithms for gesture recognition accuracy and speed.

Main Methods:

  • Utilized five wireless inertial measurement units (IMUs) on participants' arms and upper body.
  • Collected motion capture data from eleven participants.
  • Compared Support Vector Machines (SVM) and Artificial Neural Networks (ANN) for classification.

Main Results:

  • Achieved near-perfect classification accuracies for small gestures.
  • Demonstrated classification speeds sufficient for interactive applications.
  • Observed reduced accuracy when participants were not involved in system training.

Conclusions:

  • The developed system shows high potential for accurate and interactive gesture recognition.
  • User-independent gesture recognition remains a challenge requiring further research.
  • Future work should focus on improving generalizability for diverse user populations.