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

1.1K
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...
1.1K
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

845
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
845

You might also read

Related Articles

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

Sort by
Same author

Engineering Ultrathin Bismuth Nanosheets With Active Facet for Highly Efficient CO<sub>2</sub> Electroreduction to Formate.

ChemSusChem·2026
Same author

Depressive symptom trajectories around incident hip fracture in older adults: an event-centered matched analysis from the Health and Retirement Study.

Maturitas·2026
Same author

Cognitive trajectories before and after geriatric hip fracture: a matched longitudinal analysis of the Health and Retirement Study, 1996-2016.

BMC geriatrics·2026
Same author

Molecular mechanisms of KMT2C alterations in gastrointestinal cancers: enhancer network destabilization, lineage plasticity, and clinical translation.

Frontiers in immunology·2026
Same author

Discovery of novel 20S-protopanaxadiol derivatives in alleviating sepsis-associated liver injury by modulating p65/p50 activity.

European journal of medicinal chemistry·2026
Same author

A retrospective analysis of risk factors for intestinal necrosis in pediatric secondary intussusception.

BMC pediatrics·2026

Related Experiment Video

Updated: Apr 8, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.6K

Head Gesture Recognition Combining Activity Detection and Dynamic Time Warping.

Huaizhou Li1, Haiyan Hu2

  • 1College of Building Environmental Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China.

Journal of Imaging
|May 24, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel head movement recognition system using inertial measurement unit (IMU) sensors and dynamic time warping (DTW). The method achieved 100% accuracy in classifying six head gestures, offering an efficient human-computer interface solution.

Keywords:
activity detectiondynamic time warpinghead gesturehuman–computer interfaceinertial measurement unit

More Related Videos

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

2.7K
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

447

Related Experiment Videos

Last Updated: Apr 8, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

1.6K
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

2.7K
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

447

Area of Science:

  • Human-Computer Interaction
  • Sensor Technology
  • Signal Processing

Background:

  • Head movement recognition is crucial for advanced human-computer interfaces.
  • Inertial Measurement Unit (IMU) sensors offer advantages over image processing for this task due to lower complexity, faster processing, and reduced cost.
  • Existing methods may have limitations in accuracy or efficiency.

Purpose of the Study:

  • To propose and evaluate a new approach for head movement recognition using IMU sensors.
  • To combine activity detection with Dynamic Time Warping (DTW) for improved gesture classification.
  • To demonstrate the effectiveness of the proposed method in a real-world application.

Main Methods:

  • Collected head movement data using an IMU sensor attached to eyeglasses.
  • Implemented an activity detection algorithm to distinguish between movements and noise in time-series data.
  • Utilized Dynamic Time Warping (DTW) to calculate distances between action time series and templates for classification.

Main Results:

  • The proposed method achieved 100% accuracy in classifying six distinct types of head movements.
  • The combined approach of activity detection and DTW proved effective for robust head gesture recognition.
  • The system demonstrated high performance in distinguishing between intended gestures and background noise.

Conclusions:

  • The developed IMU-based system offers a highly accurate and efficient solution for head gesture recognition.
  • This method presents a viable and improved alternative for human-computer interface applications.
  • The findings highlight the potential of sensor fusion and advanced signal processing for intuitive interaction.