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

Machines01:19

Machines

578
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
578
Hand hygiene01:23

Hand hygiene

6.0K
Asepsis is the practice of preventing or breaking the chain of infection. The nurse employs aseptic techniques to prevent the spread of microorganisms and reduce the risk of diseases. Hand hygiene is the cornerstone of aseptic techniques and is classified into medical and surgical asepsis. Medical asepsis includes hand hygiene and the use of gloves. Surgical asepsis, or the sterile technique, refers to practices that render and keep objects and areas free of microorganisms.
Hand washing...
6.0K
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

582
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
582
Machines: Problem Solving II01:30

Machines: Problem Solving II

668
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
668
Machines: Problem Solving I01:22

Machines: Problem Solving I

714
A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
714
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

2.0K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
2.0K

You might also read

Related Articles

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

Sort by
Same author

One size does not fit all in evaluating model selection scores for image classification.

Scientific reports·2024
Same author

Volume Determination Challenges in Waste Sorting Facilities: Observations and Strategies.

Sensors (Basel, Switzerland)·2024
See all related articles

Related Experiment Video

Updated: Jan 31, 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

1.2K

Hand Gesture Recognition in Automotive Human⁻Machine Interaction Using Depth Cameras.

Nico Zengeler1, Thomas Kopinski2, Uwe Handmann3

  • 1Hochschule Ruhr West, University of Applied Sciences, 46236 Bottrop, Germany. nico.zengeler@hs-ruhrwest.de.

Sensors (Basel, Switzerland)
|December 28, 2018
PubMed
Summary

Machine learning, specifically Convolutional Neural Networks and Long Short-Term Memory, excels at hand gesture recognition using depth data from time-of-flight sensors. A new dataset, REHAP, offers over a million 3D hand posture samples for research.

Keywords:
automotive human–machine interactionhand gesture recognitionneural networkstime-of-flight sensors

More Related Videos

Author Spotlight: Deciphering the Cognitive and Neural Mechanisms of Gesture in Communication
07:18

Author Spotlight: Deciphering the Cognitive and Neural Mechanisms of Gesture in Communication

Published on: January 26, 2024

1.3K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K

Related Experiment Videos

Last Updated: Jan 31, 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

1.2K
Author Spotlight: Deciphering the Cognitive and Neural Mechanisms of Gesture in Communication
07:18

Author Spotlight: Deciphering the Cognitive and Neural Mechanisms of Gesture in Communication

Published on: January 26, 2024

1.3K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K

Area of Science:

  • Computer Science
  • Robotics
  • Human-Computer Interaction

Background:

  • Hand gesture recognition is crucial for intuitive human-computer interaction.
  • Depth data from time-of-flight (ToF) sensors offers rich information for gesture analysis.
  • Evaluating machine learning models requires comprehensive and diverse datasets.

Purpose of the Study:

  • To review current machine learning approaches for hand gesture recognition using ToF sensor data.
  • To present research findings from the Computational Neuroscience laboratory at Ruhr West University of Applied Sciences.
  • To introduce the REHAP dataset as a novel benchmark for 3D hand posture analysis.

Main Methods:

  • Review of machine learning techniques, focusing on deep learning models like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM).
  • Investigation of sensor data fusion techniques within a deep learning framework.
  • Conducting user studies to evaluate the practical performance of the developed gesture recognition system.

Main Results:

  • Convolutional Neural Networks and Long Short-Term Memory models demonstrate the most reliable results for hand gesture recognition.
  • Sensor data fusion techniques integrated into deep learning frameworks show promise for improved accuracy.
  • User studies confirm the practical viability and effectiveness of the evaluated system.

Conclusions:

  • Deep learning models, particularly CNNs and LSTMs, are highly effective for hand gesture recognition with ToF depth data.
  • The REHAP dataset provides a valuable resource with over a million 3D hand posture samples for advancing research in this field.
  • Future research should leverage advanced deep learning architectures and comprehensive datasets for robust gesture recognition systems.