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Related Concept Videos

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

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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.
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Related Experiment Video

Updated: May 31, 2025

Assessment of Static Graviceptive Perception in the Roll-Plane using the Subjective Visual Vertical Paradigm
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Active headrest combined with a depth camera-based ear-positioning system.

Yuteng Liu1, Haowen Li1, Haishan Zou1

  • 1Key Laboratory of Modern Acoustic, Nanjing University, Nanjing 210093, China.

The Journal of the Acoustical Society of America
|January 24, 2025
PubMed
Summary
This summary is machine-generated.

Active headrests (AHR) offer noise reduction, but performance degrades with head movement. An ear-positioning (EP) system enhances AHR, maintaining significant noise reduction during head translation and rotation.

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Area of Science:

  • Acoustics
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Active headrests (AHR) provide localized noise reduction (NR) but are sensitive to head position.
  • Existing head tracking systems primarily address translational head movements, limiting robustness against combined motions.
  • Maintaining effective NR requires precise alignment between the AHR quiet zone and the listener's ear.

Purpose of the Study:

  • To develop and evaluate an ear-positioning (EP) system integrated with AHR for robust noise reduction.
  • To enhance the performance of AHR against both translational and rotational head movements.
  • To improve the overall effectiveness of AHR in real-world scenarios with dynamic head motion.

Main Methods:

  • An ear-positioning (EP) system was developed using a depth camera and a human pose estimation model.
  • The EP system was integrated with active headrests (AHR).
  • A post-processing algorithm was implemented to handle occluded ear scenarios.

Main Results:

  • The EP system demonstrated effective tracking of ear movements during translation and rotation.
  • AHR integrated with the EP system achieved substantial noise reduction (up to 11.7/12.2 dBA for translation, 11.4/13.6 dBA for rotation).
  • Performance improvements were significant compared to AHR without the EP system (e.g., -5.7/-6.9 dBA without system).

Conclusions:

  • The proposed ear-positioning system significantly enhances the robustness and performance of active headrests.
  • The integration of EP with AHR provides effective noise reduction across a range of head movements, including translation and rotation.
  • This technology offers a promising solution for maintaining personalized quiet zones in dynamic environments.