Virtual Marker Tracking
Virtual Marker Tracking - Related Videos
06:36
Three-Dimensional Mapping of the Rotation of Interactive Virtual Objects with Eye-Tracking Data
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2024
We have developed a simple, customizable, and efficient method for recording quantitative processual data from interactive spatial tasks and mapping these rotation data with eye-tracking data.
08:12
Two-photon Calcium Imaging in Mice Navigating a Virtual Reality Environment
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Cited by 98 •
2014
Here we describe the experimental procedures involved in two-photon imaging of mouse cortex during behavior in a virtual reality environment.
10:38
Human Fear Conditioning Conducted in Full Immersion 3-Dimensional Virtual Reality
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Cited by 25 •
2010
Classical fear conditioning paradigm was adapted for human participants in a fully immersive virtual reality setting. Using a discrimination paradigm, conditioned fear, cue and context memory retention, and extinction was measured with skin conductance response to dynamic virtual snakes and spiders (the conditioned stimuli) in two distinct virtual contexts.
05:12
Using Virtual Reality to Transfer Motor Skill Knowledge from One Hand to Another
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Cited by 11 •
2017
We describe a novel virtual-reality based setup which exploits voluntary control of one hand to improve motor-skill performance in the other (non-trained) hand. This is achieved by providing real-time movement-based sensory feedback as if the non-trained hand is moving. This new approach may be used to enhance rehabilitation of patients with unilateral hemiparesis.
01:20
Virtual Work
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2023
The principle of virtual work states that if a body is in static and dynamic equilibrium, then the sum of all the virtual work done by all external forces and couple moments for any given virtual displacement must be zero. In static equilibrium, a body can experience an imaginary or virtual movement, such as displacement or rotation. The virtual work done by a force is equal to the dot product of force and virtual displacement in the direction of the force. When it comes to virtually rotating a...
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Across successive frames, the system identifies selected visual features or body landmarks, assigns each a virtual coordinate, and links corresponding points through time. This temporal association converts separate image observations into continuous movement trajectories. Those trajectories allow researchers to quantify changes in location rather than relying only on qualitative inspection of individual images.
Virtual coordinates represent the changing positions of selected body points, allowing researchers to reconstruct trajectories over time. These trajectories can quantify locomotion, posture, orientation, and interactions with the environment. Comparing coordinate patterns across frames provides a consistent way to analyze how movement unfolds, while distinguishing a body point’s location from the broader behavior it contributes to.
Unlike approaches that require physical markers attached to the subject, Virtual Marker Tracking uses visual information from images or video. Removing attached markers reduces interference from tracking equipment, which can matter when researchers examine natural locomotion, posture, or orientation. This makes the resulting movement measurements less dependent on equipment physically placed on the animal or person.
Visual features and body landmarks serve as the selected reference points for analysis. The system recognizes these points in successive frames, estimates their positions, and assigns virtual coordinates that can be linked over time. Choosing behaviorally meaningful points allows the reconstructed trajectories to describe specific aspects of movement, such as body orientation, posture, or locomotion.
A typical analysis begins with image or video data and selected body points or visual features. The system examines successive frames, estimates the locations of those points, assigns virtual coordinates, and links the coordinates over time. Researchers then reconstruct movement trajectories from the linked positions and use them to measure locomotion, posture, orientation, or environmental interactions.
Automated and repeatable analysis is useful when researchers need quantitative measurements from animal or human movement recordings. Instead of treating each image as an isolated observation, they can derive trajectories across the recording and examine movement with greater temporal resolution and consistency. The approach supports behavioral studies focused on locomotion, posture, orientation, or interactions with the environment.