Head Movement Analysis
Head Movement Analysis - Related Videos
10:14
Tactile Conditioning And Movement Analysis Of Antennal Sampling Strategies In Honey Bees (Apis mellifera L.)
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Cited by 9 •
2012
In this protocol we show how to condition harnessed honey bees to tactile stimuli and introduce a 2D motion capture technique for analyzing the kinematics of fine-scale antennal sampling pattern.
08:06
Eye Movement Monitoring of Memory
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Cited by 10 •
2010
Eye movement monitoring (or eye tracking) reveals where in space the eyes linger, when and for how long. Here, we demonstrate how eye tracking can be used to investigate the integrity of memory in multiple participant populations, without requiring verbal, or otherwise explicit, reports.
12:49
Purification of Transcripts and Metabolites from Drosophila Heads
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Cited by 12 •
2013
We describe here the procedures for the extraction and purification of mRNA and metabolites from Drosophila heads. We are applying these techniques to better understand the cellular perturbations underlying neuronal degeneration. These methodologies can be easily scaled and adapted for other "omic" projects.
07:51
Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study
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Cited by 21 •
2017
This manuscript describes the method to test the concurrent validity of kinematic measures recorded by the smartphone application in comparison to a 3D motion capture system in the sagittal plane. This protocol will enable clinicians to set up smartphones for video capture of human movement.
07:04
Physiological, Morphological and Neurochemical Characterization of Neurons Modulated by Movement
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2011
A technique is described to quantify the in vivo physiological response of mammalian neurons during movement and correlate the physiology of the neuron with neuronal morphology, neurochemical phenotype and synaptic microcircuitry.
FAQs
These systems provide different ways to quantify head position, orientation, velocity, or acceleration during a defined task. Motion-capture cameras track movement through recorded spatial data, inertial measurement units measure movement-related changes, and video-based tracking extracts motion from images. The resulting measurements allow clinicians or researchers to examine movement patterns rather than relying only on visual judgment.
Position and orientation describe where the head is and how it is aligned, whereas velocity and acceleration describe how quickly movement occurs and how rapidly that movement changes. Including these time-dependent measures can reveal differences in motor control that may not be apparent from position alone. Comparing these variables across tasks supports a more detailed functional assessment.
Patient reports describe perceived symptoms, while physical examination evaluates function through clinical observation and testing. Head movement measurements add objective data about performance during defined tasks, helping relate reported dizziness, coordination difficulty, or other concerns to measurable movement patterns. This combination can strengthen functional assessment without treating the recorded measurements as a replacement for clinical evaluation.
A typical workflow selects a defined task or clinical test, positions the participant for measurement, and records head movement with motion-capture cameras, an inertial measurement unit, or video-based tracking. The recorded position, orientation, velocity, or acceleration is then examined across the task. Comparing the resulting pattern with clinical findings or other task measurements supports interpretation.
Clinical applications include evaluation of dizziness, vestibular disorders, concussion, gait impairment, and other conditions that affect coordination. The method can characterize how head movement changes during relevant clinical tests or functional tasks, providing information that may complement examination findings and patient descriptions. Its value is greatest when movement data are interpreted within the broader clinical context.
Repeated measurements can show whether movement patterns change over time during recovery, allowing clinicians or researchers to track functional performance objectively. In research, the same approach helps characterize disease-related changes by comparing head movement during defined tasks. These outcomes can add quantitative detail to clinical observations and help describe differences in balance, motor control, or neurological function.