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The inner ear assumes dual functionalities of auditory perception and equilibrium maintenance. The vestibule is the organ responsible for balance. This organ contains mechanoreceptors, specifically hair cells, endowed with stereocilia, which aid in deciphering information regarding the position and motion of our heads. Two intrinsic components, the utricle and saccule, help perceive head position, while the semicircular canals track head movement. Neurological messages initiated in the...
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Estimating Vestibular Perceptual Thresholds Using a Six-Degree-Of-Freedom Motion Platform
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Objective quantification of motion-induced dizziness using a proof-of-concept multimodal wearable platform.

Nhan Cao1, Brian Loyd2, Andy Kittelson2

  • 1Department of Computer Science, University of Montana, Missoula, MT, 59812, USA.

Scientific Reports
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

A new head-worn system, EquilibriSense, objectively quantifies motion-induced dizziness using physiological sensors and AI. This technology shows high accuracy in classifying dizziness severity and enabling early detection, improving upon subjective assessments.

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Wearable Technology

Background:

  • Vestibular dysfunction is a frequent cause of dizziness and medical consultations.
  • Current dizziness assessment methods are subjective, lacking real-time data, precision, and preventative capabilities.

Purpose of the Study:

  • To introduce EquilibriSense, a novel head-worn system for objective quantification of motion-induced dizziness.
  • To model dizziness progression and classify symptom severity using an AI-driven pipeline.

Main Methods:

  • Development of a bio-inspired, head-worn system integrating multiple physiological sensors.
  • Utilizing a controlled head-rotation paradigm for dizziness induction.
  • Employing an AI-driven pipeline and neurocomputational framework for data analysis and classification.

Main Results:

  • EquilibriSense achieved 86.8% accuracy in classifying multi-level motion-induced dizziness.
  • Early detection of dizziness onset demonstrated an Area Under the Curve (AUC) of 0.99 with over 98% accuracy.
  • High precision and recall values supported the system's performance in a pilot study with healthy participants.

Conclusions:

  • Multimodal physiological sensing is feasible for characterizing motion-induced dizziness.
  • EquilibriSense serves as a proof-of-concept for objective dizziness quantification.
  • The system provides a foundation for future validation in clinical and real-world settings.